### Final Report

METEOROLOGICAL MODELING FOR THE SOUTHERN APPALACHIAN MOUNTAINS INITIATIVE (SAMI)

Prepared by:

Kevin Doty University of Alabama in Huntsville 320 Sparkman Drive Huntsville, AL 35805

Thomas W. Tesche and Dennis E. McNally Alpine Geo

physics, LLC

3479 Reeves Dr. Ft. Wright, KY 41017

Brian Timin U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Modeling Group, MD 14 Research Triangle Park, NC 27711

Stephen F. Mueller Tennessee Valley Aut hority Energy Research & Technology Applications

P.O. Box 1010 Muscle Shoals, AL 35662

- 1010

July 200 2

### 1

EXECUTIVE SUMMARY

1 INTRODUCTION

................................ ................................ ................................ ................................ ................................ . 10

1.1 O VERVIEW

................................ ................................ ................................ ................................ ................................ ....... 10

1.2 E

PISODE S ELECTIO N C RITERIA

................................ ................................ ................................ ................................ ...... 10

1.3 M ETEOROLOGICAL M ODEL E VALUATION

................................ ................................ ................................ ..................... 11
1.3.1 The Importance of Meteorological Model Performance Evaluation

................................ ........................... 11
1.3.2 Evaluation Objectives

................................ ................................ ................................ ................................ ....... 12

1.3.3 The Evaluation Process

................................ ................................ ................................ ................................ .... 13

1.3.4 Complementary Perspectives Brought to this Evaluation

................................ ................................ ......... 14

1.3.5 Structure of Report

................................ ................................ ................................ ................................ ............. 15

2.0 THE METEOROLOGICAL MOD EL

................................ ................................ ................................ ............................. 15

2.1 O VERVIEW OF RAMS

................................ ................................ ................................ ................................ .................... 15

2.2 RAMS H ORIZONTAL G RID S TRUCTURE

................................ ................................ ................................ ....................... 15

2.3 RAMS V

ERTICAL G

RID S TRUCTURE

................................ ................................ ................................ ............................ 17

2.4 RAMS M ODEL M ODIFICATIONS

................................ ................................ ................................ ................................ ... 19
2.4.1 Surface Grid Files

................................ ................................ ................................ ................................ .............. 19

2.4.2 Model Physics Changes

................................ ................................ ................................ ................................ .... 20

2.5 O THER D IFFERENCES WITH R ESPECT TO M ETEOROLOGICAL M ODELING P ROTOCOL

................................ .............. 30

2.6 RAMSIN S ELECTIONS

................................ ................................ ................................ ................................ ................... 30
3.0 OVERVIEW OF RAMS SIMULATION PROCESS

................................ ................................ ................................ ... 33

3.1 NCEP/NCAR R EANALYIS D ATA

................................ ................................ ................................ ................................ ... 33

3.2 P REPARATION OF RAMS I NPUT AND N UDGING F ILES

................................ ................................ ................................ 33
4.0 THE SAMI MODELING EPISODES

................................ ................................ ................................ ............................. 35

4.1 SAMI E

PISODE S ELECTION

................................ ................................ ................................ ................................ ........... 35
4.1.2 Important Meteorological and Air Quality Variables

................................ ................................ ............... 39

4.1.3 CART Performance

................................ ................................ ................................ ................................ ............ 40
4.1.4 Episode Selection Software

................................ ................................ ................................ ............................... 40
4.1.5 Episode Software “Theory”

................................ ................................ ................................ ............................. 41

4.1.6 Episode Software Rules

................................ ................................ ................................ ................................ .... 42

4.1.7 Episode Selection Error Terms

................................ ................................ ................................ ........................ 42
4.1.8 Biased (Unscaled) Error

................................ ................................ ................................ ................................ .... 43
4.1.9 Distance (Scaled) Error

................................ ................................ ................................ ................................ ..... 43
4.1.10 Reproducing the Annual and Seasonal Metrics

................................ ................................ ........................... 45

4.2 T HE 11

- 19 J ULY 1995 E PISODE

................................ ................................ ................................ ................................ ...... 50

4.3 T HE 9

- 18 M AY 1993 E PISODE

................................ ................................ ................................ ................................ ........ 56

4.4 T HE 21 M ARCH

- 1 A PRIL 1993 E PISODE

................................ ................................ ................................ ...................... 62

4.5 T HE 6

- 14 F EBRUARY 1994 E PISODE

................................ ................................ ................................ .............................. 68

4.6 T HE 21 J ULY

- 1 A UGUST 1991 E PISODE

................................ ................................ ................................ ....................... 74

4.7 T HE 3

- 12 A UGUST 1993 E PISODE

................................ ................................ ................................ ................................ .. 80

4.8 T HE 22

- 29 J UNE 1992 E PISODE

................................ ................................ ................................ ................................ ...... 81
4.9 T HE 24 A PRIL

– 3 M AY 1995 E PISODE

................................ ................................ ................................ ......................... 82

4.10 T HE 22

- 30 M AY 1995 E PISODE

................................ ................................ ................................ ................................ ...... 97
4.10 T HE 22

- 30 M AY 1995 E PISODE

................................ ................................ ................................ ................................ ...... 98
5.0 TECHNICAL APPROACH TO THE MODEL PERFORMANCE EVALUATION

................................ ............. 104
5.0 TECHNICAL APPROACH TO THE MODEL PERFORMANCE EVALUATION

................................ ............. 104

5.1 C OMPONENTS OF THE RAMS E VALUATION

................................ ................................ ................................ ............... 104

5.2 D ATA S UPPORTING M ODEL E VALUATION

................................ ................................ ................................ .................. 104

5.3 S TATISTICAL AND G RAPHICAL E

VALUATION T OOLS

................................ ................................ ................................ . 105
5.3.1 Mean and Global Statistics

................................ ................................ ................................ ........................... 105

5.3.2 Difference Statistics

................................ ................................ ................................ ................................ ....... 106

5.3.3 Skill Measures

................................ ................................ ................................ ................................ ................. 109

### 2

5.3.4 Graphical Tools

................................ ................................ ................................ ................................ ............... 110

6.0 EV

ALUATION OF SURFACE METEOROLOGICAL FIELDS

................................ ................................ ............. 113

6.1 11

- 19 J ULY 1995 E PISODE R

ESULTS

................................ ................................ ................................ ............................ 114
6.1.1 48

- km Results

................................ ................................ ................................ ................................ .................. 116
6.1.2 12

- km Results

................................ ................................ ................................ ................................ ................. 122

6.2 2 2

- 30 M AY 1995 E PISODE R

ESULTS

................................ ................................ ................................ ............................ 126
6.2.1 96

- km Results

................................ ................................ ................................ ................................ .................. 126
6.2.2 12

- km Results

................................ ................................ ................................ ................................ ................. 132

6.3 9

- 18 M AY 1993 E PISODE R

ESULTS

................................ ................................ ................................ .............................. 136
6.3.1 96

- km Results

................................ ................................ ................................ ................................ .................. 136
6.3.2 12

- km Results

................................ ................................ ................................ ................................ .................. 142

6.4 21 M ARCH TO 1 A PRIL 1993 E PISODE R

ESULTS

................................ ................................ ................................ ........ 145
6.4.1 96

- km Results

................................ ................................ ................................ ................................ .................. 145
6.4.2 12

- km Results

................................ ................................ ................................ ................................ .................. 152

6.5 6

- 14 F EBRUARY 1994 E PISODE R

ESULTS

................................ ................................ ................................ .................... 155
6.5.1 96

- km Results

................................ ................................ ................................ ................................ .................. 155
1.1.1 6.5.2 12

- km Results

................................ ................................ ................................ ................................ ........ 162
1.1.1 6.5.2 12

- km Results

................................ ................................ ................................ ................................ ........ 162

6.6 21 J ULY

- 1 A UGUST 1991 E PISODE R

ESULTS

................................ ................................ ................................ ............ 166
6.6.1 96

- km Results

................................ ................................ ................................ ................................ .................. 166
6.6.2 12

- km Results

................................ ................................ ................................ ................................ .................. 172

6.7 3

- 12 A UGUST 1993 E PISODE R

ESULTS

................................ ................................ ................................ ........................ 177
6 .7.1 Surface Temperatures

................................ ................................ ................................ ................................ ..... 177

6.7.2 Surface Wind Speed and Direction

................................ ................................ ................................ .............. 178
6.7.3 Mixing Ratios

................................ ................................ ................................ ................................ ................... 180
6.7.4 Precipitation

................................ ................................ ................................ ................................ ..................... 180

6.8 22

- 29 J UNE 199 2 E PISODE R

ESULTS

................................ ................................ ................................ ............................ 204
6.8.1 Surface Temperatures

................................ ................................ ................................ ................................ ..... 204

6.8.2 Surface Wind Speed and Direction

................................ ................................ ................................ .............. 205
6.8.3 Mixing Ratios

................................ ................................ ................................ ................................ .................. 207

6.8.4 Precipitatio n

................................ ................................ ................................ ................................ ..................... 208

6.9 24 A PRIL

– 3 M AY 1995 E PISODE R

ESULTS

................................ ................................ ................................ ............... 232
6.9.1 Surface Temperatures

................................ ................................ ................................ ................................ ..... 232

6.9.2 Surface Wind Speed and Direction

................................ ................................ ................................ .............. 233

6.9.3 Mixing Ratios

................................ ................................ ................................ ................................ .................. 234

6.9.4 Precipitation

................................ ................................ ................................ ................................ ..................... 236

7.0 EVALUATION OF ALOFT METEOROLOGICAL FIELDS

................................ ................................ .................. 259

7.1 11

- 19 J ULY 1995 E PISODE

................................ ................................ ................................ ................................ ............ 260

7.2 22

- 30 M AY 1995 E PI SODE

................................ ................................ ................................ ................................ ............ 264

7.3 9

- 18 M AY 1993 E PISODE

................................ ................................ ................................ ................................ .............. 268

7.4 21 M ARCH TO 1 A PRIL 1993 E PISODE R

ESULTS

................................ ................................ ................................ ........ 272

7.5 6

- 14 F EBRUARY 1994 E PISODE R

ESULTS

................................ ................................ ................................ .................... 276

7.6 21 J U LY

- 1 A UGUST 1991 E PISODE R

ESULTS

................................ ................................ ................................ ............ 280

7.7 3

- 12 A UGUST 1993 E PISODE R

ESULTS

................................ ................................ ................................ ........................ 284
7.7.1 Statistics of Aloft Predictions of Temperatures and Winds

................................ ................................ .... 284

7.7.2 Vertical Pro

files of Mixing Ratios, Temperatures and Winds

................................ .............................. 285

7.8 23

- 29 J UNE 1992 E PISODE R

ESULTS

................................ ................................ ................................ ............................ 285
7.8.1 Statistics of Aloft Predictions of Temperatures and Winds

................................ ................................ .... 285

7.8.2 V

ertical Profiles of Mixing Ratios, Temperatures and Winds

................................ .............................. 285

7.9 24 A PRIL

– 3 M AY 1995 E PISODE R

ESULTS

................................ ................................ ................................ ............... 286
7.9.1 Statistics of Aloft Predictions of Temperatures and Winds

................................ ................................ .... 286

7.9.2 Vertical Profiles of Mixing Ratios, Temperatures and Winds

................................ .............................. 286

### 3

8.0 RAMS PERFORMANCE ACROSS SAMI EPISODES

................................ ................................ .......................... 291

8.1 E

PISODE C OMPOSITE N EAR

- G ROUND L

EVEL P ERFORMANCE M EASURES

................................ ................................ 291
8.1.1 Bias and Error in Mean 2 m Temperatures

................................ ................................ ............................... 291

8.1.2 Bias and Error in Mean 10 m Wind Speeds

................................ ................................ .............................. 291

8.1.3 Bias and Error in Mean 10 m Wind Directions

................................ ................................ ........................ 292

8.1.4 Bias and Error in 2 m Mixing Ratios

................................ ................................ ................................ ......... 292

8.2 T IME S ERIES OF S PATIAL M EAN S URFACE W INDS
, T EMPERATURE AND M IXING R ATIOS

................................ ..... 292
8.2.1 Spatial Mean Temperatures

................................ ................................ ................................ .......................... 293

8.2.2 Vector Mean Wind Speeds

................................ ................................ ................................ ............................ 293

8.2.3 Mean Wind Direction Differences

................................ ................................ ................................ ............... 293

8.2.4 Index of Agreement

................................ ................................ ................................ ................................ ......... 293

8.2.5 RMSE Errors

................................ ................................ ................................ ................................ .................... 294

8. 2.6 Spatial Mean Mixing Ratios

................................ ................................ ................................ ........................ 294

8.3 C OMPARISON WITH O THER P ROGNOSTIC M ODEL E VALUATION S TUDIES

................................ ............................... 294
8.3.1 Bias and Error in Mean Temperatures

................................ ................................ ................................ ....... 294

8.3.2 Bias and Err or in Mean Mixing Ratios

................................ ................................ ................................ ..... 295

8.3.3 Average Discrepancy in Mean Wind Speed

................................ ................................ ............................... 295

8.3.4 RMSE Errors

................................ ................................ ................................ ................................ .................... 295

8.3.5 Index of Agreement

................................ ................................ ................................ ................................ ......... 296

8.3.6 A verage Discrepancy in Mean Wind Direction

................................ ................................ ........................ 296

9.0 SUMMARY AND CONCLUSIONS

................................ ................................ ................................ ........................... 333

9.1. O PERATIONAL E VALUATION S UMMARY

................................ ................................ ................................ ...................... 333
9.1.1 9

- 20 July 1995

................................ ................................ ................................ ................................ ................. 333
9.1.2 22

- 30 May 1995

................................ ................................ ................................ ................................ ............... 333
9.1.3 9

- 18 May 1993

................................ ................................ ................................ ................................ ................. 334

9.1.4 21 March

- 1 April 1993

................................ ................................ ................................ ................................ 334

9.1.5 6

- 14 February 1994

................................ ................................ ................................ ................................ ......... 334
9.1.6 21 July

- 1 August 1991

................................ ................................ ................................ ................................ . 335
9.1.7 22

- 29 June 1992

................................ ................................ ................................ ................................ .............. 3 37
9.1.8 24 April

– 3 May 1995

................................ ................................ ................................ ................................ ... 338

9.2 S CIENTIFIC E VALUATION S UMMARY

................................ ................................ ................................ ........................... 339
9.2.1 3

- 12 August 1993

................................ ................................ ................................ ................................ ............ 340

9.2.2 22

- 29 June 1992

................................ ................................ ................................ ................................ .............. 341

9.2.3 24 April

– 3 May 1995

................................ ................................ ................................ ................................ ... 341

9.3 C ONCLUSIONS

................................ ................................ ................................ ................................ ............................... 341
9.3.1 Aloft Winds and Temperatures

................................ ................................ ................................ ..................... 346

9. 4 A DEQUACY OF THE SAMI M ETEOROLOGICAL M ODELING FOR A IR Q UALITY M ODELING

................................ .... 347
9.4.1 Framing the Questions to Be Addressed

................................ ................................ ................................ .... 347

9.4.2 Comparison of SAMI RAMS Performance Against Newly Proposed Meteoro logical Model Performance Benchmarks

................................ ................................ ................................ ................................ ............... 353

9.4.3 Concluding Assessment of the SAMI RAMS Application

................................ ................................ ...... 354

9.5 I N S UMMARY

................................ ................................ ................................ ................................ ................................ . 361
9 REFERENCES

................................ ................................ ................................ ................................ ................................ .... 364

1 0. APPENDIX 1:

................................ ................................ ................................ ................................ ................................ . 391
11. APPENDIX 2:

................................ ................................ ................................ ................................ ................................ . 406
12. APPENDIX 3:

................................ ................................ ................................ ................................ ................................ . 418
13. APPENDIX 4:

................................ ................................ ................................ ................................ ................................ . 414
1. Introduction

................................ ................................ ................................ ................................ ................................ ........ 415
2. Meteorolo

gical Model Domain and Grid Configuration

................................ ................................ ............................. 416

2.1 M ETEOROLOGICAL M ODEL D OMAIN

................................ ................................ ................................ ........................... 416
2.1.1 Horizontal Extent of Meteorological Domain

................................ ................................ .......................... 416

2.1.2 Vertical Extent of Domain

................................ ................................ ................................ ............................ 417

### 4

2.2 G

RID C ONFIGURATION

................................ ................................ ................................ ................................ .................. 417
2.2.1 Horizontal Grid Configuration

................................ ................................ ................................ .................... 417

2.2.3 Vertical Grid Configuration

................................ ................................ ................................ ......................... 418

3. Model Inputs

................................ ................................ ................................ ................................ ................................ ....... 418

3.1 A TMOSPHERIC D ATA

................................ ................................ ................................ ................................ ................... 418

3.2 S URFACE C

HARACTERISTICS

................................ ................................ ................................ ................................ ....... 419
4. Model Configuration

................................ ................................ ................................ ................................ ......................... 419

4.1 N

ESTING VS

. S TAND

- A LONE D OMAINS

................................ ................................ ................................ ....................... 419

4.2 M OIST P ROCESSES

................................ ................................ ................................ ................................ ....................... 420
4.2.1 Cumulus Parameterization

................................ ................................ ................................ ............................ 420

4.2.2 Grid

- scale Moist Processes

................................ ................................ ................................ ............................ 420

4.3 F OUR

- D IMENSIONAL D ATA A SSIMILATION (FDDA)

................................ ................................ ................................ . 420

4.4 O THER C ONFIGURATION C ONSIDERATIONS

................................ ................................ ................................ ................ 421
4.4.1 Model Physics

................................ ................................ ................................ ................................ .................. 421

4.4.2 Finite

-

Difference Formulation

................................ ................................ ................................ ..................... 421

4.4.3 Diffusion

................................ ................................ ................................ ................................ ............................ 421

4.4.4 R adiation

................................ ................................ ................................ ................................ .......................... 421

4.4.5 Soil Model

................................ ................................ ................................ ................................ ......................... 422

4.4.6 Model Physics Changes

................................ ................................ ................................ ................................ . 422

5. Model Output

................................ ................................ ................................ ................................ ................................ ...... 422
6. Evaluation of Meteorological Fields

................................ ................................ ................................ ............................... 422

6.1 R

OUTINE O BSERVATIONS

................................ ................................ ................................ ................................ ............. 423

6.2 C OMPARISON WITH W IND /T EMPERATURE P ROFILER M EASUREMENTS

................................ ................................ .. 423

6.3 C OMPARISON OF C LOUD F IELDS WITH S ATELLITE I MAGES

................................ ................................ ....................... 423

6. 4 C OMPARISON WITH MM5 O UTPUT

................................ ................................ ................................ ............................ 424

6.5 L

AGRANGIAN P ARTICLE M ODEL T RAJECTORY V ISUALIZATIONS

................................ ................................ .............. 424

7. Post

- Processing of Meteorological Fields

................................ ................................ ................................ .................... 424

7.1 M AP P ROJECTIONS

................................ ................................ ................................ ................................ ....................... 424

7.2 I NTERPOLATION OF RAMS F IELDS TO THE URM

- 1ATM G RID

................................ ................................ .............. 425

7.3 C ONSERVATION OF M ASS

................................ ................................ ................................ ................................ ............ 425
8. Computational Considerations

................................ ................................ ................................ ................................ ........ 425
9. Quality Assurance

................................ ................................ ................................ ................................ ............................ 425

9.1 A PPROPRIATENESS OF I NPUT D ATA S ETS

................................ ................................ ................................ ................. 425
9.2 F ILE D OCUMENTATION

................................ ................................ ................................ ................................ ................. 427

### 5

EXECUTIVE SUMMARY

Environmental assessments made by the Southern Appalachian Mountains Ini tiative (SAMI) a re based on chemical transport modeling conducted by the Georgia Institute of Technology (GIT) and the Tennessee Valley Authority (TVA). For this work GIT and TVA used the GIT Urban

- Regional Multiscale URM

1ATM model. Both GIT and TVA used subcontractors

— University of Alabama in Huntsville (UAH) and Alpine Geophysics, LLC (AG), respectively

— to do the meteorological modeling. This was done using a version of the RAMS meteorological model that was modified and tested by Dr. Kevin Doty at UAH

.

Simulated meteorological fields were produced to drive the transport, diffusion and chemical reactions in the

URM

- 1ATM model. SAMI recognized early that, in an effort to address multiple air quality

- related impacts from ozone, fine aerosols (visibili ty) and acid deposition, it had to examine multiple pollutants over relatively long time periods. This required a methodology for estimating seasonal (for ozone) to annual (for visibility and deposition) pollutant levels. Simulations of 7

- 12 months in le ngth were impractical for studying multiple emission scenarios given the schedule and budget for the SAMI assessment. Therefore, SAMI chose to build its assessment using episodic modeling.

A contractor to SAMI classified as many days as possible during the 1991

- 1995 period according to meteorology and levels of ozone, aerosols and wet deposition observed at two target sites: Great Smoky Mountains National Park and Shenandoah National Park. Key time periods, or episodes, were identified

from these class ifications as being representative of similar conditions over the entire 5

- year period. Weights were assigned to the episodes (either by day or week, depending on pollutant), enabling SAMI to scale up episodic model results to seasonal and annual average conditions at the target sites. Consequently, 69 days in nine individual episodes were selected to represent seasonal or annual pollutant conditions with a minimum of bias. Two episodes occurred in winter (outside the ozone season), two were in spring an d four were in summer. Episodes were 6 to 9 days long.

Individual days were classified, when data were available, as falling into one of four ozone classes ranging from low to very high ozone levels. Similarly, days were classified as falling into one o f five visibility classes based on fine aerosol mass. Weeks were classified as falling into one of four wet deposition categories based on the sum of major cation and anion wet deposition species. Each target site had its own separate

set of classified d ays and weeks. Each modeling episode experienced precipitation, but some weeks had only light amounts and others experienced moderate or heavy amounts. Ozone season episodes experienced a wide range of daily ozone levels. However, visibility conditions (total aerosol mass) exhibited less variation within each episode.

UAH experimented with different model options and input data before selecting a common set to be applied across all episodes. A modeling domain was designed that, except for one episode, covered all the continental United States. The lone exception was the first episode modeled: a summer period that, because of light winds, was adequately simulated using a somewhat smaller grid. Initial and boundary nudging fields were derived using the NCEP Reanalysis data set. Nested model grids of different spatial resolution were used. In all cases the finest had a grid cell spacing of 12 km.

### 6

Several problems were experienced early in the modeling. One critical problem was achieving surface
temper ature and water vapor mixing ratio values that were unbiased, on average, across the modeling domain. Selective, non

- uniform surface nudging was implemented in the model. Note that no direct nudging of meteorological variables was done in the lower porti on of the finest (12

- km) grid. However, a unique form of surface heat and moisture flux nudging on the finest grid was applied to improve model performance. Another critical problem was initiating convective precipitation in regions where the predominant forcing mechanism was active at sub

- grid scales. In general, convective precipitation was difficult to reproduce. Problems with precipitation modeling caused significant delays in selecting the final version of the model. Some changes were made to the standard RAMS convective parameterization scheme, and for one episode an entirely different scheme was substituted. Other adjustments were made to standard RAMS microphysics, radiation schemes, the water vapor diffusion scheme, the soil moisture

initializ ation process and a few other physical parameterizations. All changes were made after efforts to model an episode indicated biases in various output variables.

Each meteorological simulation was evaluated against observations. Standard National Weather Service (NWS) surface observations were used to compute a suite of statistical metrics of surface model performance for temperature, mixing ratio, and wind speed and direction. NWS and TVA precipitation measurements were used to compute errors in model pr ecipitation amount. NWS upper air data were used

to evaluate mo del performance . These latter data heavily influenced the nudging fields. Thus, upper level comparisons with model results only indicated how well the model nudging was able to maintain cons istency with the nudging fields.

Grid

- averaged model bias, gross error, and other metrics were analyzed for the 12

- km grid. Two different techniques were used to estimate grid

- wide metrics. One method first produced a gridded field of observations by in

terpolating from the surface observations and then comparing vertically interpolated (to correct for height mismatch) model results with gridded observations. The other method compared observations at measurement sites directly with model results for grid cells containing the sites without doing any spatial interpolations of observations or model output. Statistical metrics varied between the two approaches. For example, the gridded method estimated a mean temperature b ias of about

- 1.2 oC across
all 9 e pisodes, whereas the direct

- comparison method yielded a bias, for 7 of the episodes, of

- 0.8 oC. However, the gridded technique produced a negative bias over elevated terrain that was an artifact of the methodology and contributed to the appearance of a b ias that was essentially unverifiable. Thus, the difference in results between the two methods is not considered important, whereas a surface temperature

bias of about

- 1 oC is considered to be quite good.

In other episode averaged comparisons, modeled s urface wind speed was found to be biased high across episodes by roughly 0.8 to 1.1 m s

- 1 

### . Modeled surface mixing ratio was found to be biased low by an amount somewhere between 0.1 and 0.6 g kg

- 1 

### . Modeled surface wind direction was computed by both

metho ds to have a bias, across episodes, within one degree of zero. Therefore, two methods both indicated that the meteorological modeling provided reasonably accurate surface

- level repro ductions of relevant
parameters

. O f course, large r biases and prediction

errors were found when comparing model results and observations on a daily and an hourly basis. Comparisons between modeled fields and observations aloft indicated that the maximum daily computed

### 7

mixing height over the fine grid was nearly always within ± 400 m of that estimated from upper air data. A couple episodes indicated some modest bias one way or the other. However, the spatial and temporal average for most episodes was typically within ± 200 m. A check of model conformity to upper air

conditions for three of the episodes revealed that temperatures usually agreed within about 0.5 oC and wind speeds usually agreed within 1 m s

- 1 

### . This level of agreement was expected because of the influence of nudging.

An overview is provided of meteorological mod eling performance statistics from over 20 modeling episodes other than SAMI. Regarding surface temperature, other studies have achieved results having biases in the

- 0.7 to 1.6 oC range. Most if not all of these studies relied on nudging to achieve such g ood results. The biases produced for the SAMI work are not that much different considering that no direct nudging of low

level temperature was done on the finest resolution grid. Likewise, mixing ratio bias for the non

- SAMI episodes ranged between

- 2.0 a nd 0.8 g kg

- 1 

### , while SAMI episodes were between

- 0.6 and 0.0 g kg

- 1



### . Wind speed root mean square error (RMSE) ranged between 1.6 and 3.2 m s

- 1 

### for the non

- SAMI studies,
and SAMI RMSE was between 1.9 and 2.8 m s - 1 

### . Finally, wind direction biases as larg e as 120 degrees

occurred in non

- SAMI episodes, while the largest for a SAMI episode was 103 degrees. These comparisons indicate that the SAMI modeling produced results that are at least as accurate as those for modeling done using other models, domains, and time periods.

One of the most important questions addressed in this report concerns whether the RAMS meteorological fields are adequate for their intended use in supporting the acid deposition modeling in SAMI. For the reasons discussed in Chapter 9, we are not able to answer this question definitively, yet a significant amount of information was developed in this study that supports our finding that the RAMS modeling results are

suitable for use in the UAM

- AERO acid deposition modeling although a num ber of important questions remain to be answered fully.

There is no simply way to answer definitively the question of whether the RAMS fields are adequate as input to the SAMI acid deposition model. There are no universally accepted performance benchmark s that, if passed, would allow one to declare unequivocally that the RAMS fields are appropriate for use. For complex atmospheric modeling problems like the ones being addressed by SAMI, it is quite doubtful that such a set of definitive performance crite ria will ever be completely sufficient. The question of meteorological data set adequacy depends, at a minimum, upon the specific host emissions and air quality models and the nature of the modeling episodes being used. Meteorological fields that might b e adequate for use in one situation may be quite deficient in another if the particular chemical and physical processes that must be simulated are different. Thus, quantitative statistical and graphical performance criteria, though helpful, are inherently

insufficient in telling modelers and decision

- makers whether meteorological fields are adequate for air quality modeling. In this study, we developed and then applied a multi

- step evaluation process whereby the adequacy of the RAMS fields for use in the SAMI acid deposition modeling was

evaluated.

In addition to comparing the SAMI results with a large range of previous meteorological model evaluation studies in the U.S., we also compared the RAMS evaluation results with a recently proposed set of

meteo rological model evaluation benchmarks based on the most recent model evaluation literature. While

### 8

these benchmarks are not aimed at assigning a passing or failing grade to a particular meteorological model application, they do help put the results into a useful context for decision

- makers. Based on these and other analyses reported in Chapter 9, o ur overall conclusions regarding the adequacy of the RAMS modeling and the reliability of the meteorological fields supplied to the URM

- AERO model are as follow s:

> The SAMI meteorological modeling activity clearly selected an appropriate regional prognostic model for use in the assessment;

> The RAMS modeling was carried out in a logical, sound, well

- documented manner that was consistent with good scientific principles and the procedures commonly used in the application of this sophisticated model;

> The suite of evaluation procedures employed to test the RAMS model were comprehensive and reflected several different model testing perspectives;

> The data base available to test the RAMS model was extremely limited, precluding a number of meaningful, stressful tests of the model to ascertain whether it suffers from internal, compensating errors; as the result, model testing was confined principally to an ope rational evaluation;

> Generally, the RAMS performance for surface and aloft winds, temperatures, mixing ratios, and precipitation are consistent with contemporary modeling experience and with new proposed evaluation benchmarks;

> In some cases, notabl y the under

- prediction bias for surface temperature and over

- prediction bias for surface wind speed, the RAMS model exhibits (for some episodes) features that could have an effect on the air quality model estimates; however, this has not been verified thro ugh sensitivity experiments with the URM

- AERO modeling system to demonstrate that these biases are indeed important. In other cases, notably mixing ratio, the RAMS performance was much better than is typically encountered with modeling of this complexity; and

> None of the performance testing results conducted have revealed flaws in RAMS performance of such a magnitude as to clearly indicate the presence of errors that would render the model inappropriate for use as input to regional air quality models.

We conclude that the RAMS meteorological fields may be used, with appropriate cautions, as input to the regional emissions and photochemical/aerosol models for each of the episodes selected for the SAMI

assessment.

### 9 Description of Selected Acronyms

Acro nym Description

AG Alpine Geophysics
AVHRR Advanced Very High Resolution Radiometer

FDDA Four

- Dimensional Data Assimilation
GIT Georgia Institute of Technology
GSM Great Smoky Mountains National Park
MAPS Model Performance Evaluation Analysis and Pl otting Software

MM5 NCAR/PSU Mesoscale Model version 5

MPE Model Performance Evaluation
NADP National Atmospheric Deposition Program
NCAR National Center for Atmospheric Research
NCEP National Centers for Environmental Prediction

NDVI Normalized Diff erential Vegetation Index
NOAA National Oceanic and Atmospheric Administration
NWS National Weather Service

PBL Planetary Boundary Layer
PSU The Pennsylvania State University
RAMS Regional Atmospheric Modeling System
SAMI Southern Appalachian Mountai n Initiative
SST Sea Surface Temperature
SNP Shenandoah National Park
UAH University of Alabama at Huntsville

URM Urban

- Regional Multiscale Model
UTC Universal Time Coordinate (UTC

- 5 hours = Eastern Standard Time)
TVA Tennessee Valley Authority

### 10

1 INT RODUCTION

1.1 Overview

Computer

- based modeling done as part of an integrated environmental impact assessment sponsored by the Southern Appalachian Mountains Initiative (SAMI) can be classified into four categories: 1) emissions, (2) meteorological, (3) a ir quality (atmospheric chemistry), and (4) environmental effects. Together this body of work represents an interdisciplinary approach for developing a comprehensive view of the relationships between anthropogenic emissions and the environment of the south ern Appalachians as of the mid

- 1990s. In addition, SAMI is using its modeling system to forecast environmental changes expected to occur in response to alternative future emission scenarios. This report, prepared by contractors to SAMI and a

member of th

e SAMI Atmospheric Modeling Subcommittee, describes the second modeling component that deals with the meteorological state of the atmosphere.

Meteorological modeling is necessary to provide certain required inputs to emissions modeling and to

describe t he state of the atmosphere in which a large number of chemical reactions determine air quality. Meteorology influences the transport and dispersion of pollutants, chemical reaction rates, and the natural (so

- called “deposition”) processes that remove poll utants from the air. Therefore, meteorological modeling is a critical early step in the SAMI integrated assessment. The meteorological modeling done for SAMI
represents a state

- of

- the

- art analysis using a three

- dimensional Eulerian (“grid”) model whose d omain covers the continental United States. Airflow, air turbulence, temperature, water vapor, cloud liquid water and rain water content of the atmosphere were each computed to a depth of over 17 km through the atmosphere. This information in various for ms was passed to the emissions and air quality models after completion of the meteorological simulations.

1.2 Episode Selection Criteria

Model simulations of the atmosphere, whether meteorological or chemical, make intensive use of computer

resources. Even with today’s high

- speed machines, modeling of more than a few weeks is usually cost

- and

time

- prohibitive. Unfortunately, an assessment of air pollution impacts must examine pollutant levels over multiple seasons or years in order to provide useful e stimates of long

- term environmental effects. Therefore, SAMI elected to model air pollution using an episodic approach. In essence, this approach selects a subset of modeling days that have some known relationship to longer time periods of interest. Aft er modeling is done the results can then be extrapolated to the longer periods. An unavoidable loss in accuracy occurs as a result. However, if modeling episodes are carefully selected then, in theory, the effect on accuracy can be quantified.

Before SA

MI selected modeling episodes it hired a contractor to develop an episode characterization and

selection scheme. This scheme is described in detail in section 4.1. The focus of the scheme was to characterize short periods (days or weeks) according to the level of pollutants observed during those periods. An observational data base was assembled for a period from 1991 through 1995. This data base included meteorological data and ground

- level air quality data at selected target sites. The air quality dat a base components were hourly ozone mixing ratio, 24

- hour average fine particle mass concentrations, and

### 11

weekly wet deposition totals for four cation/anion species, SO 4 2

- 

### , NO 3 - 

### , Ca 2+ 

### and Mg 2+ 

### . Estimated weekly dry deposition totals were later examined to classify time periods for dry deposition, but these data were not part of the episode selection process. Time periods for each pollutant type were then classified and the ability of various days to represent entire classes was estimated based on a statist ical analysis. Finally, various episodes were selected based on their overall ability to represent a full range of annual pollution conditions. SAMI selected nine episodes, a total of 69 days, for modeling. Details of the episode selection

criteria are found in section 4.1.

1.3 Meteorological Model Evaluation

Model performance evaluation (MPE) is the process of testing a model's ability to estimate accurately observed atmospheric properties over a range of synoptic and geophysical conditions. When con ducted thoughtfully and thoroughly, the process focuses and directs the continuing cycle of model development, data collection, model testing, diagnostic analysis, refinement, and re

- testing. At times, however, this process has been foreshortened in order to "validate" the model with readily available data so that its operational or scientific use can be justified. Below, we briefly summarize the philosophy and objectives that govern the evaluation of the RAMS prognostic model for the SAMI application and then identify the specific evaluation procedures that we employed to calculate the model’s performance relative to

measurements. I n Section 9.3 we present a formal process for judging the adequacy of the RAMS results

.

1.3.1 The Importance of Meteorologic al Model Performance Evaluation

Historically, the practice of meteorological MPE consisted of comparing observed and predicted (actually, estimated) atmospheric state variables. The principal assessments included comparisons of two

- dimensional

fields of predicted and observed variables, temporal comparisons of differences between observation s and

estimat es for individual monitoring sites, spatial comparisons of differences, as shown through residual maps, and a range of statistics, including regional and sub

- regional average bias, root mean square error, and differences in area wide statistics (e.g., precipitation amounts) independent of time and location. The focus of all these types of comparisons have typically been within the planetary boundary layer (PBL) up to 850

m illi b ars (mb) or so. A millibar is a unit of pressure often used in meteorology as a surrogate for height

above the surface. Pressure decreases with altitude. The standard (average) pressure at sea level is taken

to be 1013 mb.

No stan dard practice for judging meteorological model performance has evolved. While urban

- scale photochemical air quality models have been accepted for use in regulatory control strategy assessments when average discrepancies (e.g., gross errors) for ozone are of the order of 35% or less, and inaccuracy or bias was "not large." (i.e., + 5

- 15% according to EPA definition), an analogous set of performance goals

has yet to be adopted

for meteorological models intended f or air quality use (Seaman, 2000 ). Even when performance goals have been specified and achieved by air quality models, the models passing arbitrary performance criteria have often been found to contain significant flaws, typically in the form of internal compensating errors. Such errors can compromi se the overall reliability of the modeling applications. Thus, requiring a prognostic meteorological model to pass similar ad hoc performance goals runs a similar risk.

### 12

A key limitation in meteorological model performance evaluation to date has been the generally inadequate level of stressfulness to which models have been subjected in testing. Three main outcomes of testing are possible: A model performs inadequately and is so judged, a model performs well and is so judged, or a model appears to perform adequately but is, in fact, significantly flawed. To ensure during testing that a model reveals any flaw(s), it must be adequately "stressed," that is, subjected to testing that is designed to reveal and even highlight or amplify inherent inadequacies. B ecause performance testing of prognostic meteorological models has not been sufficiently stressful, flawed models (actually models plus input data bases) containing internal compensating errors have occasionally been accepted for use. Recommendations

for improvements to the meteorological MPE process have been proffered by several scientists (Tesche et al., 1990; Tesche, 1991a,b; 1994; Hanna, 1994; Seaman, 2000 ) motivated by a number of objectives, including improving the process, adequately stressing mode ls, improving the quality of available data bases, standardizing the practice, and demystifying the practice through clearer communication.

Guidelines have been developed (Reynolds et al., 1994 ; Roth et al., 1998 ) for providing a sound context for

model performance evaluation, establishing a common understanding of the process, and ensuring that evaluation efforts are properly formulated and reasonably complete. Elements of an “idea L model evaluation process ”

include: (a) evaluating the scientific formu lation of the model through a thorough review process, (b) assessing the fidelity of the computer code to the scientific formulation, governing equations, and numerical solution process, (c) evaluating the predictive performance of individual process modul es and preprocessor models (e.g., emissions and meteorological), (d) evaluating the predictive performance of the full model, (e) conducting sensitivity analyses, (f) carrying out corroborative analyses, (g) carrying out comparative modeling, and (h) imple menting a quality assurance activity. Ideally, all of these activities should be carried out in accordance with the procedures prescribed in an application

- specific M PE protocol.

Obviously, the effort suggested above is considerably greater than that cus tomarily devoted to MPE for either meteorological or air quality models. However, integrated meteorological, emissions and air quality models are being viewed as essential tools in the development of emissions control plans. The costs of

controls are suff

iciently high that decision

- makers as well as society in general will wish assurance that imposed controls will be effective in reducing air pollution levels. It is thus vital that the overall planning process includes sufficient time and resources for co nducting thorough evaluations of model performance.

1.3.2 Evaluation Objectives

The objective of the RAMS performance evaluation was to assess the adequacy of the surface and aloft meteorological fields estimated by the model for the nine (9) episode s selected by SAMI. More specifically, we have attempted to assess the adequacy and reliability of the meteorological fields for input to

the URM

- 1ATM regional photochemical model. Meteorological inputs required by URM

- 1ATM include hourly estimates of su

rface pressure and clouds; the three

- dimensional distribution of winds, temperatures, and mixing ratio; and other physical parameters or diagnosed quantities such as turbulent mixing rates (i.e., eddy diffusivities) and planetary boundary layer heights. A s described below, the RAMS evaluation centered on comparisons between surface and aloft meteorological measurements obtained principally from National Weather Service (NWS) reporting stations.

### 13

1.3.3 The Evaluation Process

As noted, the primary aim of the RAMS evaluation was to assess whether the simulated fields from the meteorological modeling systems may be relied upon to provide wind, temperature, mixing, moisture, and radiation inputs to URM

- 1ATM for a variety of adverse ozone, visibility and acid dep osition periods in the eastern U.S. We use the term "modeling system" to refer to the RAMS model source code, its preprocessor and data preparation programs, the underlying data base, and the post

- processor programs that map (i.e., interpolate) the simula ted meteorological fields onto the air quality model grid meshes.

Ideally, a comprehensive evaluation of RAMS would include all of the steps identified previously : Such an intensive evaluation process is rarely, if ever, carried out due to time, resour ce and data base limitations. Nevertheless, it is useful to identify the ideal evaluation framework so that the results of the

actual evaluation can be judged in the proper perspective. This also allows one to set realistic expectations

for the reliabili

ty and robustness of the actual evaluation findings.

The RAMS modeling system is well

- established with a rich development and refinement history spanning more than two decades (Pielke et al., 1992). The model has seen extensive use worldwide by many

ag encies, consultants, university scientists and research groups. Thus, the current version of the model as well as its predecessor versions have been extensively "peer

- reviewed" and considerable algorithm development and module testing has been carried out with all of the important process components. Accordingly, the performance evaluation focused three steps in the ideal testing process ; namely:

> Evaluate the full modeling system's predictive performance;

> Evaluate the direct meteorological output fr om the models as well as the mapped fields that are processed into air quality model

- ready inputs; and

> Implement a quality assurance activity.

Performance testing of the RAMS model is divided into two general categories: operational and scientific.

Th e operational evaluation refers to an assessment of a model’s ability to estimate atmospheric observations independent of whether the actual process descriptions in the model are accurate (Tesche, 1991a,b). It is an examination of how well the model repro duces the observed meteorological fields in time and space consistent with the input needs of the air quality model. Here, the primary emphasis is on the model's ability to reproduce hourly surface wind speed, wind direction, temperature, and mixing ratio observations across the 12 km grid domain. The operational evaluation provides only limited information about whether the results are correct from a scientific perspective or whether they are the fortuitous product of compensating errors. Thus, a "succe ssful" operational evaluation is a necessary but insufficient condition for achieving a sound, reliable performance testing exercise. An additional scientific evaluation is also

needed.

The scientific evaluation attempts to elucidate the realism of the b asic meteorological processes simulated by the model. This involves testing the model as an entire system (i.e., not merely focusing on surface wind

### 14

predictions) as well as its component parts. The scientific evaluation seeks to determine whether the

mod el's behavior in the aggregate and in its component modules is consistent with prevailing theory, knowledge of physical processes, and observations. The main objective is to reveal the presence of bias and internal (compensating) errors in the model that, unless discovered and rectified, or at least quantified, may lead to erroneous or fundamentally incorrect technical or policy decisions. Typically, the scope of the scientific evaluation is limited by the availability of special meteorological observation s (radar profiler winds, turbulence measurements, PBL heights, precipitation and radiation measurements, inert tracer diffusion experiments, and so on). Unfortunately little if any of this type of supplemental data collection was performed during the SAMI episodes. Thus, the scientific evaluation component in this study is quite limited, focusing

on the models ability to reproduce daily precipitation amounts. Furthermore, due to the leak of adequate diagnostic information, where errors in the modeled pre cipitation fields arise, it is very

difficult to assign a specific course.

1.3.4 Compl e mentary Perspectives Brought to this Evaluation

One consequence of SAMI’s selection of two different groups to perform the meteorological modeling

elements of its stu

dy is that somewhat different perspectives are introduced into the model performance evaluation portion of the research. Generally speaking, the researchers at UAH examine model performance from a ‘synoptic’ or broad

- scale perspective while scientists at A G emphasize the fidelity of model predictions in the vicinity of surface and aloft measurement sites within the planetary boundary layer. While there is definitely overlap between the two perspectives, the analytical procedures, statistical metrics and g raphical tools employed by the two groups differ somewhat, reflecting their particular perspective and experience in prognostic model evaluation. Actually, these differences a re a strength, leading to comple mentary and corroborating examinations of perfor mance and producing insights into the model’s operation that might not be as thoroughly examined via one approach alone. As a result, the results of the episodes evaluated by UAH (e.g., July 1995, May 1995, May 1993, March 1993, February 1994, and July 19 91) are presented using the UAH

- developed analysis software. In contrast, the MAPS evaluation software developed by AG was used in performance testing of the August 1993, June 1992, and April

May 1995 episodes. While the graphical presentation methods di ffer slightly, both have been used extensively and effectively in past evaluations reported broadly in the literature. Thus, readers should have minimal difficulty understanding each approach.

To integrate the UAH and AG evaluations with a common set of statistical and graphical tools, we have also

re

- evaluated seven of the episodes (four from UAH and three from AG) using certain of the MAPS statistical and graphical measures. Moreover, the UAH surface layer methodology was used to compute

estimate mixin

g ratio, temperature, and wind speed and direction performance statistics for all nine of the SAMI episodes. Thus, the RAMS evaluation presented in this report features two complimentary perspectives from which the model testing is carried out together wi th two additional analyses that summarize most or all of the modeling episodes using common statistical and/or graphical tools. We feel this additional effort strengthens the overall evaluation of the model and far outweighs any inconvenience

introduced b y, say, different plotting methods in a common report.

### 15

1. 3. 5 Structure of Report

This report is organized as follows: Details of the meteorological model are described in section 2 and the application of the model is described in section 3. Section 4 outlines the episode selection process. The approach for the meteorological model performance evaluation is described in section 5. Results of the surface evaluation of the modeling are presented in section 6 and the evaluation of conditions aloft is

pre sented in section 7. A comparison between SAMI modeling performance and that for other modeling exercises is presented in section 8. The report is summarized in section 9 together with an assessment of the suitability for the RAMS meteorological field as inputs to the URM

- 1ATM model.

2.0 THE METEOROLOGICAL MODEL

2 .1 Overview of RAMS

The Regional Atmospheric Modeling System (RAMS) version 3a was the meteorological model used for the SAMI simulations. A description of the RAMS model and the modeling p rocess is provided in the SAMI meteorological modeling protocol (see Appendix 4). The reader is referred to the protocol

document for details not covered here.

RAMS is a three

- dimensional primitive

- equation model which has a sigma

- z vertical coordinate a nd a choice of horizontal coordinates. For the SAMI simulations the horizontal coordinate system was an oblique stereographic map projection which yields map scale factors close to 1.0 if the map projection pole

is chosen close to the center of the coarse grid domain. RAMS is a merger of a hydrostatic model originally developed at the University of Virginia (Pielke, 1974; Mahrer and Pielke, 1977; McNider and Pielke, 1981, 1984) and a non

- hydrostatic model developed by Tripoli and Cotton (1982) at Colorado State University. RAMS was chosen as the meteorological model because of its previous successful simulations in the southeastern United States of circulations important in air pollution (e.g., Casey et al. 1995; Mueller et al. 1996) and because of the ex perience of the UAH contractor in using the model.

The July 1995 episode was the first simulation performed. This was followed by attempts at simulating the April 1994 episode. Examination of the results of these two simulations led to a series of chang es to the RAMS system to improve performance in light of the SAMI requirements. The rest of the episodes were performed with essentially a fixed set of procedures and program codes. The inability to produce a realistic precipitation field for the April 19 94 case led to it being replaced by the July 1991 episode.

2.2 RAMS Horizontal Grid Structure

Figure 2

- 1 shows the nested horizontal grid structure used for the July 1995 episode. The coarse, intermediate, and fine grids had grid cells of 48

- , 24

- , and 12

- km, respectively. The next simulation attempted was the April 1994 episode and it revealed that the original grid structure would need to be changed because of unacceptable interactions between the eastern portions of the 12

- and 24

- km grids in high sp eed flows. The nested grid arrangement used for all the other episodes is shown in

### 16

Fig ure 2

- 2. The coarse, intermediate, and fine grids for this new grid setup had grid cells of 96

- , 24

- , and

12

- km, respectively. The 96

- km grid was chosen so that its b orders were as far as possible from the other two grids and from natural boundaries such as the Rocky Mountains and land

- ocean boundaries parallel with one or more grid edges.

Fig ure 2

- 1

. Nested grid system used for the July 1995 episode only. Only every other grid point is plotted for each of the grids by a “+”. “CLAT” and “CLON” in the top label refer to the central latitude and longitude, respectively, for each grid.

“DX” and “DY” refer to the horizontal mesh siz e in km. “NX” and “NY” refer to the number of the grid points in the west

east and north

- south directions, respectively.

### 17

2.3 RAMS Vertical Grid Structure

RAMS employs a sigma

- z vertical coordinate defined by

(2.1) 

# ( ) ( ) E H E z H h k

k



### − −

= ,

w here h

k 

### is the sigma

- z coordinate at level k, z k 

### is the actual height above mean sea level, H is a scale height at the top of the model domain where the sigma

- z levels are horizontal, and E is the terrain height above

mean sea level. RAMS has two vertical grids which are staggered with respect to each other. The first grid has only the vertical velocity defined on it and its first level is at the surface. The second grid with all the other variables has its first level below ground but has the same total number of levels. For the SAMI grid F igure 2

- 2

. Nested grid system used for all the episodes after the July 1995 episode. Only every other grid point is plotted for each of the grids by a “+”. “CLAT” and “CLON” in the top label

refer to the central latitude and longitude, respectively, for each grid. “DX” and “DY” refer to the horizontal mesh size in km. “NX” and “NY” refer to the number of the grid points in the west

- east
and north

- south directions, respectively.

### 18

35 vertical levels were chosen with H equal to 17.4 km. The SAMI grid is shown in Fig ure 2

- 3 for the second vertical grid with the exception that the first sigma

- z level is plotted at the surface. The sigma

- z surfaces follow the terrain surface in an approximate manner and are flat over areas where the terrain surface is uniform. In sigma

- z coordinates, for either vertical grid, the first level above ground has a depth of

20 m.

Figure 2

- 3

. Sigma

- z vertical coordinate system with 35 l evels used for all episodes. Hypothetical mountain is shown with a maximum altitude of about 1.2 km which is about the maximum height of the

actual terrain on the 12

- km horizontal grid over the southern Appalachian Mountains.

### 19

2.4 RAMS Model Modificatio ns

2.4.1 Surface Grid Files

The original scheme in RAMS calculates the surface fields of terrain, vegetation type, land percentage, and sea surface temperatures (SST) for each grid from data files which are part of the modeling system. The

leaf area in dex (LAI) and vegetation fraction are calculated primarily through a single seasonal temperature. The soil type is also specified as a single type. The latter approach for the LAI, vegetation fraction, and soil type was used for the July 1995 episode. I nitial simulations of the next episode (April 1994) revealed that the LAI and vegetation fraction fields would need to be defined in a better way. Soil type and vegetation fraction data were obtained from the Earth System Science Center (ESSC) of the Penn sylvania State University (

http://www.essc.psu.edu/) . The State Soil Geographic (STATSGO) data set provided 1

- km resolution soil type data for the continental United States. The vegetation fraction data were deriv ed from Advanced Very High Resolution Radiometer (AVHRR) satellite imagery using a normalized differential vegetation index (NDVI)

. These data had a spatial resolution of 1 km and were available as biweekly composites. NDVI data sets were chosen with be ginning and ending times which best fit the SAMI episode. Another major data set was a CD

- ROM collection from the International Satellite Land Surface Climatology Project (ISLSCP). This data set provided global 1 o 

### latitude

- longitude resolution for leaf a rea index, soil type, and vegetation fraction data. The 1 o 

### soil type and vegetation fraction data provided coverage for those fields for the 96

- km coarse grid which were not covered by the STATSGO and NDVI

data. The 1 o 

### leaf area index data was interpolate d to each of the nested SAMI grids. The ISLSCP soil type and vegetation fraction data were monthly averages from the year 1987 and were used by choosing the closest month for the SAMI episode being considered. Another major data set which will be discuss ed further in section 3.1 is the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (Kalnay et al. 1996). The reanalysis data was used to provide SST data rather than the RAMS monthly climato logical values for all episodes. Original resolution of the reanalyis SST data is 1.875 o 

### longitude and approximately the same resolution by latitude. All the episodes after the July 1995 episode used these data sets to define the surface characteristics.

The soil moisture for the July 1995 episode was initialized by interpreting the reanalysis soil moisture values as a fraction of saturation and multiplying them times the RAMS saturation values. As discussed in section 6.1 there was a significant dry bias for boundary layer values of water vapor mixing ratio in the July 1995 simulation. The remaining episodes used a specified fraction of the reanalysis soil moisture for initializing the RAMS soil moisture values. The remaining episodes also used the surf ace nudging technique described in section 2.4.2.5 which reduces the impact of the soil moisture. In areas where the surface nudging is not done (primarily over mountainous areas) the primary influence of the soil moisture is the impact on the soil specif ic heat values.

Figure 2

- 4 illustrates the terrain field for the 12

- km grid. The highest terrain had an altitude on the order

of 1.1 km.

### 20

2.4.2 Model Physics Changes

2.4.2.1 Microphysics

The complete RAMS microphysics suite of condensate regime s includes cloud water and six precipitation types: rain, pristine ice, snow, ice aggregates, graupel, and hail. A subset of these types was selected for the SAMI simulations because of time constraints and computer memory limitations. Only cloud water an d rain precipitation water were activated for all episodes. In attempting to simulate the second episode (April 1994) it became apparent that several changes would be required in the microphysics with such a limited choice of condensate types. Unrealisti c cloud water values at heights above 5 km led to changing the RAMS autoconversion of cloud water to a simple scheme as in Kessler (1969)

(1) 

# [ ]

r r k r

crit

c a c


### t − = ∂ ∂

,

where r

c 

### is the cloud water mixing ratio, r crit 

### is a threshold value, and k
a 

### is a constant set to 10

- 3 

### s. When cloud water values are below the threshold value the autoconversion tendency is set to zero. The threshold value was determined by Figure 2

- 4 . Terrai

n for the 12

- km grid used for episodes after the July 1995 episode. The highest values are near 1.1 km over the West Virginia

- Virginia and Carolinas

- Tennessee borders.

### 21

(2) 

# [ ] kg g 

# r r sat v crit 1

, 

### 5

. 0

, 10

. 0 min −



### = ,

where r

v,sat 

### is the saturation water vapor mixing ratio. Equation (2) then sets the threshold value to the smallest of either 10% of the saturation water vapor mixing ratio or 0.50 g kg

- 1



### .

Two other changes where made to accommodate the small selection of condensate types. One was to make the terminal velocity of rain a fu nction of temperature. For temperatures above 0 o 

### C the default RAMS values where used. For temperatures below

– 20 o 

### C a value of 1 m s

- 1 

### was used to imitate the effects of small ice particles. For temperatures between the two limits a linear transition was used. The other change was to use the same temperature limits to make the collection efficiency a function of temperature. For temperatures above 0 o 

### C the efficiency was set to 1.0 and for temperatures below

– 20 o 

### C

it was set to 0.50.

2.4.2.2 Kuo Convectiv e Scheme

The only choice of a convective parameterization for this version of RAMS is a modified Kuo (1974) scheme with a simple downdraft. Initial simulations of the July 1995 episode revealed a need for some changes to the convective updraft calculatio ns. The initial simulations revealed areas with unrealistically deep convection and also large areas of model convection were produced where none was observed. A simple entrainment model as originally proposed by Turner (1962) was introduced

(3) R

z M

M 20

. 0 1

= ∂ ∂



### ,

where M and R are the updraft mass and radius, respectively. The updraft radius was set to 1500 m as a typical value after Kain and Fritsch (1990). This change reduced the tendency for convection to be too deep in some areas. The def ault RAMS code allowed convection to occur in areas with large convective available potential energy (CAPE) but also with extremely large magnitudes of convective inhibition (CIN) (Colby, 1984), which in reality prevent convection from occurring. A CIN cal culation was introduced into

the Kuo scheme such that no convection was allowed to occur if the CIN was less than

- 20 J kg

- 1


### regardless of the CAPE value. This change produced the needed results when compared with observations

for initial simulations of th

e July 1995 episode.

The last change to the Kuo convective scheme allowed more flexibility in the maximum downdraft cooling at the surface. The original formulation set the surface cooling magnitude to the largest difference between the updraft and the e nvironment with a limit of 2.5 o 

### C imposed. As an alternative the evaporation formula from Rutledge and Hobbs (1983) was used to calculate cooling rates beneath cloud base. This approach allowed more realistic variability for downdraft temperatures as a fu nction of the relative humidity of the

environment.

2.4.2.3 Radiation Calculations

### 22

Results from the July 1995 and April 1994 episodes revealed that clouds and precipitation were having too large of an impact on surface temperatures. In areas of precipi tation this was characterized by surface temperatures being too cool during the daytime and to a lesser extent being too warm at night. One possible explanation for this is that cloud water values may be too large given the simple set of microphysics

chos en for these simulations. No observations exist to verify cloud water values but the model values for all the simulations seemed reasonable even with the microphysics options chosen. The Stephens (1978) parameterization uses the vertical liquid water pat h (LWP) as a means to introduce condensate effects into the radiation calculations. The LWP for a given model layer is given by

(4) z

f LWP 

# r t 

### Δ = r


### ,

where f is an arbitrary factor which will be discussed later, ρ is the air density, r t 

### is the t otal condensate mixing ratio including cloud and all types of precipitation, and Δ z is the thickness of the model layer. To help with the problems discussed above, r t 

### was changed to include only cloud water and not precipitation water. In the original

ve rsion there was no factor f but was introduced to reduce the LWP values. For the longwave calculations f was set to 10

- 2 

### while for the shortwave calculations it was set as a function of temperature. For temperatures greater than

– 5 o 

### C it was set to 0.50 an d for temperatures less than

– 15 o 

### C it

was set to 10

- 2 

### . For temperatures in between it was set as a linear transition between the two values. These changes helped with the temperature errors in areas of precipitation but did not remove them entirely.

In the original RAMS code the cloud fraction which is used in the radiation calculations is either one or zero. The Ek and Mahrt (1991) cloud fraction scheme was introduced as described by Mocko and Cotton (1995). This allowed cloud fractions in the unstabl e boundary layer to be a function of the horizontal mesh size, the surface moisture flux, and the standard deviation of the subgrid

- scale vertical velocity. This change was made in an effort to improve the surface temperature performance of the model.

2. 4.2.4 Nudging

The original nudging scheme in RAMS can be described as

(5) 

# ( )

A A A

m

nd m 

### W
t − = ∂ ∂

,

where A

m 

### is a given model variable, A nd 

### is the value being nudged towards, and W is a weight which is the inverse of a time scale which provides the magni tude of the desired nudging. The weight W is the maximum of two weighting schemes: one is a quadratic function of the horizontal distance from the nearest model grid edge, and the other is a linear function of height. The quadratic scheme can be describ ed by

(6) 

# ( ) d d W W W W

m h 2 2

max min max 

### − + = ,

where d is the horizontal distance from the nearest model grid edge, d

m 

### is the distance beyond which

### 23

W h 

### =W min 

### , and the weights W min 

### and W

max 

### are defined by the time scales as in (7). The distance d

m 

### is represented by t

he RAMS variable NUDLAT.

(7)
      

= = =

TNUDTOP TNUDCENT TNUDLAT



# W W W

top 

### 1 1 1

max , min max



### The vertical scheme is described by (8) where z is the height of a given model grid point, z t 

### is the height of the top model level, z b 

### is a specified height which is the lowest height where the linear scheme is calculated,

and W

top,max 

### is defined by a time scale as in (7). The height z b 

### is represented by the RAMS variable ZNUDTOP. The RAMS variables TNUDLAT, TNUDCENT, TNUDTOP, NUDLAT, and ZNUDTOP are specified in the RAMS file "RAMSIN."

(8) 

# z z z z W z z z z W W

b t b

top
b t t

z



### − − +

− −

=

max , min



### For heights below z b 

### the quadratic scheme is used to give W while for heights above z b 

### the maximum of the two schemes is used. In the original version of RAMS there is no provision for varying the RAMS variables TNUDLAT, TNUDCENT, TNUDTOP, NUDLAT, and ZNUDTOP across the nested grids. These were made variable for the SAMI episodes after the July 1995 episode and the values used are described in section 2.6. The values were chosen so that nudging was performed strongly on the coarse grid bu t then reduced sharply on the intermediate grid and with little or no nudging on the fine grid. The exception to this was near the model top where strong nudging was done on all grids to control noise production near the top boundary.

This original RAM S weighting scheme was modified for the SAMI simulations as in (9):

(9) 

# W

W W W
col pbl adj 

### =



### where W adj 

### is the new adjusted weight and W pbl 

### and W col 

### are adjustments to the original RAMS weight W.

The W

pbl 

### adjustment is set to zero for the lowest 2 km in order to allow the model physics to perform fully without interference from the nudging fields which do not have the spatial and temporal resolution to be used

there. The W col 

### was also used to turn off the nudging of moisture for lower levels where the nudging fields were less accurate with respect to the observed moisture.

The original RAMS model uses the Louis (1979) parameterization in determining the surface fluxes which eliminates the need for any iterative calculations to determine the Monin

- Obuk hov (1954) length. Beljaars and Holtslag (1991) provide evidence that the difference between the Louis (1979) approach and an exact calculation can be large in stable situations. Therefore, an efficient iterative approach which explicitly solves

for the Monin

- Obukhov length as a function of the surface Richardson number was implemented using the

### 24

general recommendations of Beljaars and Holtslag (1991). The transition of the surface fluxes to the free convection regime was implemented in a fashion similar to Beljaars (1994).

2.4.2.5 Surface Nudging

The July 1995 episode and attempts at simulating the April 1994 episode demonstrated the inability to specify soil moisture adequately to enable reasonable calculation of the surface heat and moisture fluxes b y the model. This led to unacceptable errors in temperature and moisture in the planetary boundary layer (PBL) which could become quite large in the 10

- day simulations required for SAMI. A scheme was developed which used observed analyses of near

- surface temperature, moisture, and wind to improve the PBL performance of the model. The basics of the scheme are described by equations 10

- 14. They use a generic variable “A” which can represent temperature, water vapor mixing ratio, or wind speed. Using

Monin

- Obukhov (1954) similarity theory the vertical gradient of a variable “A” in the PBL surface layer is given by (10) (see e.g., Beljaars and Holtslag, 1991):

(10) 

### F A L z L z

z z

v A A A r

r k z 

### *

ln *

0 

### =    

    

      

+        

− =

− y

y ,

where the subscript z represents values at a specified level above the surface at height z (2 m for temperature, 10 m for winds), the subscript “0” represents surface values, A* is the similarity scaling
parameter, v

k 

### is the von Karmen constant, z

r 

### is an appropriate roughness length, Ψ is the integrated similarity functio n, and L is the Monin

- Obukhov (1954) length. The right

- hand side of (10) expresses the equation in a simple manner where the von

- Karmen constant and the terms in the brackets have been

relabeled as “F.”

(11) 

### F A L z L z

z z

v A A A obs r

r k obs obs z 

### *

ln *

0

, 

### =    

    

      

+        

− =

− y

y

Equation (11) shows the same relationship but expressed for the observed value of A denoted by A z,obs 

### with
a corresponding scaling parameter 

# A
obs * 

### . This is a correct statement if one assumes that the function “F”
remains unchanged which is acceptable for th e short time steps used in the model. Subtracting (11) from (10) gives equation (12) which expresses the difference between the observed and model similarity scaling parameter as a function of the difference between the observed and model values at height z.

(12) 

# ( )

F A A
A A A z obs z

obs 

### −

= − = Δ ,



### * * *

The scaling parameter is then nudged toward a value in agreement with similarity theory by (13), where

### 25

Δ t s 

### is a specified time scale (10 min for all episodes), and Δ t m 

### is the model time step.

(13) t

t A A A

m

s new 

### Δ

Δ Δ + = * * *



### The surface "skin" values of A are nudged in a similar manner based upon the difference between the observed and model values at height z as in (14).

(14) t t A A A A

m

s z obs z

new 

### Δ

Δ − + = , 0

, 0



### Test simulations showed that this technique improved near

- surface values of temperature and moisture significantly but had little effect on winds. All three variables were nudged in this manner but the major impact was on the surface fluxes of heat and moisture. The near

- surface analyses of the required observed

values are ob tained by a technique which is described in section 3.2. Figures 2

- 5 to 2

- 7 illustrate the regions where the surface nudging was performed on the 96

- , 24

- , and 12

- km grids, respectively. The surface data used to create the observed surface analysis were o nly available for the United States and southern Canada. Surface nudging was not done in areas with no available data, areas over water, and areas exceeding specified terrain slope limits for each grid.

Figure 2

- 5

. Colored areas denote where sur face nudging was performed on the 96

- km grid as described in
the text. Non

- colored regions are where surface nudging was not performed because of terrain slope, a water surface, or unavailability of data.

### 26 Figure 2

- 6

. Colored areas denote where surface nudging was perform ed on the 24

- km grid as described in
the text. Non

- colored regions are where surface nudging was not performed because of terrain slope, a water surface, or unavailability of data

.

Figure 2

- 7

. Colored areas denote where sur face nudging was performed on the 12

- km grid as described in
the text. Non

- colored regions are where surface nudging was not performed because of terrain slope, a water surface, or unavailability of data.

### 27

2.4.2.6 Surface Energ y Budget

Initial simulations of the April 1994 episode revealed a strong cool bias of surface temperatures over vegetated areas. This led to changes in the radiation balance for the soil and vegetation canopy which considerably decreased the cool bias. The approach chosen was a simple version based on ideas taken primarily from Avissar and Mahrer (1988) but also some details from Sellers et al. 1986 and Yamazaki et
al. 1992.

First the changes to the radiation balance for the soil surface will be discu ssed briefly. The transmissivity for both shortwave and longwave radiation for vegetation was calculated by

(15) 

# ( )

L

veg 

### 50

. 0 exp

− = 

# t

,
where τ veg 

### is the vegetation transmissivity and L is the leaf area index. The net shortwave radiation for soil

cov ered by vegetation is given by

(16) 

# ( ) R

f R

s veg g a soil s 

# t a

− = 1

, 

### ,

where R

s,soil 

### is the shortwave radiation absorbed by the soil, R
s 

### is the downward shortwave radiation just above the surface, α g 

### is the soil albedo, and f
a 

### is an albedo reduction factor. The latter factor was set to 0.50 over mainly mountainous areas where the surface nudging is not done to help the remaining cool bias. The net shortwave radiation for bare soil is given by (16) with the vegetation transmissivity set to 1.0. The only other ch

ange made for the soil shortwave radiation was to set the albedo to a fixed value of 0.80 for areas with snow cover for the February 1994 episode. The contribution from the atmospheric downward longwave to soil covered by vegetation is given by

(17) 

# R R
L

veg v soil L 

# t e =

, 

### ,

where R

L,soil 

### is the longwave radiation absorbed by the soil, R L 

### is the downward longwave radiation just above the surface, and ε v 

### is the vegetation emissivity. The atmospheric longwave radiation for bare soil is given by (17) with t

he vegetation transmissivity and the vegetation emissivity set to 1.0. The longwave contribution from the vegetation to the soil surface is described by

(18) 

# ( )

( ) 

# ( )

( ) ( ) ( ) c b a T T T R g g v veg v g g v veg g soil Lv 4 4 4 2

, 

### 1

1 1 s e s t e e s e t e

− − +

− − =

,

where R

Lv,soil 

### is the longwave radiation emitted by the vegetation an d absorbed by the soil, ε g 

### is the soil emissivity, T

g 

### is the soil skin temperature, T

v 

### is the bulk canopy vegetation temperature, and σ is the Stefan

Boltzmann constant. In (18) term (a) represents the radiation initially emitted from the soil and reflect ed back to the surface, term (b) represents the radiation emitted from the vegetation to the surface, and term

### 28

(c) represents the emission from the soil surface itself. For soil with no vegetation terms (a) and (b) are

zero.

Total shortwave radiation absorbed by the vegetation, R s,veg 

### , is given by (19) where α v 

### is the vegetation albedo. Term (a) in (19) is the portion of the downward solar radiation which is directly absorbed while term (b) is that portion which is absorbed after one reflection from t he ground.

(19) 

# ( )

( ) ( )

b a 

# R f R f R

s veg g a s v a veg veg s 

# t

a a t +

− − = 1

,



### The atmospheric longwave radiation absorbed by the vegetation, R L,veg 

### is given by (20). The remaining components of the vegetation longwave balance are given by (21) where term (a) represents the absorption

of som e of the longwave radiation emitted by the vegetation initially and then reflected from the soil surface, and term (b) represents the emission of longwave radiation from both sides of the vegetation canopy.

(20) 

# ( ) R R
L veg v veg L 

# t e

− = 1

,



### (21) 

# ( )

( ) ( )

( ) ( ) b a 

# T T R

v veg v v g veg v veg Lv 4 4 2

, 

### 1 2 1 1 s

s 

# t e e t e

−

− − − =

The bulk specific heat for the vegetation canopy was changed to the formulation suggested by Sellers et
al. 1986

(22) LAI

c c ww pv 

### 20

. 0 = ,

where c pv 

### is the vegetation specific heat, c

ww 

### is the specific heat for liquid water, and LAI is th e leaf area

index.

2.4.2.7 Alternate Convective Parameterization

Initial simulations of the July 1991 episode (chosen to replace the April 1994 episode) showed poor precipitation performance using the Kuo convective parameterization. An alternate scheme was used only for this episode. It consists of a plume model for the convective updraft and downdraft and a mass balance approach to determine the environmental subsidence. A closure scheme is then used for weighting the contributions from the updraft, downdraft, and environment to the grid

- scale tendencies of temperature and

moisture.

The updraft plume model follows the Kain and Fritsch (1990) approach for calculating the entrainment of environmental air into the updraft. The level chosen for starting the updraft parcel is the one with the largest

### 29

CAPE greater than 50 J kg

- 1 

### with a CIN greater than

– 20 J kg

- 1 

### and with a positive grid

- scale vertical velocity. The initial updraft radius is taken as the largest of the following: a radius which is a funct ion of the bulk Richardson number, a radius based on the planetary boundary layer (PBL) mass flux, and a radius which is a function of the depth of the PBL. The updraft calculations also include the following: 1) a simple form of Kessler (1969) microphysi cs; 2) the effects of water loading, friction, and perturbation pressure on the vertical velocity; 3) freezing of condensate water; and 4) the updraft radius is calculated by a mass conservation approach.

The downdraft calculations also use a plume mode l and are done after the updraft calculations. The initial and highest starting level for the downdraft calculations is the level of the minimum saturated equivalent potential temperature (LFS). Downdrafts are attempted starting at this level and at cons ecutively lower levels down to the updraft condensation level (LCL). The acceptable downdraft is the first one which reaches the ground or if none reach the ground the one with the deepest thickness. The initial downdraft starting radius is set to the ave rage updraft radius between the LFS and LCL heights. The downdraft plume model includes the effects of water loading, ice melting, friction, entrainment, and the evaporation of precipitation. The downdraft is fed by a steady

- state calculation of precipit ation from the updraft. This

initial downdraft calculation is then modified to achieve a rain

- cooled pool of air below cloud base. The total mass of the cool pool is calculated from the initial downdraft values of vertical velocity and radius at

cloud ba se and a time scale required to build the updraft above cloud base, which for this discussion is denoted by τ u 

### . The initial downdraft radius profile between the levels LFS and LCL is then adjusted by iteration to provide a time required to build the compl ete cool pool which is close to the time scale τ u 

### .

The last major set of calculations involve the determination of an environmental radius, R
e 

### , which controls the magnitude of the environmental subsidence and provides a means to weight the updraft, down draft, and

environmental contributions to the total tendencies. The radius R
e 

### is chosen as the largest of the following: the radius of PBL air required to build the updraft, the surface cool pool radius, the maximum updraft radius, and a radius based on the mean wind speed over the updraft depth and a time scale based on the

mean Brunt

- Vaisala frequency over the upper half of the updraft. The environmental heating and moistening rates are determined by the vertical gradients of temperature and moisture m ultiplied by an environmental vertical velocity, W
e 

### . The velocity W
e 

### is calculated from a mass flux conservation approach which allows environmental subsidence to complete the mass balance from the residual of the updraft and downdraft

mass fluxes. The f

inal step in determining the grid

- scale tendencies is an area weighting of the updraft, downdraft, and environmental tendencies.

2.4.2.8 Changes in Other Physical Parameterizations

The diffusivity of water vapor was changed to a formulation which include d the effects of pressure following the formula given by Pruppacher and Klett (1980). The thermal conductivity of dry air was also changed to a formula given by Pruppacher and Klett (1980). The saturation vapor pressure formulation after the July 1995 epi sode was changed to the absolute norm of the eighth order scheme described by Flatau et al. (1992) to accommodate a broader range of possible temperatures.

A summary of the changes described in this section across the episodes is given in Table 2

- 1.

### 30 T able 2

- 1 Summary of main changes to the RAMS modeling system across all episodes.

EPISODES Modification Type July
1995 May
1993 May
1995 Mar
1993 Feb
1994 July
1991 Aug
1993 June
1992 April
1995

Surface Grid

Definition no yes yes yes yes yes yes yes yes

Microphysics no yes yes yes yes yes yes yes yes Kuo Convective
Scheme yes yes yes yes yes yes yes yes yes

Radiation
Calculations yes yes yes yes yes yes yes yes yes

Nudging yes yes yes yes yes yes yes yes yes Surface Layer
Calculations yes yes y es yes yes yes yes yes yes

Surface Nudging no yes yes yes yes yes yes yes yes

Surface Radiation Budget no yes yes yes yes yes yes yes yes

Alternate Convective

Parameterization no no no no no yes no no no

Water Vapor Diffusivity Change yes yes yes yes yes yes yes yes yes

Thermal Conductivity Change yes yes yes yes yes yes yes yes yes

Saturation Water Vapor Change no yes yes yes yes yes yes yes yes



### 2.5 Other Differences with Respect to Meteorological Modeling Protocol

This section summarizes depart ures from the meteorological modeling protocol (see Appendix 4) that were not described previously. The protocol mentioned the possibility of performing 4

- km simulations. Time constraints did not allow for this. A cumulus parameterization (Kuo scheme fo r all episodes except the July 1991 episode; alternate scheme for the July 1991 episode) was used on all grids whereas the protocol indicated the possibility of not using a cumulus parameterization on the 12

- km grid. Without using a cumulus parameterizati

on on the 12

- km grid it would have been difficult to achieve any significant precipitation for

the warm

- season cases. Time constraints did not allow for the comparison of model cloud fields with satellite data. A comparison with the Pennsylvania State Un iversity

- National Center for Atmospheric Research (PSU

- NCAR) mesoscale model, version 5, (MM5) was performed for the April 1994 episode. Neither MM5 or RAMS was capable of a realistic precipitation forecast over the Appalachians so that episode was repl

aced by the July 1991 episode. Time constraints also prevented the calculation of Lagrangian particle model trajectories as mentioned in the protocol.

2.6 RAMSIN Selections

### 31

The file which provides the choices for various RAMS model options is called the RAMSIN file. Table 2

- 2 gives a summary of the main RAMSIN variables which are either not a function of the nested grid or which were constant across the grids. Unless stated otherwise the values mentioned were used for all the episodes. The variable ITM DIFF controls the type of time differencing used and was set to 3 which uses leapfrog time differencing for the velocity components and pressure and uses forward time differencing for

all other variables. The variable NONHYD was set to 1 which activated t he nonhydrostatic mode. The frequency at which the radiation tendencies were updated is controlled by the variable RADFRQ and was set to 1200 s. The frequency at which the convective tendencies were updated is controlled by the variable CONFRQ and was se t to 1200 s for all the episodes except the July 1991 case where it was set to 1800 s. The basic diffusion scheme is controlled by the IDIFFK variable and it was set to 1. This choice makes the horizontal diffusion a function of the gradients of the hori zontal wind components; the horizontal grid size; and the variables CSX, XKHKM, and AKMIN. It also makes the vertical diffusion calculated by a turbulent kinetic energy scheme according to Mellor and Yamada (1982). The variable CSX was set to 0.3

and XKH KM was set to 1.0 for all grids and simulations except the July 1995 episode where it was to 3.0. The horizontal momentum diffusion coefficients for variables other than momentum are obtained by multiplying the horizontal momentum diffusion coefficients b y the factor XKHKM. If a simulation remains stable then in general one desires to use a minimum amount of diffusion and therefore XKHKM was reduced for the other episodes. The variable AKMIN sets a minimum value for the diffusion and is in a

later table. The variable NLEVEL controls the degree to which water in its various phases effects several calculations. Setting NLEVEL=3 allows the maximum influence. The variables IRAIN, IPRIS, ISNOW, IAGGR, IGRAUP, and IHAIL control the activation of the following condensate regimes, respectively: rain, pristine ice, snow, ice aggregates, graupel, and hail. Only rain was considered in the SAMI simulations so it was the only one with a non

- zero value. The variable ISWRTYP was set to 1 which activated the Chen

and Cotton (1983) shortwave radiation parameterization which includes the effects of clouds. The variable ILWRTYP was set to 1 which activated the Chen and Cotton (1983) longwave radiation parameterization

that also includes the effects of clouds.

Table 2

- 2 Summary of main RAMSIN variables which are either not a function of the nested grid or which were constant across the grids.

RAMSIN Variable Name Value

ITMDIFF 3

NONHYD 1 RADFRQ 1200 s CONFRQ 1200 s (July 91 used 1800 s)

IDIFFK 1 (all grids)

CSX 0.3

XKHKM 1.0 (all grids) (July 95 used 3.0 for all grids)

NLEVEL 3

IRAIN 1

IPRIS 0

ISNOW 0

IAGGR 0

IGRAUP 0

IHAIL 0

### 32 ISWRTYP 1

ILWRTYP 1

Table 2

- 3 Summary of main RAMSIN variables which are a function of the nested grid for the July 1995 ep isode

. RAMSIN Variable Name 48

- km grid 24

- km grid 12

- km grid

TIMESTEP 80 40 20

TNUDLAT 3,600 s 3,600 s 3,600 s

TNUDCENT 10,800 s 10,800 s 10,800 s

TNUDTOP 600 s 600 s 600 s

ZNUDTOP 12 km 12 km 12 km

NUDLAT 15 15 15

AKMIN 1.5 1.5 1.5



### Table 2

- 3 giv

es a summary of the main RAMSIN variables which are a function of the nested grid for the July 1995 episode. As discussed earlier the nudging time scales were held constant across the grids for this episode. The AKMIN parameter was also held constant at 1.5 across all the grids.

Table 2

- 4 gives a summary of the main RAMSIN variables which are a function of the nested grid for all the other episodes. The nudging time scales below a height of 12 km varied in the following manner: for the

96

- km grid the t

ime scale increased from 1 h at the outer boundary to 3 h at the 15 th 

### row/column inward,

for the 24

- km grid the time scale increased from 3 h at the outer boundary to 24 h at the 19 th 

### row/column inward, and for the 12

- km grid the time scale was held consta nt at 24 h which essentially amounts to little or nudging. The top nudging time scale was held constant at a value of 10 min across all the grids. The AKMIN diffusion parameter increased from 0.5 for the 96

- km grid to 1.0 for the 12

- km grid.

Table 2

- 4 Summary of main RAMSIN variables which are a function of the nested grid for all episodes other than the July 1995 episode.

RAMSIN Variable Name 96

- km grid 24

- km grid 12

- km grid

TIMESTEP 120 s 60 s 30 s

TNUDLAT 3,600 s 10,800 s 86,400 s

TNUDCENT 10,8 00 s 86,400 s 86,400 s

TNUDTOP 600 s 600 s 600 s

ZNUDTOP 12 km 12 km 12 km

NUDLAT 15 19 19

AKMIN 0.5 0.8 1.0

### 33

3.0 OVERVIEW OF RAMS SIMULATION PROCESS

3.1 NCEP/NCAR Reanalyis Data

Figure 3

- 1 provides a description of the major data sets and progra ms which are required for a SAMI

RAMS simulation. In section 2.4.1 the various data sets used to define surface con dition s were discussed. Here the discussion will focus on the sources and preparation of the atmospheric data for the RAMS model. The only source for atmospheric three

- dimensional variables was the NCEP/NCAR reanalysis data (Kalnay et al. 1996) (

http://dss.ucar.edu/pub/reanalysis/ ). The reanalysis data set is the result of a large

effort to analyze global data back to 1957 with a fixed state

- of

- the

- art analysis/forecast system. Part of the effort was a comprehensive qua lity control scheme that screened all observations used by the system. Therefore, no further checks of this kind were made for the SAMI simulations. The reanalysis data variables are a mixture of model and observations, with observations dominating in those areas where data is more plentiful. Reanalysis fields are classified as A

- D according to the following: A) variables w hich are dominated by observations (e.g., upper

- air temperatures and winds); B) variables which are affected by observations but also are significantly influenced by the model (e.g., upper

- air humidity and surface temperature); C) variables which are only model dependent; and D) variables which are only climatological values. The nudging scheme described in section 2.4.2.4 used the class A variables of the upper

- air winds, temperatures, and pressure and the class B variable of the water vapor specific humi dity. Nudging with respect to the specific humidity was the strongest over the coarse grid and was essentially zero for the fine grid (12

- km) except for levels above 13 km. The latter choice was made in light of the early simulations of the April 1994 epi

sode which attempted to nudge strongly toward the reanalysis values above the PBL on all grids and produced model values of clouds and precipitation which were too small.

3.2 Preparation of RAMS Input and Nudging Files

In Figure 3

- 1 the surface grid defi nitions files and the NCAR/NCEP reanalysis data are input into the RWGRIB program which transforms the reanalysis data to the RAMS grid and makes other adjustments to make the data compatible with the RAMS model. Details of this procedure are given in the SAMI meteorological modeling protocol (see Appendix 4). The bulk of the computation time at this step is the adjustment of the divergent component of the horizontal wind to insure a near

- zero vertical velocity at the model top. The output from RWGRIB is composed of three

- dimensional fields of horizontal wind components, water vapor mixing ratio, potential temperature, and a scaled pressure (the Exner function) every 6 h for the episode period for the coarse grid. The first RWGRIB file output is used by RAMS to initialize all the nested grids whereas all the other times are used for nudging as described in this section and

in section 2.4.2.4. RWGRIB files for the 24

- and 12

- km grids are obtained by interpolation done by the RAMS model internally.

An ind ependent run of RWGRIB uses the hourly surface observations obtained from NCAR to create analyses on the coarse grid of the near

- surface values of temperature, water vapor, and wind speed. The NCAR data set used for this was DS472.0 which is the hourly ai rways surface data ( http://dss.ucar.edu/datasets/ds472.0/ ). Again details of this procedure are given in the SAMI

### 34

meteorological modeling protocol (see Appendix 4) but the basic method is a Barnes (1973 ) analysis scheme as implemented by Koch et al. (1983). These analyses are used in two ways. The first is the surface nudging scheme described in section 2.4.2.5. The required surface nudging fields on the 24

- km and

12

- km grids were obtained by bilinear interpolation from the 96

- km analysis. The second is to verify the model coarse grid results and will be used in section 6 for the various episodes. Limited quality control was performed on this data. Each station variable was compared against its neig hboring station values and not included in the analysis if it exceeded specified criteria.

Once the initial, nudging, and surface analysis fields are produced by RWGRIB a RAMS model simulation can be started. After completion of a RAMS simulation the re levant data are sent to the Air Quality Modeling phase.

(RAMS) terrain {10 min or 30 sec data} land percentage {10 min data} vegetation type {30 sec data}

[PSU-ESSC] soil type {1 km data} vegetation fraction {1 km data}

[NCEP/NCAR] Reanalysis Data Sea-Surface Temperatures {1.875 degree data}

[GEWEX] leaf-area index {1 degree data} vegetation type {1 degree data} soil type {1 degree data} Surface Definition
Files

(RWGRIB)
Create Initial and Nudging Files
for RAMS [NCEP/NCAR] Reanalysis Data Atmospheric Data {1.875 degree data}

(RAMS) Create Atmospheric
Files Air Quality Modeling [NCAR] hourly surface
observations

Figure 3

- 1. Flow diagram of major data sets and programs for a RAMS simulation for a SAMI episode. Names inside [ ] represent data sources, names inside ( ) represent computer programs, and names inside { }
represent the horizontal resolution of the original data source. See the text for additional 

### details.

### 35

4.0 THE SAMI MODELING EPISODES

4.1 SAMI Episode Selection

This section provides an overview of the episode selection process. Additional details are described in Appendices 1 and 2. SAMI’s integrated assessment is focusing on the impacts of ozone, visibility, and acid deposition on the natural resources in the Southern Appalachian mountains. The impacts of these pollutants are generally measured on an annual or seasonal time scale. Theref ore, model predicted changes in pollutants need to be estimated on an annual and/or seasonal basis.

Ideally, several years of meteorology would be modeled to simulate the variability in meteorological influences on air quality over a long time period. A presumably robust response to future control strategies could be predicted by examining the average predicted change in annual and/or seasonal air quality metrics derived from modeling multiple years.

Due to the ambitious SAMI goal of modeling multiple pollutants in a “one atmosphere” approach, SAMI chose to use a sophisticated 3 dimensional Eulerian air quality model (the Urban

- Regional Multi

- scale model) to simultaneously model ozone, visibility, and acid deposition. The Urban

- Regional Multiscale

mod el ( URM

- 1ATM ) treats gaseous, aerosol, and precipitation chemistry. Computer limitations (even with the fastest modern CPUs available) forced SAMI to limit the number of potential modeling days to approximately 50

- 100.

Therefore, it became necessary to model a number of discreet episodes in order to approximate a full year or season. The challenge was to determine how to model the seasonal and/or annual metrics for 3 pollutants for the SAMI area with a limited set of modeling days. A contractor , SAI, I nc., develop ed software to aid in the selection of the SAMI episodes.

The objective of the episode selection process was to identify multi

- day episodes t hat could represent the range of effects of emissions controls on air quality values. An attempt was made to optimize the selection process across seasonal ozone, annual visibility, and annual acid deposition, fo r two Class I areas: Great Smoky Mountains National Park (GSM) and Shenandoah National Park (SNP). These two Parks were used to represent all o f the Class I areas in the SAMI region. Optimizing across two Class I areas (rather than a larger number) greatly simplified the episode selection process. GSM and SNP were chosen because they contained the most complete ambient data sets, they are spati ally representative of the southern and northern portion of the SAMI domain respectively, and they are the most visited Class 1 areas in the SAMI region.

Historical ambient air quality and meteorological data fr om the 1991

- 1995 time period were used to cl assify each day using the Classification and Regression Tree analysis software (CART) (Brieman, 1984). More details on the use of CART and the development of the episode selection software can be found in SAI’s episode selection documentation (Deuel, 1998 ).

### 36

In the analysis, each pollutant was represented by a daily or weekly ambient air quality value. Ozone was represented by the daily W 126 

### cumulative exposure index defined as



### , ] O [ ] O [ 3 3 126 W



### f W 

# ∑

=

where [O 3 

### ] is the one hour average ozone mixing rat io and f

W 

### is a weighting function of [O 3 

### ]. The sum in the equation is done over all hours during April

- October to compute a seasonal index. For classification purposes, a daily W 126 

### (the sum being done over all hours in a single day) was calculated for e ach day during the ozone season in the 1991

- 1995 ozone data base.

Visibility was represented by the 24

- hour average sum of sulfate, nitrate, organic carbon, and soil fine

mass 1 . The mass measurements were from the IMPROVE monitoring network. Acid depo sition was represented by the weekly sum of wet deposition of sulfate, nitrate, calcium, and magnesium as measured by the NADP monitoring network.

The daily (and weekly for acid deposition) observed air quality information was divided into 4 or 5 “classes ” before being input into CART. The classes were defined from low to high observed air quality values with

C lass 1 being the lowest (or cleanest) and C lass 4 or 5 being the highest (or dirtiest). The ozone and acid deposition classifications wer e C lass 1 , 2, 3, and 4 defined as the cleanest 70%, 20%, 7%, and 3% of days (weeks) respectively. The visibility classification was C lass 1, 2, 3, 4, and 5 defined as the cleanest 30%, 20%, 20%, 17%, and 3% of d ays respectively.

Air quality data were combined wi th observed meteorological data within CART to allow days to be segregated into “bins”. Bins represent similar patterns of meteorology within similar ranges of observed air quality values. CART was run for each pollutant for both Class I areas creating s ix sets of bins.

The following air quality and meteorological data was used as CART input:

Table 4

- 1. Air Quality Variables (used only for ozone)

1. Maximum observed ozone concentration in the Baltimore/Washington area

2. Maximum observed ozone concentrati on in the region between the GSM and SNP

3. Maximum observed ozone concentration in the Knoxville/Chattanooga area

4. Maximum observed ozone concentration in the Atlanta area

5. Previous day’s maximum observed ozone concentration in the Baltimore/Washington area



### 6. Previous day’s maximum observed ozone concentration in the region between the GSM and 

### SNP

1 . The CART analysis was run for both annual visibility and “summer” visibility (June, July, and August). During the episode selection process SAMI decided to limit the visibility analysis to calculation of the annual average only. Therefore, all episode selection results presented in this document are for the annual average. It is possible to use the selected episode days to recalculate the summer average visibility using the episode selection software. But the errors associated with the summer average visibility would be relatively large since only 4 out of 9 episodes occur durin g the

summer.

### 37

7. Previous day’s maximum observed ozone concentration in the Knoxville/Chattanooga area

8. Previous day’s maximum observed ozone concentration in the Atlanta area

T able 4

- 2. Surface Meteorological Variables for Ozone and Visibility

1. Average surface wind direction from 0000

- 0700 LST. The measured values were used for the averaging, and the final vector direction was cast into one of five bins, one representing calm and four centered on N, S, E, and W.

2. Ibid., but from 0800

- 1500 LST

3. Ibid., but from 1600

- 2300 LST

4. Average surface wind speed from 0000 to 2300 LST

5. Cumulative surface precipitation measured from 0000

- 2300 LST.

6. Average solar radiation from 0800

- 1500 LS T

7. Average dew point temperature depression 2 from 0000

- 2300 LST (Look Rock 3 only)

8. Average relative humidity from 0000

- 2300 LST (Big Meadows 4 only)

9. Maximum surface temperature

10. Minimum surface temperature

Table 4

- 3. Surface Meteorological Variables f or Acid Deposition

1. Cumulative surface precipitation measured from 1200 LST on day one of the measurement period to 1200 LST on day eight of the measurement period

2. eight

- dimensional array variable indicating whether precipitation occurred for each day of the measurement period (this variable will consist of a series of ones and zeros, with one indicating that precipitation occurred, for example, 11001101)

3. Average (over days and time) solar radiation from 0800

- 1100 LST

4. Ibid., but from 1200

- 2300 LST

5. Avera ge (over days and time) dew point temperature depression from 0000

- 1100 LST (Look Rock only)

6. Average (over days and time) dew point temperature depression from 1200

- 2300 LST (Look Rock only)

7. Average (over days and time) relative humidity from 0000

- 1100 L ST (Big Meadows only)

8. Average (over days and time) relative humidity from 1200

- 2300 LST (Big Meadows only)

9. Average (over days) maximum surface temperature (treated as pm for averaging)

10. Average (over days) minimum surface temperature (treated as am for a veraging)

2 . Dew point depression is defined as the difference between the ambient temperature and the dew point temperature (a measure of the moisture content of the air). A large dew point depression indicates that the air is relatively dry; a small dew point depression indicates that the air is relatively moist.

3 . Look Rock data is from Great Smokies

4 . Big Meadows data is from Shenandoah

### 38

### 39

Table 4

- 4. Upper Air Meteorological Variables for Ozone and Visibility

1. Wind direction bin at the 850 mb pressure level from the morning sounding for each individual site. Wind directions were cast into five bins, one representing calm and four centered on N, S, E, and W.

2. Ibid., but from the afternoon sounding

3. wind speed at the 850 mb pressure level, from the morning sounding, for each individual site

4. Ibid., but from the afternoon sounding

5. Average height of the morning 850 mb pressure le vel over all sites

6. Ibid., but from the afternoon sounding

7. Average temperature at the 850 mb pressure level, from the morning sounding over all sites

8. Ibid., but from the afternoon sounding

9. Same variables as above, but for the 700 mb pressure level

Ta ble 4

- 5. Upper Air Meteorological Variables for Acid Deposition

1. Same upper air variables as for ozone and visibility except the morning and afternoon sounding variables were averaged over a seven day period



### CART defines the best way to sort the days w ithin each pollutant class, given observed meteorological and air quality variables, to explain the variation in observed air quality data. The following number of bins were identified by CART:

Table 4

- 6. Summary of CART bins

Shenandoah Great Smokies

Seasonal Ozone 32 32

Annual Visibility 29 26

Annual

Acid Deposition 12 19



### 4.1.2 Important Meteorological and Air Quality Variables

For each pollutant and for each park, certain variables were important i n distinguishing between bins. For

ozone at GS M, the most important variables were relative humidity, morning wind speed at 850 mb at Greensboro, evening 700 mb height at Athens, and maximum observed ozone between GSM and SNP.

For ozone at SNP, the most important variables were relative humidity, eve ning 700 mb temperature at Huntington, incoming solar radiation, average surface wind speed, and maximum observed ozone between SNP and GSM and in the Baltimore/Washington area.

For annual visibility at GSM, the most important variables were maximum surfa ce temperature, morning 700 mb wind speed at Athens, morning 700mb wind direction at Nashville, 700mb

### 40

temperature at Nashville, precipitation at GSM, morning 850 mb wind speed at Athens, and evening 850 mb height at Greensboro.

For annual visibility at SNP, the most important variables were minimum and maximum surface temperature, morning 700 mb temperature at Dulles, precipitation at SNP, morning 700 mb wind speed at Huntington, and morning 700 mb height at Greensboro.

For acid (wet) deposition at GSM, the most important variables were precipitation at GSM, average maximum temperature, solar radiation, 700 mb height at Greensboro, and dew point temperature.

For acid (wet) deposition at SNP, the most important variables were precipitation at SNP, mornin g 700 mb wind direction at Greensboro, evening 850 mb wind speed at Dulles, and the number of days of measurable precipitation (during the 8 day period).

4.1.3 CART Performance

The accuracy of the CART process can be examined by looking at the number of “misclassified days” in each class for each pollutant and Class I area. The episode days are identified as a C lass 1, 2, 3, 4, or 5 day so that all of the C lass 1 days should be sorted into C lass 1 bins at the end of the CART process and

each of the C lass 2 days should be sorted into C lass 2 bins, etc. Days that are classified into the wrong bin (based on their observed air quality value) are termed misclassified days. A large number of misclassif i ed days may indicate that more independent variables were needed in order to differentiate between days. It may also indicate that the relationship between the observed air quality value and the observed meteorology may be weak.

The CART results indicate that the number of misclassified ozone and acid deposi tion days (weeks) are relatively small. The number of misclassified visibility days is much larger. This is likely due to the difference in the definition of the visibility classes as compared to the other pollutants. The software had a particularly

dif ficult time distinguishing the C lass 1, 2, and 3 visibility days from each other. Performance for the C lass 4 and 5 days was much better.

Details of the classification statistics and a more complete description of the meteorological variables that

were i mportant in the classification can be found in the SAI report (SAI, 1998).

4.1.4 Episode Selection Software

The outputs from the CART software analysis became the basis of the episode selection. CART sorted all of the days into classes and bins (by poll utant and air quality “

Class I ” area). At this point, some combination of days could be selected to represent the seasonal and annual air quality metrics. The class definitions could be used to ensure that a variety of air quality values were selected an d the bin definitions could be used to ensure that a variety of meteorological regimes were represented. But it would be nearly impossible to hand select days for each pollutant and Class I area in an effort to replicate each of the annual

and seasonal me trics.

### 41

Therefore, software was developed to aid in the selection of episode days. The software automatically selects a set of days given certain user defined inputs. The software attempts to optimize across the pollutants and Class I areas in an effort to minimize the errors associated with using a set of episode days to represent annual or seasonal air quality metrics.

4.1.5 Episode Software “Theory”

The following is the basic theory behind the episode selection software. Each bin contains a certain number of days and each day has an observed air quality value. Therefore, if you add up the value of all the days for all bins for each pollutant/Class I area, it should equal the annual or seasonal metric. Additionally, if you multiply the mean value of

the population in each bin by the number of days in each bin and then sum the total, you will arrive at the same annual or seasonal metric.

This calculation provides a basis for the episode selection. If a single day was selected from each CART

bin an d that day had a value exactly equal to the mean value of the bin, then the annual or seasonal metric could easily be replicated (assuming the number of days in each bin is known). Additionally, this would ensure that each meteorological/air quality regim e would be represented by at least one episode day.

Table 4

- 7 shows a simple mathematical example. Let’s assume that for a single pollutant at a single Class I area there are 2 classes and 2 bins in each class. There are a total of 40 observation days w ith a total annual metric of 210. The “weight” of each bin is calculated as the number of days times the observed daily values in the bin. If an episode day was selected from C lass 1, bin 1 and that day had a value of 3, then the value could be multiplie d by the number of days in the bin to get a total weight of 21. This represents 10% of the total annual weight. So in this case, the single episode day would represent 10% of the annual metric. Note that this takes into account both the magnitude of the ambient value and the frequency of occurrence. If one day was selected from each of the 4 bins and each day had a value exactly equal to the mean of each bin, then the annual metric could be exactly recalculated. Of course, in most cases, selected episo de days will not have a value exactly equal to the mean value of the bin. This is a potential source of “error” in the episode selection process.

Table 4

- 7

Class Bin Number Days Mean Value Weight (days X mean) % of Annual Weight

Class 1 Bin 1 7 3 21 1 0.0

Bin 2 13 8 104 49.5

Class 2 Bin 1 15 2 30 14.3

Bin 2 5 11 55 26.2

Total 40 210 100%



### The above example assumes that at least one episode day is chosen from each bin. Unfortunately, this was not possible in the SAMI episode selection process. There are a total of 150 bins between the 3 pollutants and 2 Parks. There are too many bins to be able to select a single day from each one. Additionally, the collection frequency of the ambient air quality data adds to the difficulty of episode selectio n. The ambient ozone data is available on a daily basis. But the IMPROVE visibility data are only collected on

### 42

Wednesdays and Saturdays. This means that even if a Wednesday and Saturday were selected from the same week, all of the days in between need t o be modeled to form a continuous episode. Modeling a full week only yields two valid visibility days. The acid deposition data are collected as a total weekly sample. Modeling a single acid deposition “day” actually means modeling an entire 8 day perio d (Tuesday

Tuesday). Also, it was recommended by SAI to exclude “misclassified” days from consideration as episode days. This also made it more difficult to string together continuous episode periods. With all of these factors, modeling at least a singl e day from each bin may have entailed modeling as many as 300

- 400 actual modeling days.

Since the approximate number of modeling days needed to be limited to ~50

- 100 days, sacrifices had to be made in selecting episode days. Not all bins could be represe nted. This was taken into account when the episode software was designed. The software established rules for selecting bins and also contained scaling factors to account for errors and biases introduced by not representing all episode bins and/or classes and by not selecting misclassified days.

4.1.6 Episode Software Rules

If episode days were not selected from all bins and/or classes then the order in which days were selected becomes important. There needs to be a way to select from the “most important ” bins first. Also, since the episode selection was optimized across the 3 pollutants and 2 parks, a pollutant order and a park order

needed to be established.

The “importance” of the bins was established by using the previously defined “weight” of each b in (weight is equal to the number of days times the ambient value). Within each park for each pollutant, the bins were ordered by their total weight. This ensures that days with a high frequency of occurrence and/or a high magnitude are selected first. In theory, the bins that are not used in the episode selection should be bins with low weights that contribute a relatively small portion of the annual and/or seasonal metrics. This episode selection rule was hardwired into the software.

Another selectio n rule was that a day/week had to be selected for each park/metric combination prior to a second day being selected for any park/metric combination. There were 3 metrics and 2 parks, so six days/weeks would be selected before each park/metric combination was repeated. The order of the park/metric selection is chosen by the user in the episode selection input file.

4.1.7 Episode Selection Error Terms

All days must be modeled in order to represent an entire five

- year data period without introducing non

m odel errors or biases. However, this scope of modeling was not possible. Therefore, an attempt was

made to minimize non

- model errors caused by modeling only a subset of days.

There is variability within each pollutant class identified by CART. Non

- mod el error occurs in part because most days chosen to represent a class of days do not contribute to the seasonal or annual metric for a given pollutant category in a manner identical to all days in a class. The exception is the unlikely occurrence of a

### 43

day on which the pollutant metric (for example, W 126 

### ) equals the average daily metric across all days in its class. The extent to which a subset of days fails to replicate conditions across all days is one component of

non

- model error associated with episodi c modeling.

4.1.8 Biased (Unscaled) Error

Another component of non

- model error is due to the inability of the episodic days that are selected to represent all CART class bins. Within the episode software, the error associated with not selecting days

fro m all bins is termed “biased error” or “unscaled error”. This error term is based on the amount of weight represented by the selected episode days. The biased error is equal to the amount of weight that is not represented by the selected episode days. 5 

### Since not selecting all bins always results in an underestimate of annual/seasonal weight, the biased error term is always negative. For example, the biased er ror for ozone at

Shenandoah is

- 16.6%. This means that the selected episode days represent 83.4 % of the total seasonal ozone weight. In the SAMI episode

selection the biased errors ranged from

– 16.6% to

- 55.2%. In all cases, the primary goal in the episode selection was to minimize the biased errors for all pollutants and parks.

4.1.9 Distan

ce (Scaled) Error

The episode selection software attempts to minimize the error in day selection by creating several “scaling factors” which try to account for unrepresented bins, classes and misclassified days. The complete equation

to calculate the ann

ual and/or seasonal metrics is equation 4

- 1 in the episode software documentation. The documentation also contains a complete description of equation 4

- 1.

where:

A

r 

### is the value of the metric due to all selected, representative days

S is the number of se asons or years included in the data set

N is the total number of days included in the data set

P j 

### is the number of days in bin j ( P j 

### = n j 

### if the user specifies that all days can be used to represent

5 . The biased error is also affected by how close the selected days are to the mean value of their respective b ins. The difference between the mean and the selected day(s) is part of the unbiased error calculation. Errors associated with selected days with values higher than the bin mean can be canceled out by days with values lower than the bin mean.



# ∑ ∑ ∑ ∑ ∑
∑

= = =



#       

      

   

    

              

    

 

    

 

   

 

   

 

= nclass

k l m

j jl

l l

l l

l l b

j j

k k r

k k l

k

k k

k k

k k k



# a

m n

WP WA

P P

P N

S A

1 1 1

d represente not class a 

# 1

### 44

the bin; when only properly classified days are used, th ey may differ).

nclass is the number of distinct values of the classification variable

b k 

### is the number of bins with predicted value k of the classification variable

WA j 

### is the weight for bin j using all days in bin j

WP j 

### is the weight for bin j using only the properly classified days, or all days, in bin j, depending on the user’s specification

l k 

### runs over all bins with the classification value k , from which a representative day was chosen

n j 

### is the number of properly classified days, or all days, in bin j , depending on the user’s specification

m j 

### is the number of representative days chosen from bin j

a jk 

### is the observation

- based value of the metric for representative day j in bin k

Within equation 4

- 1 are several scaling factors which attempt to reduce the error associated with not selecting all bins. There are three general scaling factors:

The WA/WP portion of the equation is a scaling factor to account for misclassified days. In the episode selection, SAMI chose to allow the selection of only prope rly classified days as episode days. The scaling factor accounts for the number of unused misclassified days.

The P j 

### /P l 

### portion of the equation is a scaling factor to account for unselected bins within each class. It is

assumed that all bins within a c lass have a similar mean value. Therefore, the weight of the selected bins is used to approximate the weight from the unselected bins. There is some uncertainty in this assumption since the mean values between bins in the same class can differ (although not by a large amount).

The N/P k 

### portion of the equation is a scaling factor to account for classes not selected. This would only be

used if no bins within a class were selected 6 

### . The factor uses the values of the days selected to account for

the bins a nd classes that were not selected. There is a large amount of uncertainty in this assumption. For example, the values of a C lass 1 day should not be used to approximate values from a C lass 4 bin. If the missing class is a high or low class, it is probab ly a poor assumption to adjust the values based on bins from other classes (it may be reasonable to scale for C lass 2 based on C lass 1 or 3 days.)

Equation 4

- 1 performs the scaling of the results and calculates a final “distance” or “scaled” error. In m ost cases this error term is < 10%. But since the scaled error has a number of assumptions built into it, the unscaled error was used as the primary judgment of episode selection error. The scaled error was used as a secondary measure.

Using equation 4

- 1, the episode selection software outputs all of the information necessary to recalculate the annual and/or seasonal metrics from the selected episode days.

6 In tes ting of the software, it was found that the largest source of error (creating a large scaling factor) was not selecting at least one bin from each class for each pollutant. It was made a priority to always choose at least one day
for each class. This was accomplished for all pollutants except dry deposition.

### 45

4.1.10 Reproducing the Annual and Seasonal Metrics

Selecting the episode days to be modeled wa s an iterative process that took more than a year to complete. This process occurred in parallel with the early stages of the actual modeling. In the end, nine episodes were selected to represent the range of conditions for ozone, visibility and acid dep osition at Great Smoky Mountains and Shenandoah National Parks. Details of the selection process are described in Appendix 2.

The episode selection software tool that was a critical component in the selection process outputs the necessary information to reproduce the annual and seasonal metrics from the selected episode days 7



### . The following series of tables contain the scaling factors and other information needed to recreate the annual totals for acid deposition and visibility and the seasonal total for o zone.

The following is an explanation of the variables used to reconstruct the annual/seasonal metrics:

Table 4

- 8

. Variable definitions

Date Date of the first day of each acid deposition week or the actual episode days for ozone and

visibility. The CART bin classification for each day/week

1/S*N/P(j) 1 divided by the number of years (or seasons) of air quality data used

Class The class that each day/week was assigned to. B (Class) A scaling factor which accounts for bins and/or classes not se lected. A separate “B” scaling factor is applied to all selected days within the same class.

WA/WP A scaling factor which accounts for misclassified days within a bin. A separate WA/WP scaling

factor is applied to all selected days within the same bin. n(j)/m(j) The number of days in the bin. n(j) is the number of days in the bin selected. This number is

divided by the number of days selected from the same bin (m(j)). For example, if 2 days from the same bin are selected, then each day will be weight ed by half of the number of days in the bin. This avoids double counting the weights. Deposition/

PM2.5/

W126 The observed ambient value of wet deposition, dry deposition, PM2.5, or ozone on the selected

day/week. The units are kg/ha/week, ug/m3, ng/m3, and W126, respectively.

Contribution (absolute) This is the absolute contribution to the annual or seasonal total. This value is calculated by the

formula: [ 1/S*N/P(j)] * [B(Class] * [WA/WP] * [n(j)/m(j)] * [Deposition/PM2.5/W126]. The

absolute contrib ution is weighted by both the ambient concentrations and the frequency of occurrence. The absolute contribution attributable to each episode day represents a portion of
the annual or seasonal metric.

Contribution (%) This is the same as the absolute cont ribution except normalized to 100%. The percent contribution

of each day/week represents the percentage that each day/week contributes to the annual or seasonal total. For example, an ozone day with a contribution percentage of 4.2% means that the
observ ed W126 on that day represents 4.2% of the seasonal total W126. (It does not represent 4.2% of the days in a season.)



### 7 The episode software only calculates the metrics for GSM and SNP. Interpolation methods need to be applied to

calculate the metrics for the other SAMI class 1 areas. Different methods will be use d for each pollutant.

### 46

Table 4

- 9. Episodic days selected for GSM wet acid deposition modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) Deposition (kg/ha/week) Contribution

(absolute) Contribution

(%)

910723 19 0.2 4 1.6 1 5 5.49 8.78 23.2

920623 15 0.2 2 2.023 1.122 17 0.89 6.87 18.1

930323 4 0.2 2 2.023 1.186 8 1.13 4.34 11.4

930511 12 0.2 3 1.846 1.268 4 1.64 3.07 8.1

930803 12 0.2 3 1.846 1.268 4 1.89 3.54 9.3

940208 4 0.2 2 2.023 1.186 8 1.17 4.49 11.8

950425 7 0.2 1 1.136 1.081 25 0.69 4.24 11.2

950523 1 0.2 1 1.136 1.093 61 0.17 2.58 6.8

Total 37.90 100.0

Table 4

- 10. Episodic days selected for SNP wet acid depositi on modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) Deposition (kg/ha/week) Contribution

(absolute) Contribution

(%)

910723 12 0.2 4 1.286 1.13 2.5 5.12 3.72 13.1

920623 1 0.2 1 1.474 1 38 0.21 2.35 8.3

930323 6 0.2 3 1.286 1.403 14 1 .97 9.95 35.0

930511 12 0.2 4 1.286 1.13 2.5 2.84 2.06 7.3

940208 8 0.2 2 1.186 1.619 4.5 1.24 2.14 7.5

950425 3 0.2 2 1.186 1.541 11 1.02 4.10 14.4

950523 8 0.2 2 1.186 1.619 4.5 1.41 2.44 8.6

950711 1 0.2 1 1.474 1 38 0.15 1.68 5.9

Total 28. 45 100.0

Table 4

- 11

. Episodic days used for GSM dry acid deposition modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) Deposition (ug/m3) Contribution

(absolute) Contribution

(%)

910723 16 0.2 1 1.314 1.019 19.75 3.62 19.1 10.8

920623 16 0.2 1 1.314 1.019 19.75 7.16 37.9 21.3

930323 7 0.2 1 1.314 1.068 12 6.73 22.7 12.8

930511 5 0.2 2 5.667 1 2 9.65 21.9 12.3

930803 16 0.2 1 1.314 1.019 19.75 6.02 31.8 17.9

940208 7 0.2 1 1.314 1.068 12 3.41 11.5 6.5

950523 16 0.2 1 1.314 1.019 19 .75 6.17 32.6 18.4

950711 21 0.2 2 5.667 1.135 6 8.14 62.8 35.4

Total 177.5 100.0

### 47

Table 4

- 12

. Episodic days used for SNP dry acid deposition modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) Deposition (ug/m3) Contribution

( absolute) Contribution

(%)

910723 17 0.2 1 1.171 1.273 19 8.97 50.8 12.7

930323 17 0.2 1 1.171 1.273 19 9.58 54.3 13.6

930803 17 0.2 1 1.171 1.273 19 13.55 76.8 19.2

940208 11 0.2 1 1.171 1.087 18 13.42 61.5 15.4

950425 17 0.2 1 1.171 1.273 19 6.31 35.7 9.0

950523 17 0.2 1 1.171 1.273 19 7.49 42.4 10.6

950711 17 0.2 1 1.171 1.273 19 13.68 77.5 19.4

Total 399.0 100.0

Table 4

- 13

. Episodic days selected for GSM visibility modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n (j)/m(j) PM 2.5 (ng/m3) Contribution

(absolute) Contribution

(%)

910727 22 0.2 5 1.667 1.031 3.5 26605 32007.8 3.6

910731 18 0.2 5 1.667 1 1 31824 10610.1 1.2

920624 16 0.2 4 1.383 1.404 9 20918 73110.9 8.3

930324 7 0.2 2 1.223 1.872 50 8223 188262.0 2 1.3

930327 1 0.2 1 1.263 2.743 13 4505 40578.6 4.6

930515 15 0.2 3 1.311 1.445 16 15464 93743.8 10.6

930804 17 0.2 3 1.311 1.521 10 10029 39996.3 4.5

930807 17 0.2 3 1.311 1.521 10 9338 37240.5 4.2

930811 16 0.2 4 1.383 1.404 9 19024 66491.1 7.5

9402 09 9 0.2 1 1.263 1.904 27 3919 50890.8 5.7

950426 4 0.2 2 1.223 1.156 11 6538 20335.4 2.3

950429 6 0.2 3 1.311 1.894 16 9766 77597.8 8.8

950527 16 0.2 4 1.383 1.404 9 17516 61220.5 6.9

950712 16 0.2 4 1.383 1.404 9 17785 62160.7 7.0

950715 22 0.2 5 1. 667 1.031 3.5 25882 31138.0 3.5

Total 885384.1 100.0

### 48

Table 4

- 14

. Episodic days selected for SNP visibility modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) PM 2.5 (ng/m3) Contribution

(absolute) Contribution

(%)

910724 29 0 .2 4 1.215 1.157 8 16610 37359.3 3.7

910731 24 0.2 4 1.215 1.613 6 23289 54770.0 5.5

920624 25 0.2 4 1.215 1 3.5 16664 14172.7 1.4

920627 25 0.2 4 1.215 1 3.5 18077 15374.5 1.5

930324 9 0.2 2 1.467 1 5 5580 8185.9 0.8

930331 12 0.2 1 1.9 2.805 18 3892 74672.7 7.5

930512 13 0.2 4 1.215 1.609 7 23834 65231.5 6.5

930515 14 0.2 3 1.49 1.246 13 9576 46223.4 4.6

930804 27 0.2 3 1.49 1.414 7.5 13994 44225.1 4.4

930807 13 0.2 4 1.215 1.609 7 16282 44562.4 4.5

930811 23 0.2 4 1.215 1.392 5 18647 31537.3 3. 2

940209 12 0.2 1 1.9 2.805 18 3715 71276.7 7.1

950426 1 0.2 2 1.467 1.736 21.333 5548 60283.5 6.0

950429 1 0.2 2 1.467 1.736 21.333 9031 98129.1 9.8

950503 1 0.2 2 1.467 1.736 21.333 6745 73289.8 7.3

950524 29 0.2 4 1.215 1.157 8 16746 37665.2 3.8

9 50527 7 0.2 3 1.49 1.174 10 11944 41786.3 4.2

950712 20 0.2 5 1 1.568 7 26001 57077.4 5.7

950715 20 0.2 5 1 1.568 7 38917 85430.6 8.5

950719 27 0.2 3 1.49 1.414 7.5 12226 38637.7 3.9

Total 999891.1 100.0

### 49

4

- 15

. Episodic days selected for GSM ozone modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) W126 (ppb) Contribution

(absolute) Contribution

(%)

910726 23 0.2 3 1.747 1.056 8 871.8 2573.3 3.8

910727 7 0.2 2 1.217 1.207 5.333 499.3 782.3 1.1

910728 3 0.2 1 1 1.349 30 243. 4 1970.1 2.9

910731 7 0.2 2 1.217 1.207 5.333 620.7 972.5 1.4

920624 16 0.2 4 1.178 1.081 9 1396.5 3201.0 4.7

920626 21 0.2 1 1 1.263 23 83.3 484.0 0.7

920628 25 0.2 3 1.747 1.473 5 763.8 1965.5 2.9

930511 10 0.2 2 1.217 1.162 6 638.3 1083.2 1.6

9305 13 1 0.2 1 1 1.138 94.8 89 1920.3 2.8

930515 5 0.2 2 1.217 1.623 15.75 488.6 3040.0 4.4

930516 10 0.2 2 1.217 1.162 6 551.9 936.6 1.4

930517 13 0.2 2 1.217 1.442 3 703.6 740.9 1.1

930805 5 0.2 2 1.217 1.623 15.75 385.8 2400.4 3.5

930806 1 0.2 1 1 1.13 8 94.8 104.2 2248.3 3.3

930807 3 0.2 1 1 1.349 30 290.8 2353.7 3.4

930808 2 0.2 2 1.217 1.882 7 558.2 1789.9 2.6

930809 7 0.2 2 1.217 1.207 5.333 549.6 861.1 1.3

930811 5 0.2 2 1.217 1.623 15.75 568.5 3537.1 5.2

950426 5 0.2 2 1.217 1.623 15.75 540.6 3363.5 4.9

950429 12 0.2 3 1.747 1.49 2.5 807.6 1051.1 1.5

950501 1 0.2 1 1 1.138 94.8 168.6 3637.8 5.3

950502 1 0.2 1 1 1.138 94.8 78.5 1693.8 2.5

950503 2 0.2 2 1.217 1.882 7 548.4 1758.5 2.6

950524 25 0.2 3 1.747 1.473 5 820.4 2111.2 3.1

950525 12 0.2 3 1.747 1.49 2.5 771.7 1004.4 1.5

950526 29 0.2 3 1.747 1.438 1.5 767.1 578.1 0.8

950527 2 0.2 2 1.217 1.882 7 658.4 2111.2 3.1

950529 1 0.2 1 1 1.138 94.8 87.9 1896.6 2.8

950711 30 0.2 3 1.747 1 3 1017 1066.0 1.6

950712 25 0.2 3 1.747 1.473 5 87 4.6 2250.6 3.3

950713 25 0.2 3 1.747 1.473 5 1086.4 2795.7 4.1

950714 31 0.2 4 1.178 1.281 10.5 1351.1 4281.6 6.3

950715 29 0.2 3 1.747 1.438 1.5 1012.2 762.9 1.1

950717 32 0.2 3 1.747 1.327 4 824.2 1528.6 2.2

950719 31 0.2 4 1.178 1.281 10.5 1161.2 3 679.8 5.4

Total 68431.2 100.0

### 50

Table 4

- 16

. Episodic days selected for SNP ozone modeling

Date Bin 1/S*N/P(j) Class B(Class) WA/WP n(j)/m(j) W126 (ppb) Contribution

(absolute) Contribution

(%)

910723 29 0.2 3 1.425 1.174 1 1143.1 382.5 0.6

910726 18 0.2 2 1.191 1.688 5.818 658.9 1541.4 2.3

910728 18 0.2 2 1.191 1.688 5.818 737.8 1725.9 2.6

910730 1 0.2 1 1 1.089 52.875 62.8 723.2 1.1

910731 18 0.2 2 1.191 1.688 5.818 638.5 1493.6 2.3

920624 18 0.2 2 1.191 1.688 5.818 391.5 915.8 1.4

920625 4 0.2 1 1 1.134 53 224.5 2698.6 4.1

920626 18 0.2 2 1.191 1.688 5.818 547 1279.6 1.9

920628 18 0.2 2 1.191 1.688 5.818 449 1050.4 1.6

920629 22 0.2 3 1.425 1.323 12 1022.1 4624.7 7.0

930511 29 0.2 3 1.425 1.174 1 1019.6 341.1 0.5

930512 10 0. 2 3 1.425 1.497 9.333 958.4 3816.2 5.8

930513 1 0.2 1 1 1.089 52.875 72.4 833.8 1.3

930805 4 0.2 1 1 1.134 53 70.4 846.2 1.3

930806 1 0.2 1 1 1.089 52.875 89.8 1034.2 1.6

930809 18 0.2 2 1.191 1.688 5.818 745.4 1743.7 2.6

930810 18 0.2 2 1.191 1.688 5 .818 567.9 1328.5 2.0

930811 18 0.2 2 1.191 1.688 5.818 549.3 1285.0 1.9

950427 6 0.2 4 1.205 1.477 2 1167.8 831.4 1.3

950428 2 0.2 1 1 1.452 37 250.6 2692.6 4.1

950429 3 0.2 2 1.191 1.356 23 428.7 3184.8 4.8

950501 1 0.2 1 1 1.089 52.875 65.8 757.8 1 .1

950502 1 0.2 1 1 1.089 52.875 85.6 985.8 1.5

950503 1 0.2 1 1 1.089 52.875 140.6 1619.2 2.4

950524 10 0.2 3 1.425 1.497 9.333 1121.6 4466.1 6.7

950525 18 0.2 2 1.191 1.688 5.818 620 1450.4 2.2

950528 1 0.2 1 1 1.089 52.875 72 829.2 1.3

950529 1 0. 2 1 1 1.089 52.875 58.1 669.1 1.0

950711 10 0.2 3 1.425 1.497 9.333 935.8 3726.2 5.6

950712 27 0.2 3 1.425 1.554 3 901.7 1198.1 1.8

950713 26 0.2 4 1.205 1.013 5 1162.6 1419.1 2.1

950714 30 0.2 4 1.205 1.108 13 1456.6 5056.4 7.6

950715 28 0.2 4 1.205 1.181 7 1334.7 2659.2 4.0

950717 23 0.2 2 1.191 1.379 12 527.3 2078.5 3.1

950718 18 0.2 2 1.191 1.688 5.818 430.6 1007.3 1.5

950719 15 0.2 2 1.191 1.577 19 545.2 3891.2 5.9

Total 66186.7 100.0



### 4.2 The 11

- 19 July 1995 Episode

The synoptic dis cussion for each episode will have four basic elements. These are the following: discussion of why the episode was chosen (expanded discussion of this is available in section 4), discussion of the

### 5 1

departure of the 500

- mb geopotential height from a longer term mean, discussion of the main upper

- air and surface weather features during the episode, and a discussion of the main features of the meteograms of the NWS sites closest to the Great Smoky National Park (GSM) and the Shenandoah National Park (SNP).

T he 500

- mb geopotential height anomaly plots were obtained from the National Oceanic and Atmospheric

Administration

- Cooperative Institute for Research in Environmental Sciences (NOAA

- CIRES) Climate Diagnostics Center web page (www.cdc.noaa.gov/HistData). The episode synoptic discussion utilized the NOAA publication

Daily Weather Maps. Because of the importance of the Class I areas in GSM and SNP, meteograms of the closest NWS sites to these locations were included. For GSM it was Knoxville, Tennessee (a

bout 22 km from the center of GSM), and for SNP it was Charlottesville, Virginia (about 44 km from the center of SNP). Because of missing data problems at Charlottesville, the alternate sites of Staunton, Virginia and Martinsburg, West Virginia ( 49 and 10 5 km from the center of SNP, respectively)

were also used.

The July 1995 episode was Class 1 for acid deposition at SNP. The episode contained one Class 5 visibility day and one Class 4 visibility day at GSM, and two Class 5 and one Class 3 visibility da ys at SNP. For ozone at GSM there were five Class 3 days and two Class 4 days. For ozone at SNP there were three Class 2 days, two Class 3 days, and three Class 4 days. Overall, this episode experienced periods of low visibility and high ozone. Apart fr om SAMI it is well known for a relatively short but intense heart wave which claimed around 800 lives across the country (Changnon et al. 1996; Kunkel et al. 1996)

The anomaly field (showing departures from a 15

- year mean) for the 500

- mb geopotential heig ht for this episode is shown in Figure 4

- 1. Heights were on average 30

- 45 m above the long

- term mean over portions of Midwest. This resulted in surface high pressure over the same area for much of the period which

contributed to the heat wave. The 500

- m b ridge intensified and moved eastward from the Nebraska

Kansas area on 10 July to northern Illinois by 14 July. During this period most precipitation occurred on the periphery of the ridge over the Great Lakes and coastal areas of the Atlantic and Gulf c oas ts. Temperatures warmed during this period

so that by 14 July a large portion of the Midwest ha d maximum temperatures 35 o 

### C or higher. For the period 15

- 20 July the 500

- mb ridge slowly weakened and moved southward taking up a position generally from Te xas to Georgia. At the same time a cold front moved slowly southeastward from the northern Great Plains and became stationary across the South by the end of the period. This brought cooler and less humid air to much of the eastern United States along wit h scattered precipitation.

Figures 4

- 2 and 4

- 3 contain time series of observations for Knoxville , while Figures 4

- 4 and 4

- 5 show the same for Staunton, Virginia. The Charlottesville, Virginia site had large segments of missing data for this episode. M

aximum temperatures at Knoxville warm to near 35 o 

### C on 15 July and then slowly decline thereafter due to an increase in cloud cover from scattered afternoon convection and the frontal passage on 18 July. The frontal passage is clearly indicated by the drop in the dew point temperatures and the mixing ratios. The relative humidity showed the typical maxima near saturation in the mornings and minima in the afternoons in the range of 45

- 55 %. Six

- hour precipitation amounts were only on the order of 1 mm and were recorded on 16 July and 17 July but the associated cooling can be seen in the transient dips on the temperature curves for the same times. Wind speeds at Knoxville were generally below 3

- 4 m s

- 1 

### with the

maximum value of 6 m s

- 1 

### on 15 July. Winds wer e generally calm at night. With the upper

- air ridge and

### 52

surface high pressure to the west for most of the period wind directions were generally west to northwest. The same plots for Staunton in Fig ure s 4

- 4 and 4

- 5 show similar patterns except no precipit ation was

recorded and the dew point temperature and mixing ratio showed more variability.

Figure 4

- 1. Anomaly field for the 500

- mb geopotential heights created by taking the mean value for the period 0000 UTC July 11

– 1200 UTC July 19 1995 and subtracting from it the 15

- year mean for the sa me period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 53

Figure 4

- 2. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures (

o C) at 2 m (dashed curve) at Knoxville, Tennessee from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizo ntal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee from 9 July 1995

1200 UTC t o 20 July 1995 1200 UTC. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 54 Figure 4

- 3. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 55

Figure 4.4. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve ) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Staunton, Virginia from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical

axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Staunton, Vir ginia from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the

### 56

4.3 The 9

- 18 May 1993 Episode

The May 1993 episode was categorized as Class 3 for GSM and Class 4 for SNP for the metric of acidic deposition. For visibility there was one Class 3 day at GSM and one Class 3 and Class 4 days at SNP. For ozone at GSM there was one Class 1 day and four Clas s 2 days. For ozone at SNP there was one Class 1 day and two Class 3 days. This episode was similar to the May 1995 episode in that it was chosen for a broad range of conditions including moderate to high acidic deposition, moderate ranges of visibility, and mainly low levels of ozone. Figure 4

- 5. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Staunton, Virginia from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. Wind speeds
are wi th respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

hourly observed precipitatio n amounts (mm) at Staunton, Virginia from 9 July 1995 1200 UTC to 20 July 1995 1200 UTC. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 57

The anomaly field for the 500

- mb geopotential height for this episode is shown in Fig ure 4

- 6. It shows an area of above normal heights over western Canada and the western United States. Two areas of below normal heights were observed: one over the southern Great Plains and another over southeastern Canada. The two below normal areas are reflections of two closed upper

- level low circulations which controlled much of the weather over the eastern United States for this epis ode. The episode started on 9 May with an

upper

- level trough over the Rocky Mountains and a ridge from the Great Lakes to Florida. The associated surface features included a frontal system from North Dakota to Texas and a high pressure ridge covering

the eastern third of the United States. For the period 10

- 12 May an upper

- level closed low remained over parts of Oklahoma, Kansas, Missouri, and Arkansas along with its surface low pressure center. A slow

moving cold front or trough from this surface low mo ved across parts of the southern Gulf of Mexico

coast al states during this period. At times another frontal system extended northward or northeastward from this same surface low to the Great Lakes or New England. By 13 May the upper

- level cutoff low had opened and moved to the Southeast. A new upper

- level closed low was developing southeast of Hudson Bay. One frontal system extended from Oklahoma northeastward to the mid

- Atlantic while another was located across southern Canada. For the peri od 14

- 16 Ma y the intense

upper

- level low south of Hudson Bay created fast zonal flow for the northern United States. In response the southern Canadian frontal system moved south and by 16 May was located from the mid

- Atlantic westward to a weak surface low

over the Ohio Valley and then west to Texas and then northward as a stationary front over the Rocky Mountains. This same frontal system was quasi

- stationary for the period 17

- 18 May with several waves moving along it.

Figures 4

- 7 and 4

- 8 show the time series of o bservations for Knoxville, Tennessee while Fig ure s 4

- 9 and

4

- 10 show the same for Martinsburg, West Virginia. Martinsburg was chosen as the nearest NWS site to SNP in place of Staunton and Charlottesville, Virginia which both had large periods of missing data.

Knoxville maximum temperatures at the beginning of the episode were near 26 o 

### C and then declined to near

22 o 

### C on the afternoon of 13 May in response to the clouds and precipitation from the closed upper

- level system in the Southern Plains moving ea stward. For the rest of the episode, maximum temperatures increased slowly once again to near 26 o 

### C. Dew point temperatures followed a similar pattern, starting near

20 o 

### C and decreased to 10 o 

### C on 14 May, and then increased to 20 o 

### C by 18 May. The correspon ding 2

- m mixing ratios started at 15 g kg

- 1 

### on 9 May, decreased to near 8 g kg

- 1 

### on 14 May, and then slowly increased back to 15 g kg

- 1 

### by 18 May. The 100% relative humidity values suggest morning fog which was confirmed by surface observations. The high est wind speeds were near 6 m s

- 1 

### on the afternoons of 12 and 16 May. Otherwise afternoon wind speeds were generally 2

- 4 m s

- 1 

### . Nighttime wind speeds were light or calm. Wind directions covered a broad range but were typically southwest to northwest. M easured 6

- h precipitation fell on 12

- 13 May with amounts of 2

- 18 mm and with amounts under 1 mm during 16

- 18 May.

For Martinsburg similar patterns were observed except that maximum temperatures made less of a recovery after the middle of the episode. Dew point and mixing ratio values were also smaller than Knoxville in general, and especially for late in the episode with Martinsburg being north of a stationary front. Wind speeds were somewhat faster and precipitation amounts were smaller. On 13 May 6

- h values up to 6 mm

### 58

were observed while lighter amounts of near 2 mm were observed on 16 May.

Figure 4

- 6. Anomaly field for the 500

- mb geopote ntial heights created by taking the mean value for the period 0000 UTC 09 May

- 1200 UTC 18 May 1993 and subtracting from it the 15

- year mean for the same period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 59 Figure 4

- 7. Top Pane l: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures (

o C) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. Temperatures are with respect to the left vertical axis while the de wpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mi xing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 60

Figure 4

- 8. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 1200 UT C 09 May 1993 to 1200 UTC 18 May 1993. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 61 Figure 4

- 9. Top Pa nel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. Temperatures are with respect to the left vertical axis whil e the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid

curve ) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertic al axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 62

4.4 The 21 March

- 1 April 1993 Episode

The March 1993 episode was categorized as Class 2 for GSM and Cl ass 3 for SNP for the metric of acidic deposition. For visibility there was one Class 1 and one Class 2 days for both GSM and SNP. This episode was outside the normal ozone season and ozone classes were not defined. The episode was

chosen for low to mode

rate acidic deposition and high to moderate ranges of visibility.

The anomaly field for the 500

- mb geopotential height for this episode is shown in Fig ure 4

- 11. A large area of above normal heights with values up to 150 m above normal existed over southe rn Canada and the northern United States whereas an area with below normal heights with a smaller magnitude existed over the southeastern United States. This anomaly pattern was the result of two closed upper

- level systems during the episode generally loc ated over the central United States and an active subtropical jet stream across the southern United States. The episode began on 21 March with mainly zonal upper

- level flow across the extreme northern United States and with a trough extending from the no rthern Rocky Mountains southwestward to Arizona. Surface features included a ridge of high pressure from southern Ontario southward to the Ohio River Valley. East of this ridge a low and associated fronts were moving across the Figure 4

- 10. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but ar e only labeled every other day. Bottom
Panel Six

- hourly observed precipitation amounts (mm) at Martinsburg, West Virginia from 1200 UTC 09 May 1993 to 1200 UTC 18 May 1993. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 63

eastern United States while another low pressure center and associated fronts were located over the central and southern Rocky Mountains. By 23 March the western upper

- level trough moved east and developed a closed low over Iowa. In conjunction with this the western frontal system had also moved east to the Ohio River Valley with a cold front southward to the Gulf of Mexico and a warm front across the southern Appalachians. For the period 24

- 25 March the Ohio Valley surface system moved off the mid

- Atlantic

coast with the southern

portion remaining as a stationary front along the Gulf coast. High pressure built over the Great Lakes and New England. The closed upper

- level low moved little with a position essentially over

Illinois. For 26

- 27 March the upper

- level low slowly moved to Georgia as a new upper

- level trough moved into the Southwest. A new surface system developed along the Gulf Coast stationary front and was located over Georgia on 27 March. During 28

- 30 March the eastern upper

- level low and its associated surface syst

em moved northward along the east coast and eventually offshore while the new western upper

level trough moved inland to a position near Texas by 30 March. This latter upper

- level system was also associated with a developing surface system over the southe rn Great Plains. For the remainder of the episode for the period of 31 March to 1 April the new upper level trough intensified and became a closed low over Illinois by 1 April. The associated surface low deepened and became a major storm moving from

Okla homa to Ohio and pulling first a warm front and then a cold front across most of the southern portion of the immediate SAMI region. This system produced precipitation at both GSM and SNP but with the

heavier amounts at GSM.

Figures 4

- 12 and 4

- 13 show the time series of observations for Knoxville, Tennessee while Fig ures 4

- 14

and 4

- 15 show the same for Martinsburg, West Virginia. Martinsburg was chosen as the nearest NWS site to SNP in place of Staunton and Charlottesville, Virginia which both again had l arge periods of time with missing data. Maximum temperatures at Knoxville increased to near 22 o 

### C on 25 March and then

decreased to 14

- 17 o 

### C for the period 26

- 28 March. This was in association with the frontal system moving up the East coast which brought light precipitation and northerly flow. Maximum temperatures increased to

20

- 23 o 

### C for the remainder of the episode in advance of the last strong storm system. Dew point temperatures at Knoxville were in the range of +2 to +15 o 

### C during the same period. Mi xing ratio values were in the range of 4 to 11 g kg

- 1 

### . Wind speeds at 10 m were generally under 5 m s

- 1 

### except for 31 March and later when speeds reached 7 m s

- 1 

### in advance of the last storm system. Wind directions were highly variable with the multiple storm systems. Three precipitation periods were delineated by the 6

- h observed precipitation amounts. For the periods 22

- 23 March and 26

- 28 March amounts were under 10 mm. The third precipitation event had amounts up to near 30 mm. Conditions at Martin sburg were similar except for the following: temperatures a nd mixing ratios were smaller, wind speeds were generally higher, and precipitation amounts were smaller.

### 64 Figure 4

- 11. Anomaly field for the 500

- mb geopotential heights created by taking the mean value for the period 0000 UTC 21 March

- 1200 UTC 1 April 1993 and subtracting from it the 15

- year mean for the same period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 65

Figure 4

- 12. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Tem

peratures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly ob served relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 66 Figure 4

- 13. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. The tick marks on the

horizontal

axis are marked in the same way as for the top panel.

### 67

Figure 4

- 14. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 March 1993 to

1200 UTC 1 April 1993. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Relative humidities are with respect to t he left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 68

4.5 The 6

- 14 February 1994 Episode

The February 1994 episode was categorized as Class 2 for GSM and for SNP for the metric of acidic deposition. For visibility there was one Class 1 day for both GSM and SNP. This episode was outside the

normal ozone season and was chosen for moderate acidic deposition and high visibility.

The anomaly field for the 500

- mb geopotential height for this episode is shown in Fig ure 4

- 16. A large area of below normal heights extended from most of southern Canada southwestward across the Great Lake s and then most of the western United States. Above normal heights were observed over the Southeast. The

500

- mb heights which were above normal over the Southeast are somewhat misleading in that a major incursion of Arctic air occurred during the period 9

- 11 February for the eastern half of the United States and was accompanied by a major ice storm from Texas to Ohio. The episode began on 6 February with an intense upper

- level closed low over Hudson Bay and broad cyclonic flow over much of the United Stat es. At the surface there was one frontal system across the northern United States from the Rocky Mountains eastward to New England and another one across the Gulf Coast states. A surface high of 1044 mb was Figure 4

- 15. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom
Panel Six

- hourly observed precipitation amounts (mm) at Martinsburg, West Virginia from 1200 UTC 21 March 1993 to 1200 UTC 1 Apri l 1993. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 69

over Alberta and would be the source of the Arc tic air for later in the period. During 7

- 10 February the Canadian high pressure moved south and east and brought much below normal temperatures to most of the eastern United States. By 10 February the upper

- level flow had become more zonal across the nor thern

half of the United States and with a sharp trough over Texas and New Mexico. A southern frontal system on 6 February dissipated while a northern one in advance of Arctic air moved south and east. By 10 February the Arctic front was located from Geo rgia southwestward to the Gulf of Mexico. A large area of

ove r running freezing precipitation developed in back of the front and was the first phase of an ice storm for much of the Southeast. During the period 10

- 11 February the upper

- level trough over Te xas moved eastward and in conjunction a weak wave developed on the southern frontal system bringing more winter precipitation to much of the Southeast. By 12 February the Arctic air had retreated to New England with a large area of Pacific high pressure b uilding over the western United States behind a developing frontal system across the Great Plains. The upper

- level flow was characterized by a trough from the northern Plains southwestward to Arizona. For the period 13

- 14 February the Plains frontal syst em moved eastward off the Atlantic coast and was followed by high pressure over much of the Southeast. Another Arctic front moved across New England. By 14 February the upper

- level flow had become zonal over much of the United States except over New Engl and where the flow was influenced by a closed low over southern Quebec.

Figures 4

- 17 and 4

- 18 show the time series of observations for Knoxville, Tennessee while Figures

4

- 19

and 4

- 20 show the same for Martinsburg, West Virginia. Martinsburg was chosen a s the nearest NWS site to SNP in place of Staunton and Charlottesville, Virginia which both again had large periods of time with missing data. The highest maximum temperature at Knoxville was near 20 o 

### C on 8 February followed by a drop of about 20 o 

### C with t he passage of the Arctic front on 9 February. Temperatures only slowly

recovered to 5 o 

### C on 12 February before another frontal passage on 13 February reduced temperatures to

near

- 5 o 

### C at the end of the episode. Dew point temperatures were in the range of

- 5 to 15 o 

### C with the mixing ratios of 2

- 11 g kg

- 1 

### . The almos t continuous relative humidity above 95% during the period 9

- 12 February coincide with the major period of frozen precipitation. The highest 10

- m wind speeds were in

advance of the Arctic front on February 9 with values near 10 m s

- 1 

### . Otherwise wind speeds were typically in the range of 3

- 5 m s

- 1 

### . The wind direction was dominated by northerly to northeasterly flow except for the period 11

- 13 February when winds were generally westerly. The main precipitation event of 9

- 11 February had 6

- h amounts up to 44 mm. Conditions at Martinsburg were similar except for the following: 1) temperatures were colder, 2) dew point temperatures and mixing ratios were smaller, 3) the highest wind speeds were at

the end of the episode during the period 12

- 13 February, and 4) 6

- h precipitation amounts were less than or equal to 8 mm and were more scattered across the episode.

### 70 Figure 4

- 16. Anomaly field for the 500

- mb geopotential heights created by taking the mean value for the period 0000 UTC 6 February

- 1200 UTC 14 February 1994 and subtracting from it the 15

- year mean for the same period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 71 Figure 4

- 17. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (d ashed curve) at Knoxville, Tennessee from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the ho rizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee 1200 UTC 6 February 199 4 to 1200 UTC 14 February 1994. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel

### 72 Fi gure 4

- 18. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 73 Figure 4

- 19. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the righ t vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Ma rtinsburg, West Virginia from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the

horizontal axis are marked in the same way as for the top panel.

### 74

4.6 The 21 July

- 1 August 1991 Episode

The July 1991 episode was chosen to replace an April 1994 episode for which attempts to simulate realistic precipitation amounts had failed. The July 1991 episode was Class 4 for acidic deposition at both GSM

and SNP. For visibi

lity there were two Class 5 days at GSM and two Class 4 days at SNP. A broad range of ozone conditions were represented at both parks with Classes 1

- 3. This episode represents high acidic deposition, low visibility, and low to moderate ozone at both GSM and SNP.

The anomaly field for the 500

- mb geopotential height for this episode is shown in Fig ure 4

- 21. The main feature affecting this episode was the area of below normal heights south of Hudson Bay to the Great Lakes

area. This was the result of an u pper

- level closed low in that region for much of the episode. Several short

waves rotated around this circulation and their associated surface fronts traveled southeastward across the Midwest and Southeast and then stalled over parts of the Southeast. Th is resulted in an active and very wet pattern for areas along and east of the Appalachians with locally heavy rains and flooding. The episode began on 21 July with an upper

- level closed low northeast of Hudson Bay and a trough extending

southward to the G

reat Lakes. Another Canadian trough was located over western Canada. At the Figure 4

- 20. Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 6 February 1994 to 1200 UTC 14 Febru ary 1994. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom
Panel Six

- hourly observed precipitation amounts (mm) at Martinsburg, West Virginia from 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 75

surface high pressure was located over the Great Lakes and over the Southeast with a frontal system across the northern United States. During 22

- 25 July the closed upper

- level l ow remained in the vicinity of Hudson Bay and the northern frontal system slowly moved southeastward and by 25 July it was a stationary front extending from Texas to Kentucky to Maryland. During this time several minor waves moved along the

front. The re mainder of the episode experienced another frontal system moving southeastward from the northern Plains which once again became stationary over the Southeast in about the same position. Both fronts produced showers and thunderstorms along and south of the ir positions.

Figures 4

- 22 and 4

- 23 show the time series of observations for Knoxville, Tennessee while Fig ure s 4

- 23

and 4

- 24 show the same for Martinsburg, West Virginia. Martinsburg was chosen as the nearest NWS site to SNP in place of Staunton and Cha rlottesville, Virginia which both again had large periods of time with missing data. Maximum temperatures at the beginning of the episode, from 21

- 23 July at Knoxville, were

near 34 o 

### C and then decreased to near 29

- 30 o 

### C for the rest of the episode with inc reased afternoon clouds

and showers. Dew point temperatures ranged from 18

- 22 o 

### C with the related mixing ratios in the range of

14

- 19 g kg

- 1 

### . Afternoon wind speeds were typically 3

- 5 m s

- 1 

### with generally light or calm winds at night.

Wind directions were

highl y variable across the episode. The 6

- h precipitation amounts were as large as 13 mm with precipitation being recorded on 24, 26, and 28 July. Conditions at Martinsburg were similar except for the following: 1) lower temperatures and mixing ratios, 2 ) higher wind speeds, and 3) lower precipitation amounts. As will be seen when the 12

- km model results are discussed (in section 6.6.2), the

Te nnessee Valley Authority rain gau ge network indicated that much heavier precipitation occurred in the

mountains t

han either Knoxville or Martinsburg recorded.

### 76

Figure 4

- 21. Anomaly field for t he 500

- mb geopotential heights created by taking the mean value for the period 0000 UTC 21 July

- 1200 UTC 1 August 1991 and subtracting from it the 15

- year mean for the same period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 77 F igure 4

- 22. Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Temperatures are with respect to the left ve rtical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 78 Figure 4

- 23. Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knox ville, Tennessee from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC eac h day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 79 Figure 4

- 24 . Top Panel: Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Temperatures are wit h respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Hourly observed relative h umidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Relative humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 80

4.7 The 3

- 12 August 1993 Episode

The 3

- 12 August 1993 episode contained one Class 3 day for acid deposition at GSM. Also at GSM, the episode contained two Class 3 visibility days and one Class 4 visibility day. At SNP, there was one Class 3 and two Class 4 visibility days during the episode. For ozone at GSM there were two Class 1 and four Class 2 days while at SNP there was one Class 1 and three Class 2 days. Thus, this episode was chosen for periods of moderately low visibility and high ozone.

The anomaly field for the 500

- mb geopotential height for this episode is shown in Figure 4

- 26 . Heights

were about a

verage over the southeastern U.S. and about 20 to 30 m below the long

- term mean over portions of upper Midwest.

Figures 4

- 27 and 4

- 28 show the time series of observations for Knoxville. Maximum daily temperatures at
Knoxville were in the 29 o 

### C to 33 o 

### C range during the episode with the minimum occurring during the middle of the period. The relative humidity showed the typical maxima near saturation in the mornings and minima in the afternoon in the range of 40

- 65 %. Precipitation occurred on several d ays of the episode. Figure 4

- 25 . Top Panel: Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees)

a t 10 m (dashed curve) at Martinsburg, West Virginia from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Wind speeds are with respect to the left vertical axis while the wind directions are with respect to the right vertical
axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom
Panel Six

- hourly observed precipitation amounts (mm) at Martinsburg, West Virginia from 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. The tick marks on the horizont al axis are marked in the same way as for the top panel.

### 81

The greatest six

- hour precipitation amounts occurred on 5 August (27 mm) and 13 August (23 mm) with lesser amounts (2mm to 17 mm) being recorded during other 6

- hr periods on the same or other days. Wind speeds at Knoxville were genera lly in the 1.5 m s - 1 

### to 4 m s - 1 

### range although occasionally winds did reach as high as 7 m s - 1 

### . Winds were generally calm at night. At Knoxville, winds were generally westerly for the first three days of the episode, becoming northerly for the 6 th 

### throug h 9 th 

### and then southeasterly through southwesterly through the remainder of the period.

The meteorological times series at Martinsburg, WV are shown in Figures 4

- 29 and 4

- 30 . At Martinsburg maximum daily temperatures were in the 26 o 

### C to 32 o 

### C range. As at Knoxville, the minimum occurred during the middle of the period. Relative humidity showed the typical maxima near saturation in the mornings and minima in the afternoon in the range of 35

- 55 %. Precipitation occurred on a few days at Martinsburg durin g the episode. The greatest six

- hour precipitatio n amounts occurred on 7 August (19 mm) with lesser amounts (2mm to 16 mm) recorded during other 6

- hr periods on the same or other days. Wind speeds were generally in the 2.5 m s - 1 

### to 5 m s - 1 

### range although occasionally winds did reach as high

as 8 m s - 1 

### . Winds were generally calm at night. Wind directions were generally variable at Martinsburg during the episode and missing data occurred for many hours.

4.8 The 22

- 29 June 1992 Episode

The 22

- 29 June 19 92 episode contained one Class 2 day for acid deposition at GSM and one Class 1 acid deposition day at SNP. The episode contained one Class 4 visibility day at GSM while SNP had two Class 4 days. For ozone at GSM there was one Class 3 and one Class 4 day s while at SNP there were three Class 3 days, one Class 1 day and one Class 3 day as well.

The anomaly field for the 500

- mb geopotential height for this episode is shown in Figure 4

- 31 . Heights were systematically lower than average over the eastern U.S. during the June 1992 episode. Over the southeastern U.S., the geopotential heights were typically 30 to 60 m or more lower than the long

- term mean. The greatest depression was in the upper Midwest where the means were as much as 150 m lower

than normal.

Figures 4

- 32 and 4

- 3 3 show the time series of observations for Knoxville. Maximum daily temperatures at

Knoxville were in the 24 o 

### C to 31 o 

### C range during the episode with the maxima occurring during the middle and at the very end of the period. Relativ e humidity showed maxima near saturation on about half of the mornings and minima in the afternoon in the range of 40

- 65 %. Light precipitation (i.e., less than 1 mm) occurred on three days of the episode. Wind speeds at Knoxville were generally in the 1. 5 m s - 1 

### to 5 m s - 1


### range although occasionally winds did reach as high as 7 m s - 1 

### . Winds were generally calm at night. At Knoxville, winds were generally westerly for the first three days of the episode, becoming northerly for the remainder of the period

.

The meteorological times series at Martinsburg, WV are shown in Figures 4

- 3 4 and 4

- 3 5. At Martinsburg maximum daily temperatures climbed steadily throughout the episode from 22 o 

### C to 30 o 

### C. Nighttime lows

were around 12 o 

### C. The relative humidity showe d the typical maxima near saturation in the mornings and minima in the afternoon in the range of 35

- 55 %. Light precipitation occurred on a couple of days at

### 82

Martinsburg during the episode. The greatest six

- hour precipitation amounts occurred on 25 June (6 mm) with 3mm recorded on 24 June. Wind speeds at Knoxville were generally in the 2 m s - 1 

### to 6 m s - 1 

### range although occasionally winds did reach as high as 7 m s - 1 

### to 8 m s - 1 

### . Winds were calm at night. Wind directions were generally westerly at Martin sburg for the first half of the episode, becoming northerly on the

26 th 

### through 28 th 

### and then becoming southerly on the 29 th 

### . Missing data were reported for many hours.

4.9 The 24 April

–3 May 1995 Episode

The 24 April

–3 May 1995 episode contained o ne Class 1 day for acid deposition at GSM and one Class 2 acid deposition day at SNP. AT GSM, the episode contained one visibility day in each of Classes 2, 3, and 4. At SNP, there were three Class 2 visibility days during the episode. For ozone at GSM t here was one Class 1, two Class 2, and one Class 1 days while at SNP there were three Class 1, one Class 2, and one Class 4 days.

The anomaly field for the 500

- mb geopotential height for thi s episode is shown in Figure 4

- 36 . Heights

were about 25 m to 7 5 m below the long

- term mean over the southeastern U.S. with larger depressions from normal over the upper Midwest.

Figures 4

- 37 and 4

- 3 8 show the time series of observations for Knoxville. Maximum daily temperatures at

Knoxville were in the 16 o 

### C to 24 o 

### C range during the episode with the maximum occurring during the middle of the period (i.e., 29 April). Relative humidity showed the typical maxima near saturation in the mornings and minima in the afternoon in the approximate range of 30

- 80 %. Preci pitation occurred on several days of the episode. The greatest six

- hour precipitation amounts occurred on a few days during the period. On 1 May, the highest 6

- hr precipitation total was 17 mm with 9 mm in the subsequent 6

- hr period. Trace amounts (< 2m

m) were reported on a couple of other days. Wind speeds at Knoxville were generally in the 1.5 m s - 1 

### to 5 m s - 1 

### range although occasionally winds did reach as high as 8 m s - 1 

### . Winds were generally calm at night. The wind directions at Knoxville generall y westerly for the first four days of the episode, becoming southerly by 1 May and then veering to the west for much of the remainder of the period.

The meteorological times series at Staunton, VA are shown in Figures 4

- 3 9 and 4

- 40 . At Staunton,

maximum daily temperatures were in the 18 o 

### C to 24 o 

### C range. The minimum temperature occurred on 27 April. Relative humidity showed the typical maxima near saturation in the mornings and minima in the afternoon in the range of 30

- 55 %. No precipitation was measu red at Staunton during the episode. Wind speeds were generally in the 1.5 m s - 1 

### to 5 m s - 1 

### range although occasional ly winds did reach as high as 8 m

s - 1 

### . Winds were generally calm at night. West to southwesterly wind directions were measured at Staunto n during the first few days of the episode, becoming northeast easterly for the remainder of the period. There were many hours for which wind direction data were missing at Staunton.

### 83 Figure 4

- 26. Anomaly Field for the 500

- mb Geopotential Heights Created by T aking the Mean Value for the Period 0000 UTC 3 August

– 1200 UTC 12 August 1993 and Subtracting From it the 15

- Year Mean for
the Same Period. Units are in Meters.

### 84 Figure 4

- 27. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewp oint Temperatures ( o C) at 2 m (dashed curve) at Knoxville, Tennessee From 3 August 1993 1200 UTC to 12 August 1993 1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at K noxville, Tennessee From 3 August 1993 1200 UTC to 12 August 1993 1200 UTC.

### 85 Figure 4

- 28. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 3 August 1993 1 200 UTC to 12 August 1993 1200 UTC. Bottom Panel : Six

- Hourly Observed Precipitation Amounts (mm) at Knoxville, Tennessee From 3 August 1993 1200 UTC to 12 August 1993 1200 UTC.

### 86 Figure 4

- 29. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewpoint Temperature s ( o C) at 2 m (dashed curve) at Martinsburg, West Virginia From 3 August 1993 1200 UTC to 12 August 1993 1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at Martinsburg , West Virginia From 3 August 1993 1200 UTC to 12 August 1993 1200 UTC.

### 87

Figure 4

- 30. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Martinsburg, West Virginia From 3 August 1993 1200 UTC to 12 August 1993

1200 UTC. Bottom Panel: Six

- Hourly Observed Precipitation Amounts (mm) at Martinsburg, West Virginia from 3 August 1993 1200 UTC to 12 August 1993 1200 UTC.

### 88 Figure 4

- 31. Anomaly Field for the 500

- mb Geopotential Heights Created by Taking the Mean Value for
the Period 0000 UTC 22 June

– 1200 UTC 29 June 1992 and Subtracting From it the 15

- Year Mean for the

Same Period. Units are in Meters.

### 89

Figure 4

- 32. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewpoint Temperatures ( o C) a

t 2 m (dashed curve) at Knoxville, Tennessee From 22 June 1992 1200 UTC to 29 June
1992 1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee From 22 June 1992 1200 UTC to 29 June
1992 1200 UTC.

### 90

Figure 4

- 33. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 22 June 1992 1200 UTC to 29
June 1992 1200 UTC

. Bottom Panel : Six

- Hourly Observed Precipitation Amounts (mm) at Knoxville,
Tennessee From 22 June 1992 1200 UTC to 29 June 1992 1200 UTC.

### 91 Figure 4

- 34. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewpoint Temperatures ( o C) at 2 m (dashed cur ve) at Martinsburg, West Virginia From 22 June 1992 1200 UTC to 29
June 1992 1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at Martinsburg, West Virginia From 22 June 1992 1200 UTC
to 29 June 1992 1200 UTC.

### 92 Figure 4

- 35. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Martinsburg, West Virginia From 22 June 1992 1200 UTC to 29 Jun e 1992 1200

UTC. Bottom Panel: Six

- H ourly Observed Precipitation Amounts (mm) at Martinsburg, West Virginia from 22
June 1992 1200 UTC to 29 June 1992 1200 UTC.

### 93 Figure 4

- 36. Anomaly Field for the 500

- mb Geopotential Heights Created by Taking the Mean Value for the Period 0000 UTC 24 April

– 1200 UTC 3 May 1995 and Subtracting From it the 15

- Year Mean for the
Same Period. Units are in Meters.

### 94 Figure 4

- 37. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewpoint Temperatures ( o C) at 2 m (dashed cu rve) at Knoxville, Tennessee from 24 April 1995 1200 UTC to 3 May
1995 1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee From 24 April 1995 1200 UTC to 3 May
1995 1200 UTC.

### 95 Figure 4

- 38. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 24 April 1995 1200 UTC to 3 May 1995 1200 UTC. Bottom Panel : Six

- Hourly Observed Precipitation Amounts (mm) at Knoxville, Tennessee From 24 April 1993 1200 UTC to 3 May 1995 1200 UTC.

### 96

Figure 4

- 39. Top Panel : Hourly Observed Temperatures ( o C) at 2 m (solid curve) and Dewpoint Temperatures ( o C) at 2 m (dashed curve) at Staunton, Virginia From 24 April 1995 1200 UTC to 3 May 1995

1200 UTC. Bottom Panel: Hourly Observed Relative Humidities (%) at 2 m (solid curve) and Mixing Ratios (gm kg
- 1 ) at 2 m (dashed curve) at Staunton, Virginia From 24 April 1995 1200 U TC to 3 May 1995 1200 UTC.

### 97 Figure 4

- 40. Top Panel : Hourly Observed Wind Speeds (m s
- 1 ) at 10 m (solid curve) and Wind Directions (degrees) at 10 m (dashed curve) at Staunton, Virginia from 24 April 1995 1200 UTC to 3 May 1995 1200 UTC.

Bottom Panel: Six

- Hourly Observed Precipitation Amounts (mm) at Staunton, Virginia from 24 April 1995 1200 UTC to 3 May 1995 1200 UTC.

### 98

4.10 The 22

- 30 May 1995 Episo de

The May 1995 episode was categorized as a Class 1 for GSP and Class 2 for SNP for the metric of acidic deposition. For visibility there was one Class 4 day at GSM and one Class 3 and one Class 4 days at SNP. For ozone both Parks registered Class 1, C lass 2, and Class 3 days. For GSP the number of days (in parentheses) for each class was: Class 1 (1), Class 2 (1), and Class 3 (3). For SNP the number of days (in parentheses) for each class was: Class 1 (2), Class 2 (1), and Class 3 (1). So overall t his episode was chosen for a broad range of conditions which can briefly be summarized as low to moderate acidic deposition, moderate ranges of visibility, and low to moderately high levels of ozone.

The anomaly field for the 500

- mb geopotential height fo r this episode is shown in Fig 4

- 41. It reveals below normal heights for much of the western and central United States and above normal heights for much of the eastern and southeastern United States. The boundary between these two areas represented an

ac tive frontal area with several storm systems with the strongest being during the period 27

- 30 May. The surface low with the latter system tracked from Kansas on 27 May to northeast of Maine on 30 May. This system produced an unusual F4 tornado in Great B arrington, Massachusetts ( Storm Data ). During the time

of 22

- 26 May the surface pattern was predominated by a quasi

- stationary front which extended generally from Texas northeastward to New England. By 26 May the eastern portions of this front had moved south such that the frontal position was located from Texas to Virginia. Several waves which moved along this front brought considerable precipitation to the central and northern United States. Very little precipitation fell across the immediate SAMI reg ion during this period where surface high pressure dominated. The episode ended with the strong storm system mentioned earlier which pulled the stationary front back north as a warm front across the entire SAMI region during 28

- 29 May followed by a cold f rontal passage during

29

- 30 May.

Figures 4

- 42 and 4

- 43 show the time series of observations for Knoxville, while Fig ure s 4

- 44 and 4

- 45 show the same for Staunton, Virginia. The Charlottesville, Virginia site again had large segments of missing

data for this episode. Knoxville experienced a slow warming trend for 22

- 27 May with maximum temperatures increasing from near 25 o 

### C to near 30 o 

### C on 27 May. Maximum temperatures decreased for

28

- 29 May in response to increased clouds and precipitation related to the strong system moving across the

northern United States. Dew point temperatures showed a similar trend, increasing from near 12 o 

### C on May

22 to 22 o 

### C on 27 May followed by fairly constant values until a decrease on the morning of 30 May with a

cold front al passage. The related mixing ratios increased from 10 g kg

- 1 

### on May 22 to near 17 g kg

- 1 

### on 27 May followed by a slight decrease until the cold frontal passage on 30 May when values decreased to near 11 g kg

- 1 

### . On several mornings the relative humidity was at 100% which probably meant fog was observed. Winds were typically light or calm at night with afternoon maximum speeds generally of 3

- 5 m s

- 1 

### and from

directions of 180

- 270 o 

### in response to the surface high pressure for most of the week. Precipitat ion was only observed on 27

- 28 May with 6

- h amounts ranging up to 12 mm. Staunton experienced similar conditions with the following exceptions. Temperatures were cooler in general with the lowest maximum temperatures near 17 o 

### C recorded during 27

- 28 May w hen Knoxville was the warmest for the episode. This was the result of being north of the stationary front much of the time. Afternoon maximum wind speeds were higher than Knoxville, being in the range of 4

- 7 m s
- 1 

### . No precipitation was recorded at Staun ton for the entire episode.

### 99 Figure 4

- 41. Anomaly field for the 50 0

- mb geopotential heights created by taking the mean value for the period 0000 UTC 22 May

- 1200 UTC 30 May 1995 and subtracting from it the 15

- year mean for the same period. Units are in decameters (dam). Image obtained from NOAA

- CIRES CDC.

### 100 Figure 4

- 42. Top Panel : Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures (

o C) at 2 m (dashed curve) at Knoxvill e, Tennessee from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC eac h day but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Rela tive humidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 101 Figure 4

- 43. Top Panel : Hourly observed win d speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Knoxville, Tennessee from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Wind speeds are with respect to the left vertical axis while the wind directions are wit h respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

- hourly observed precipitation amounts (mm) at Knoxville, Tennessee from 1200 UTC 22 May 1995 to 1 200 UTC 30 May 1995. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 102

Figure 4

- 44. Top Panel : Hourly observed temperatures ( o C) at 2 m (solid curve) and dewpoint temperatures ( o C) at 2 m (dashed curve) at Staunton, Virginia from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Temperatures are with respect to the left vertical axis while the dewpoint temperatures are with respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each da y but are only labeled every other day. Bottom Panel Hourly observed relative humidities (per cent) at 2 m (solid curve) and mixing ratios (g kg
- 1 ) at 2 m (dashed curve) at Staunton, Virginia from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Relative h umidities are with respect to the left vertical axis while the mixing ratios are with respect to the right vertical axis. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 103 Figure 4

- 45. Top Panel : Hourly observed wind speeds (m s
- 1 ) at 10 m (solid curve) and wind directions (degrees) at 10 m (dashed curve) at Staunton, Virginia from 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Wind speeds are with respect to the left vertical axis while the wind directions are wi th respect to the right vertical axis. The tick marks on the horizontal axis are at 1800 UTC each day but are only labeled every other day. Bottom Panel Six

hourly observed precipitation amounts (mm) at Staunton, Virginia from 1200 UTC 22 May 1995 to 12 00 UTC 30 May 1995. The tick marks on the horizontal axis are marked in the same way as for the top panel.

### 104

5.0 TECHNICAL APPROACH TO THE MODEL PERFORMANCE EVALUATION

5.1 Components of the RAMS Evaluation

The goal of the RAMS model evaluation is to assess whether and to what extent confidence may be placed in the modeling system to provide meteorological variables such as wind, temperature, moisture, cloud , and precipitation inputs to the SAMI URM

- 1ATM air quality model . The term "modeling system" refers both to the core RAMS model code as well as its various preprocessor and data preparation programs, and the supporting data bases used to exercise the mod el. The main objective of the evaluation is to reveal the presence of bias and internal, compensating errors in the model that, unless discovered and rectified, or at least quantified, may lead to erroneous or fundamentally incorrect decisions based on ai r quality model usage. If the evaluation is sufficiently rigorous and no significant errors or causes for concern are identified, then there is justification for declaring the model suitable for use in its intended purpose, i.e, supporting regional acid d

eposition and visibility modeling.

The present evaluation of the RAMS model consists of two components. The operational evaluation entails an assessment of the model's ability to estimate correctly key meteorological variables including surface wind, tem

perature, and mixing ratios largely independent of whether the actual process descriptions in the model are accurate. The operational evaluation essentially tests whether the predicted meteorological fields used as inputs URM

- 1ATM are reasonable, consiste nt and agree adequately with available observations. In this study, the operational evaluation focuses on the RAMS’ ability to reproduce hourly surface wind speed, wind direction, temperature, and mixing ratio observations across the SAMI modeling

domain. Tables 5

- 1 and 5

- 2 identify the full set of statistical measures and graphical displays that have been produced the SAMI RAMS simulations. (Only for the July ’95 and July ’91 episodes was a more limited set of evaluation statistics/displays developed).

Ideally, the scientific evaluation addresses the realism of the meteorological processes simulated by the model through testing the model as an entire system (i.e., not merely focusing on surface wind predictions) as well as its component parts. The scie ntific evaluation seeks to determine whether the model's behavior, in the aggregate and in its component modules, is consistent with prevailing theory, knowledge of physical processes, and observations. Ideally, the scientific evaluation consists of a ser ies of diagnostic and mechanistic tests aimed at: (a) examining the existence of compensatory errors, (b) determining the causes of failure of a flawed model, (c) stressing a model to ensure failure if indeed the model is flawed, (d) provide

additional ins ight into model performance beyond that supplied through routine, operational evaluation procedures. Unfortunately, a detailed scientific evaluation of the RAMS model was not possible with the SAMI data sets due to the absence of the specific measurements needed to test the process modules (e.g., soil moisture, Reynolds str ess measurements, and so on). However, we can provide a limited scientific evaluation through comparisons of the modeled and observed liquid precipitation fields. However, where

discre pancies exist, there is insufficient dat a to allow a definit ive diagnosis of the potential causes.

5.2 Data Supporting Model Evaluation

Hourly surface observations were obtained from NCAR to support an evaluation of RAMS near

- surface

### 105

temperature, water v

apor, and wind speed fields. The specific NCAR data set used for this purpose was DS472.0 which is the hourly airways surface data. The primary data set available for comparing model performance aloft was the NCEP/NCAR reanalysis data files. The reanaly sis data set is the result of a large effort to reanalyze global data back to 1957 with a fixed state

- of

- the

- art analysis/forecast system. The reanalysis data variables are a mixture of model and observations, with observations dominating in those

areas w here data is more plentiful. The reanalysis data were processed as described in section 3.2 and in the SAMI meteorological modeling protocol (Norris and Doty, 1998).

5.3 Statistical and Graphical Evaluation Tools

The operational evaluation includes the calculation and analysis of several statistical measures of model performance and the plotting of specific graphical displays to elucidate the basic performance of the model in simulating atmospheric variables. These specific statistical measures are defi ned below. These procedures have been employed extensively in other prognostic model performance testing (see, for example, Steyn and McKendry, 1988; Ulrickson and Mass, 1990; Hanna, 1 994; Tesche and McNally,

1993 , 1996; McNally and Tesche, 1996a,b, 1998; Seaman and Stauffer, 1996; Seaman e t al., 1997; Tesche et al., 2000 ). These analysis procedures are incorporated into the M odel Performance Evaluation,

A nalysis, and P lotting S oftware (MAPS) system (McNally and Tesche, 1994) which also includes a variety of other statistical and graphical testing methods for photochemical and meteorological models.

5.3.1 Mean and Global Statistics

Several statistical measures are calculated as part of the meteorological model evaluation. In the definitions below, the va riable F represents a model

- estimated or derived quantity, e.g., wind speed, wind direction, PBL height, mixing ratio, precipitation amount, or temperature. The subscripts e and o correspond to

model

- estimated and observed (i.e., measured) quantities, res pectively. The subscript i refers to the ninth hour of the day.

Mean Estimation (M
e 

### ). The mean model estimate is given by:

Φ 

# ∑ ei N

1 = i 

### N 1

= M e


### where N is the product of the number of simulation hours and the number of ground

- level monitoring

locations providing hourly

- averaged observationa l data. F e i 

### represents the model

- estimate at hour i.

Mean Observation (M
o 

### ). The mean observation is given by:

Φ 

# ∑ oi N

1 = i 

### N 1

= M o



### Here, F oi 

### represents the observations at hour i.

Average Wind Direction. Because wind direction has a crossover point b etween 0 degrees and 360 degrees, standard linear statistical methods cannot be used to calculate the mean or standard deviation. The

### 106

method proposed by Yamartino (1984) performs well in estimating the wind direction standard deviation. Specifically, thi s quantity is calculated by:

where: 

# [ ] ] ) ( + ) ( [

- 1.0 = 2 2 1/2


### a a b cos

sin

Here, α is the measured hourly or instantaneous wind direction value.

5.3.2 Difference Statistics

Residual (d

i 

### ). For quantities that are continuous in space and time (i. e., wind speed, temperature, pressure, PBL height, species concentrations) difference statistics provide considerable insight into the model’s performance, temporally and spatially. Difference statistics are based on the definition of a residual quantity. A mixing ratio residual, for example, is defined as:



### t)

, x ( c - t)

, x ( c = d i o i e i



### where d i 

### is the i

- th residual based on the difference between model

- estimated (c
e 

### ) and observed (c
o 

### ) mixing

ratio at location x and time i. In the definitions that follow, we shall use the letter c to denote any continuous atmospheric variable (e.g., temperature, precipitation amount, PBL height).

Standard Deviation of Residual Distribution (SDr). The standard deviation of the residual distribution is given by :

        

# ∑ )

MBE

- d ( 2
i N

1 = i 0.5



### 1

- N 1

= SD

r



### where the residual is defined as:



### t)

, x ( c - t)

, x ( c = d i o i e i



### and MBE is the first moment, i.e., the mean bias error, defined shortly. This statistic describes the "dispersion" or spread of the residual distribution about the estimate of the mean. The standard deviation is calculated using all estimation

- observation pairs above the cutoff level. The second moment of the residual distribution is the variance, the square of the standard deviation. Since the standard deviation has the same units of measure as the variable (e.g., meters/sec for wind), it is used here as the metric for dispersion. The standard deviation and variance measure the average "spread" of the residuals, independent of any systematic bias in the estimates. No di rect information is provided concerning sub

- regional errors or about ] 0.1547 + 1 [ ) ( = 3 

### b b s a 

### arcsin

### 107

large discrepancies occurring within portions of the diurnal cycle although in principle these, too, could be

estimated.

### 108

Mean Bias Error (MBE). The mean bias error is given by:

t))

, x ( c

- t)

, x ( c ( i

o i e N

1 = i 

# ∑ N 1

= MBE



### where N equals the number of hourly estimate

- observation pairs drawn from all valid monitoring station data on the simulation day of interest.

Mean Normalized Bias Error (MNBE). The mean normalized bias error, often just called the bias, is given by:

% 100 x t)

, x ( c t))

, x ( c - t)

, x ( c (

i

o i o i e N

1 = i 

# ∑ N 1

= MNBE



### Mathematically, the bias is derived from the average signed deviation of the mixing ratio (or temperature) residuals and is calculated using all pairs of estimates and observations above the cutoff level.

Mean Absolut e Gross Error (MAGE). The mean gross error is calculated in two ways, similar to the bias. The mean absolute gross error is given by:

| t)

, x ( c

- t)

, x ( c | i

o i e N

1 = i 

# ∑ N 1

= MAGE



### Mean Absolute Normalized Gross Error (MANGE). The mean absolute normalized gross error (or simpl y ‘gross error’) is:

% 100 x t)

, x ( c |

t)

, x ( c - t)

, x ( c |

i

o i o i e N

1 = i 

# ∑ N 1

= MANGE



### The gross error quantifies the mean absolute deviation of the residuals. It indicates the average unsigned discrepancy between hourly estimates and observations and is calculated for all pairs. Gross error is a robust measure of overall model performance and provides a useful basis for comparison among model simulations across different model grids or episodes. Unless calculated for specific locations or time intervals, gross error estimates provide no direct in formation about sub

- regional errors or about large discrepancies occurring within portions of the diurnal cycle.

Root Mean Square Error (RMSE). The root mean square error is given by:

|

- | oi ei 2 N

1 = i 1/2



###       Φ Φ 

# ∑ N 1

= RMSE

### 109

The RMSE, as with the gross error, is a good overall measure of model performance. However, since large errors are weighted heavily, large errors in a small subregion may produce large a RMSE even though the errors may be small elsewhere.

Systematic Root Mean Square Error (RMSE
s 

### ). A measure of th

e model's linear (or systematic) bias may be estimated from the systematic root mean square error given by:

|

- ˆ | oi ei 2 N

1 = i 1/2



###       Φ Φ 

# ∑ N 1

= RMSE

s



### Unsystematic Root Mean Square Error (RMSE

u 

### ). A measure of the model's unsystematic bias is given by the unsystematic root me an square error, that is:

| ˆ

- | ei ei 2 N

1 = i 1/2



###      
Φ Φ 

# ∑ N 1

= RMSE

u



### The unsystematic difference is a measure of how much of the discrepancy between estimates and observations is due to random processes or influences outside the legitimate range of the model.

A "good" model will provide low values of the root mean square error, RMSE, explaining most of the variation in the observations. The systematic error, RMSE
s 

### should approach zero and the unsystematic

error RMSE

u 

### should approach RMSE since:

RMSE

2 

### = (RMSE

S 

### ) 2 

### + (RMSE

U 

### ) 2



### It is important that RMSE, RMSE
s 

### , and RMSE

u 

### are all analyzed. For example, if only RMSE is estimated (and it appears acceptable) it could consist largely of the systematic component. This bias might be removed, thereby reducing the bias transferred to the photochemical model. On the other hand, if the RMSE consists largely of the unsystematic component (RMSE

u 

### ), this indicates further error reduction may require model refinement and/or data acquisition. It also provides error bars that may used with th e inputs in subsequent sensitivity analyses.

5.3.3 Skill Measures

Index of Agreement (I). Following Willmont (1981), the index of agreement is given by:



###     

 

    

 

Φ Φ 

# ∑ ) | M

- | + | M

- | (

o oi o ei N

1 = i 2



### (RMSE) N

- 1 = I 2

### 110

This metric condenses all the differences between model estimates and observ ations into one statistical quantity. It is the ratio of the cumulative difference between the model estimates and the corresponding

observations to the sum of two differences: between the estimates and observed mean and the observations

and the observed mean. Viewed from another perspective, the index of agreement is a measure of how well the model estimates departure from the observed mean matches, case by case, the observations' departure from the observed mean. Thus, the correspondence between estima ted and observed values across the domain at a given time may be quantified in a single metric and displayed as a time series. The index of agreement has a theoretical range of 0 to 1, the latter score suggesting perfect agreement.

RMS Skill Error (Skill
e 

### ). The root mean square error skill ratio is defined as:

SD RMSE

= Skill

o u E



### Variance Skill Ratio (Skill
var 

### ). The variance ratio skill is given by:

SD SD

= Skill

o

e Var



### 5.3.4 Graphical Tools

Many features of meteorological model simulations are be st analyzed through graphical means. In addition to revealing important qualitative relationships, graphical displays also supply quantitative information. The main graphical displays used to analyze the performance results include:

> The temporal cor

relation between estimates and observations;

> The spatial distribution of estimated fields;

> The correlation among hourly pairs of estimates, observa tions and residuals;

> The variation in bias and error estimates as functions of time and space;

> The degree of mismatch between volume

- averaged model estimates and point measurements;

and

> Log p/Skew

- T plots of wind, temperature and mixing ratio.

These plotting methods are exemplified in the many recent prognostic meteorological model evaluation

studies (see, for example, Seaman, 2000 ; Tesche, et al., 200 0 ).

### 111

Table 5

- 1. Statistical Measures and Graphical Displays Used in the Operational Evaluation of RAMS Surface Meteorological
Variables

Statistical Measure Graphical Display

Surface Winds ( 

### ms - 1 ) Vector mean observed wind speed Vector mean modeled and observed wind speeds as a function of time Vector mean predicted wind speed Scalar mean modeled and observed wind speeds as a function of time

Sca lar mean observed wind speed Modeled and observed mean wind directions as a function of time Scalar mean predicted wind speed Modeled and observed standard deviations in wind speed as a

function of time

Mean observed wind direction RMSE, RMSE

s , and RMSE

u errors as a function of time Mean predicted wind direction Index of Agreement as a function of time Standard deviation of observed wind speeds Surface wind vector plots of modeled and observed winds every 3

- hrs Standard deviation of predicted wind spee ds

Standard deviation of observed wind directions Standard deviation of predicted wind directions Total RMSE error in wind speeds Systematic RMSE error in wind speeds Unsystematic RMSE error in wind speeds Index of Agreement (I) in wind speeds

SKILL

E skill scores for surface wind speeds

SKILLvar skill scores for surface wind speeds

Surface Temperatures ( 0 C ) Maximum region

- wide observed surface temperature Normalized bias in surface temperature estimates as a function of time

Maximum region

- wide predicted surface temperature Normalized error in surface temperature estimated as a function of time Normalized bias in hourly surface temperature Scatter plot of hourly observed and modeled surface temperatures Mean bias in hourly surface t emperature Scatter Plot of daily maximum observed and modeled surface
temperatures Normalized gross error in hourly surface temperature Standard deviation of modeled and observed surface temperatures as a

function of time Mean gross error in hourly surfa ce temperature Spatial mean of hourly modeled and observed surface temperatures as

a function of time Average accuracy of daily maximum temperature
estimates over all stations Isopleths of hourly ground level temperatures every 3

- hr

Variance in hourly t emperature estimates Time series of modeled and observed hourly temperatures as selected

stations Surface Mixing Ratio (gm/Kg) Maximum region

- wide observed mixing ratio Normalized bias in surface mixing ratio estimates as a function of time Maximum reg ion

- wide predicted mixing ratio Normalized error in surface mixing ratio estimates as a function of time Normalized bias in hourly mixing ratio Scatter Plot of hourly observed and modeled surface mixing ratios Mean bias in hourly mixing ratio Scatter Plo t of daily maximum observed and modeled surface mixing

ratios Normalized gross error in hourly mixing ratio Standard deviation of modeled and observed surface mixing ratios as a

function of time Mean gross error in hourly mixing ratio Spatial mean of hou rly modeled and observed surface mixing ratios as a

function of time Average accuracy of daily maximum mixing ratio Isopleths of hourly ground level mixing ratios every 3

- hr Variance in hourly mixing ratio estimates Time series of modeled and observed ho urly mixing ratios at selected

stations

### 112

Table 5

- 2. Statistical Measures and Graphical Displays Used in the RAMS Operational Evaluation of Aloft Meteorological Variables

Statistical Measure Graphical Display

Aloft Winds ( 

### ms - 1 )

Vertically averaged mean observed wind speed aloft for each sounding Vertical profiles of modeled and observed horizontal winds at each sounding location

Vector averaged mean predicted wind speed aloft for each sounding

Vertically averaged mean observed wind direction aloft for each sounding

Vertically averaged mean predicted wind direction aloft for each sounding
Aloft Temperatures ( 0 C )

Vertically averaged mean temperature observations aloft fo r each sounding Vertical profiles of modeled and observed temperatures at each sounding location

Vertically averaged mean temperature predictions aloft for each sounding
Aloft Mixing Ratio ( gm/Kg )

Vertically averaged mean mixing rat io observations aloft for each sounding Vertical profiles of modeled and observed mixing ratios at each sounding location

Vertically averaged mean mixing ratio predictions aloft for each sounding

### 113

6.0 EVALUATION OF SURFACE METEOROLOGICAL FIELDS

There is a variety of methods that can be used to evaluate the performance of meteorological modeling. Besides the statistical measures described in section 5, there are different ways of manipulating both model results and observations for direct comparison be tween them. Therefore, it is not surprising, with two groups doing the meteorological simulations for SAMI, that each shared some common evaluation elements while also having preferences for somewhat different approaches. This section describes the perfor mance evaluations for each episode. Similarities and differences in techniques are highlighted, where appropriate. Note that differences in techniques are more a matter of preference than correctness. Each technique requires some assumptions that influen ce the outcome. It is possible, by using a variety of approaches, to look at model performance from different viewpoints and gain a clearer understanding of the results.

Both sets of modeling episodes were evaluated by focusing primarily on model perform ance for the 12

- km grid. In addition, both approaches computed bias, relative error, root mean square error, and index of agreement for temperature, wind speed and direction, water vapor mixing ratio and precipitation. Both approaches also examined model performance using measurements made near the surface (section 6) and aloft (section 7).

Methodological differences were mainly due to the emphasis on analyzing a variety of residual measures (i.e. differences between predictions and observations at discr ete monitoring locations) for the April 1995, June 1992 and August 19 9 3 episodes (the so

- called Alpine Geophysics, or AG, episodes), while for the other (so

- called University of Alabama in Huntsville, or UAH) episodes more emphasis was placed on the
comput ation of spatial biases and comparisons between the model and measurements for two ground stations near GSM and SNP. In addition, surface statistical results for the AG episodes were computed by comparing model results for the lowest level directly with s urface observations. Surface observations were not measured at the exact same heights above ground as the corresponding model level, so some differences between model and observation are caused by the height difference. For the UAH episodes the

model

- to

- observation comparisons were done by vertically interpolating, using similarity theory, model parameters to match the measurement heights. This reduces bias for these comparisons in many situations,

but can also introduce bias under certain conditions. L ikewise, the computation of spatial differences in modeled fields done for the UAH episodes provides a picture of model performance that is easy to understand, but it is complicated by the fact that observations must be spatially interpolated for compariso n with model results across all portions of the grid. The AG method for spatial comparison is more straightforward and does not rely on spatial interpolation assumptions, but results are somewhat more difficult to visualize. Therefore, no “perfect” syste m for model evaluation exists, but the techniques used by UAH and AG are complementary and comprehensive. Section 8 provides an analysis of RAMS performance across all SAMI modeling episodes using a common set of performance metrics.

However, one importa

nt aspect of the meteorological modeling that was not assessed was the simulation of cloud cover. Clouds play important roles in controlling the surface energy balance, vertical long

- and short

wave radiation fluxes (the latter influencing photochemical r eaction rates), precipitation formation and heterogeneous chemical reactions. Although no formal attempt was made to determine the success of cloud modeling, a simple comparison was made between cloud cover observations at two NWS sites near the

### 114

GSM (Knox

ville and Asheville) and cloud cover as expressed in the URM

- 1ATM by cloud information passed to it from RAMS. This comparison, done for the April 1995 and June 1992 episodes, revealed that the cloud cover (expressed as fractional cloud cover) in URM

- 1ATM was far less than observed. There is insufficient information to draw conclusions, but it should be noted that, for episodes experiencing extensive cloud cover, an underestimation bias could contribute to biases in simulated near

- surface air temperature (underestimates at night and overestimates during the day), wind speed, wind direction, and even to some extent water vapor mixing ratio. In addition, insufficient clouds would likely lead to an under

- representation of heterogeneous chemical processes suc h as the aqueous phase oxidation of sulfur dioxide to sulfate in

cloud water.

6.1 11

- 19 July 1995 Episode Results

Verification of the UAH episodes was done with respect to the eastern portions of the coarse grid (48

- km for the July 1995 episode, 96

- km f or all the rest) and the 12

- km grid. Unless otherwise noted the entire simulation was used, including the so

- called "ramp

- up days". The verification tools for the coarse grid were horizontal plots of the bias and the root mean square error (RMSE) statist ics for temperature and water vapor mixing ratio at 2 m, and for wind direction and wind speed at 10 m. Horizontal plots were also presented of the total model and observed precipitation on the coarse grid. The bias and RMSE are defined by 6.1

- 6.4

(6.1) 

# ( )

∑

= − = n

i A A

n BIAS

c m 

### 1 1



### ,

(6.2) 

### ∑

= 

      

−

= n

i A A A

n BIAS

c c m 

### 1 100

(6.3) 

# ( )

∑

= − = n

i A A

n RMSE

c m 

### 1 1 2



### ,

(6.4) ∑

= − = 

# 

    
n

i A A A

n RMSE

c c m 

### 1 100 2



### ,

where n is the number of comparison times, A

m 

### is the model value of the variable "A", and A
c 

### is the comparison value for the same varia ble. Equations (6.2) and (6.4) express the bias and RMSE, respectively, as a percentage of the comparison values. Unless otherwise noted, all hours of an episode will comprise the number of comparison times. For the coarse grid the comparison values are obtained by using a Barnes (1973) analysis (as discussed in section 3.2) of NWS hourly observations which gives an observed analysis on the same grid as the model coarse grid. One important note must be made about such an observed analysis: it is not an accurate indication of actual conditions in high terrain areas because the

### 115

observations are almost entirely made from locations outside the mountainous areas. For example, if the model is performing correctly in situations where the temperatures are decr easing with height, then there will be a cool "bias" over high terrain areas. For the 12

- km verifications the model values are bi

- linearly interpolated to an observation site (usually Knoxville, Tennessee or Charlottesville, Virginia) and the comparison v

alues are the actual reported station values.

Two other verification tools were used for precipitation. One was to interpolate the model 12

- km 24

- hour precipitation amounts to rain gauge positions at GSM and SNP. The other used the 12

- km 6

- hour model

pr ecipitation amounts interpolated to all available NWS sites within the 12

- km domain. These values are then used to calculate rainfall statistics as described by McBride and Ebert (2000) and Schaeffer (1990) which all use the contingency table categories s hown in Table 6

- 1. The categories of correct no

- rain forecasts, false alarms, misses, and hits are denoted by Z, F, M, and H, respectively.

Table 6

- 1. Contingency table categories used for comparing 12

- km 6

- hour model precipitation with 6

- hour
observed values at National Weather Service site locations. Terminology taken from McBride and Ebert (2000)

Predicted

Observed No Rain Rain

No Rain Z F

Rain M H



### (6.5)


### H

M H F PBIAS
+

+

=

(6.6)


### H

M H POD
+ =

(6.7)


### H

F F FAR
+ =

(6.8)


### F

Z Z ANR

+ =

(6.9) 1

− + = ANR POD HK

(6.10) 

# ( ) ( ) ( ) MF

- HZ Z F M H F M MF

- HZ ET
+

+ + + + =

Each of the precipitation statistics will be calculated for thresholds of 0.2, 2, 5, 10, 15, 25, 35, 50, and 75 mm with respect to the observed 6

- hour amounts. The precipitation bias (PBIAS) as defined by (6.5) is the total number of model forecasts of precipitation divided by the total number of observed precipitation events. The precipitation probability of detection (POD) given b y (6.6) is the total number of correct model

### 116

forecasts of precipitation divided by the total number of observed precipitation events. The precipitation false alarm ratio (FAR) defined by (6.7) is the total number of times of model predictions of precipita tion when there was none observed divided by the total number of model forecasts of precipitation. The accuracy for non

- rain events (ANR) given by (6.8) is the total number of correct model forecasts of no precipitation divided by the total number of obse rved no precipitation events. The Hanssen

- Kuipers score (HK) described by (6.9) is a composite of the POD and ANR and has a range of ± 1. Schaeffer (1990) originally introduced a modified critical success index which has become known as the equitable thre at score (ET) and is defined by (6.10). It has a range of +1.0 to

- 1/3. The correct way to verify model grid precipitation forecasts with observations is to have observations of enough density to create an observed analysis on the same grid mesh as the m odel. That was outside the scope of this report so model values are essentially being compared against point measurements. Apart from model precipitation errors this implies that the model will have a low bias on larger observed thresholds which will be especially true when the precipitation is predominately convective in nature.

6.1.1 48

- km Results

Figure 6

- 1 shows the bias and RMSE of temperatures at 2

- m for the 48

- km grid using hourly data for the entire episode. The immediate SAMI region away from the Appalachian mountains generally had bias

values of ± 1 o 

### C but with a warm bias of +2 o 

### C and higher over the Ohio valley and northwestward to the northern Plains and southward to the Gulf coast. The RMSE for temperature was generally 3 o 

### C or less in

most places. In Fig ure

6

- 2 the bias and RMSE of the water vapor mixing ratio at 2

- m are expressed as a percentage of the observed values. A definite dry bias is observed with a large area of the eastern United States having values of 5

- 15 % below observed val ues. This was mainly due to the difficulties of specifying the RAMS soil moisture which was discussed in section 2.4.1. The water vapor bias patterns explain much of the temperature bias. Where the near

- surface mixing ratio is too dry are the same areas with a warm bias and conversely. An example of the latter is over central Georgia where temperatures were cooler than observed and the surface moisture was too wet. The RMSE values for the mixing ratio were generally in the range of 10

- 20 % of the obser ved values. GIT was able to use this episode in the Air Quality Modeling phase by utilizing a boundary layer correction file for the water vapor mixing ratios. This did not change the model precipitation or the cloud water values but did improve the wate r vapor values in the lowest layers. The wind speed bias and RMSE at the level of 10 m in Fig ure 6

- 3 show bias values generally in the ± 0.5 m

s - 1 

### range and RMSE values around 1 m s

- 1 

### . The large bias values over Maine in Fig ure 6

- 3 may be due to boundary problems or some other reason. The wind direction bias in Fig ure 6

- 4 is generally in the range of

± 10 degrees, while the wind direction RMSE approaches 90 o 

### over a large area of the Southeast. One reason for this is that in light wind conditions such as this episode the observed winds are dominated by local terrain effects which can not be duplicated on model grids even at the 12

- km resolution. The comparison of the total model precipitation with the total observed for the episode is given by Fig ure 6

- 5 which confirms the dry bias observed in the near

- surface mixing ratio values. The most extreme dryness in model precipitation was observed over the Kansas

- Missouri area and over the Gulf Coast.

### 117 (a)

(b)

Figure 6

- 1 . (a) Bias of the model minus the observed temperature at 2 m in d egrees Celsius for the July 1995 episode for the 48

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 118 (a)

(b)

Figure 6

- 2 . (a) Bias of the model minus the observed water vapor mixing ratio at 2 for the July 1995 epi sode for the 48

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid. The bias and RMSE are expressed as a percentage of the observed value.

### 119 (a)

(b)

Figure 6

- 3. (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the July 1995 episode
for the 48

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 120 (a)

(b)

Figure 6

- 4. (a) Bias of the model minus the observed wind direction at 10 m in degrees for the July 1995 episode for the 48

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 121 (a)

(b)

Figure 6.5.

(a) Model precip itation in mm for the entire July 1995 episode for the 48

- km grid. (b) Analysis of observed total precipitation for the same period for the 48

- km grid. Analyzed values in excess of 50 mm
have been truncated to 50 mm.

### 122

6.1.2 12

- km Results

Figure 6

- 6 shows the bias and RMSE for the near

- surface variables for Knoxville, Tennessee for the July 1995 episode for the 12

- km grid. Nighttime temperatures at 2 m had a bias on the order of +1 o 

### C and a

minimum cool bias of about

- 2 o 

### C in the afternoon. RMSE values were near 2 o 

### C at night and approached

3 o 

### C in the afternoon. The mixing ratio at 2 m had a dry bias at all hours of around

- 1 to

- 2 g kg

- 1 

### and

RMSE value s of around 2 g kg

- 1 

### . The wind speed bias at 10 m was generally positive with the largest

values of +1 to +2 m s

- 1 

### occurring after midnight and around noon. RMSE values for wind speed were largest at night with values around 2 m s

- 1 

### . The wind direction b ias at 10 m had less of an overall pattern

with values of ± 60 degrees and with perhaps a consistent negative bias in the afternoon hours. RMSE values for the wind direction were largest at night with values approaching 180 degrees. Some of these large va lues are reflective of very light or calm wind conditions. Figure 6

- 7 shows the same variables for Staunton, Virginia which was used in place of Charlottesville, Virginia because of large segments of missing data. The overall patterns were very similar t o Knoxville with the exception of smaller wind speed RMSE

at 10 m.

Table 6

- 2 compares the 24

- h 12

- km precipitation with the daily rain gauge values at GSM and SNP. Model values were reasonable at the Elkmont and Big Meadows sites but the model missed the large values

at the Look Rock site at GSM. Table 6

- 3 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode. The dry bias is clearly evident with all measures of skill having poor values

for thresholds of 5 mm

and above.

### 123 (a)

(b)

Figure 6

- 6. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Kno xville, Tennessee for the period 1200 UTC 9 July

- 1200 UTC 20 July 1995. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is f or temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 1 24 (a)

(b)

Figure 6

- 7 . Diurnal time series of bias (solid dot s) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Staunton, Virginia for the period 1200 UTC 9 July

- 1200 UTC 20 July 1995. Model values are obtained by horizontal interpolation of th e hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed a t 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 125

Table 6

- 2. Midnight to midnight 24

- h precipitation values in inches for the period 11

- 19 July 1995. The Look Rock site is within the Great Smoky Mountain National Park while the Big Meadows site is within the Shenandoah National Park. Model values at the two observation sites were obtained by linear horizontal interpolation of the 12

- km gridpoint data.



### Day Look Rock

Observed Look

Rock

Model Elkmont

Observed Elkmont

Model Big

Meadows
Observed Big Meadows Model



### 11 0.00 0.00 0.00 0.00 0.00 0.00



### 12 0.00 0.00 0.00 0.00 0.00 0.00



### 13 0.00 0.00 0.00 0.00 0.00 0.00



### 14 0.02 0.00 0.00 0.00 0.00 0.00



### 15 0.21 0.01 0.00 0.00 0.00 0.00



### 16 0.92 0.04 0.01 0.03 0.00 0.00



### 17 1.00 0.10 0.02 0.08 0.12 0.06



### 18 0.00 0.03 0.00 0.02 0.00 0.04



### 19 0.00 0.01 0.00 0.00 0.00 0.06



### TOTAL 2.15 0.19 0.03 0.13 0.12 0.16

Table 6

- 3. Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid compared against 6

- hour observed values a t all National Weather Service sites within the 12

- km grid for the July 1995 episode. Statistic abbreviations and definitions are described in the text. Categories
with

- 99 are those where model data did not exist for a calculation. The row labeled "OBS EVENTS" is

the sum of all non

- zero precipitation 6

- hour events across all observation sites for each threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 1.9802 0.2018 0.0247 0 0 0 0 0

- 99

POD 0.2129 0.0263 0 0 0 0 0 0

- 99

FAR 0.8925 0.8696 1

- 99

- 99

- 99

- 99

- 99

- 99

ANR 0.9303 0.9962 0.9996 1 1 1 1 1 1

HK 0.1432 0.0225

- 0.0004 0 0 0 0 0

- 99

ET 0.05 0.02

- 0.00 0.00 0.00 0.00 0.00 0.00

- 99

OBS EVENTS 202 114 81 48 32 19 7 3 0

### 126

6.2 22

- 30 May 1995 Episode Results

6.2.1 9 6

- km Results

Figure 6

- 8 shows the bias and RMSE of 2

- m temperatures for the 96

- km grid using hourly data for the entire episode. This episode was the first to use the surface nudging technique as described in Section

2.4.2.5. As a result the bias values outside of the immediate SAMI region were ± 1 o 

### C with values approaching

- 4 o 

### C over the higher terrain of the Appalachians. The latter feature was an indication of the model decreasing temperatures with height in areas where the observed analysis was domina ted by lower

level observations. RMSE values were generally 0.5

- 1.5 o 

### C in the nudged areas and approached 4.5 o 

### C over the higher terrain of the Appalachians. In Fig ure 6

- 9 the bias and RMSE of the 2

- m water vapor mixing ratio are expressed as a percentage of the observed values. Bias and RMSE values were generally within 2% of the observed values outside of the immediate SAMI region except near the coastlines of the Great Lakes, the Atlantic, and the Gulf of Mexico where the observed analysis is probably in adequate. The

10

- m wind speed bias in Figure 6

- 10 shows errors of ± 0.5 m s

- 1 

### over most of the southeastern United States with larger values over the Great Lakes region and southeastern Canada. The 10

- m wind speed RMSE showed a similar pattern with values near 1 m s

- 1 

### over most of the southeastern United States and with larger values elsewhere. Figure 6

- 11 shows 10

- m wind direction biases generally of ± 20 o 

### with the largest magnitudes in the coastal areas of the mid

- Atlantic and New England and the central G reat Plains. The RMSE wind direction values over the southeastern United States were generally in the range of 40

- 60 o



### . This is the result of the Southeast being dominated by surface high pressure and light wind conditions for much of the episode. The mo del and analyzed observed precipitation for the entire episode for the 96

- km grid are presented in Fig ure 6

- 12. The model simulation reproduced the large areas of precipitation quite

well but over

- predicted precipitation in a west

- east band extending from Iowa eastward to Pennsylvania.

### 127 (a)

(b)

Figure 6

- 8 . ( a) Bias of the model minus the observed temperature at 2 m in degrees Celsius for the May 1995 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 128 (a)

(b)

Figure 6

- 9. (a) Bias of the model minus the observed water vapor mixing ratio at 2 m for the May 1995 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid. The bias and RMSE are expressed as a percentage of the observed value.

### 129 (a)

(b)

Figure 6

- 10 . (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the May 1995 episode
for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 130 (a)

(b)

Figure 6

- 11 . (a) Bias of the model minus the observed wind direction at 10 m in degrees for the May 1995 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 131 (a)

(b)

Figure 6

- 12. (a) Model precipitation in mm for the entire May 1995 episode for the 96

- km grid. (b) Analysis of observed total precipitation for the same period for the 96

- km grid. Analyzed values in exces s of 70 mm have been truncated to 70 mm.

### 132

6.2.2 12

- km Results

Figure 6

- 13 shows the bias and RMSE for the near

- surface variables for Knoxville, Tennessee for the May 1995 episode for the 12

- km grid. For the 2

- m temperatures a generally consistent bias of

- 2 o 

### C was evident across the daily period and with RMSE values of 2

- 4 o 

### C. A consistent bias was observed as well

with the 2

- m mixing ratio of

- 1 g kg

- 1 

### and with R

MSE values of 1

- 2 g kg

- 1 

### . The 10

- m wind speed bias was

near ± 0.5 m s

- 1 

### during the daytime and with a positive values at night which approached 1.5 m s

- 1 

### . The RMSE values for the wind speed were generally in the range of 1

- 2 m s

- 1 

### across the day. The win d direction bias and RMSE were the largest during the night with magnitudes of 60 o 

### and larger. Daytime values of the same quantities were generally less than 60 o 

### . The same plots for Staunton, Virginia in Figure 6

- 14 show similar patterns but with larger v alues for the bias and RMSE for the temperature and mixing ratio.

Table 6

- 4 compares the interpolated 12

- km daily precipitation with selected rain gauge sites at GSM and SNP. The model episode totals at Look Rock and Elkmont of about 11 and 9 mm, respect ively, compared well with the observed values at the same respective sites of 4 and 17 mm. At Big Meadows the model episode total of ~3 mm is much smaller than the observed value of near 52 mm. The observed analyses at

96

- km does not show any precipitatio n of this magnitude so this may have been a more local precipitation

event which the model did not simulate.

Table 6

- 5 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode.

The "OBS EVENTS" row indicates that nearly all the 6

- h observed amounts were 15 mm or less. Most of the heavy precipitation for this episode was outside of the 12

- km domain. The bias values show over

prediction for the thresholds of 2 mm and less and under

- prediction of the thresholds of 5 mm and higher. The false alarm ratios were 0.80 or higher for all the thresholds. Inspection of the model and observed 6

- h precipitation amounts for the 96

- km grid (not shown) indicate a tendency for the model to over

- predict the area of light precipit ation which explains part of the false alarm ratio patterns.

### 133 (a)

(b)

Figure 6

- 13. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Knoxville, Tennessee for the period 1200 UTC 22 May 1995 to 1200 UTC 30 May 1995. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 134 (a)

(b)

Figure 6

- 14. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Staunton, Virginia for the period 1200 UTC 22 May 1995 t o 1200 UTC 30 May 1995. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1

. (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 135 Table 6

- 4. Midnight to midnight 24

- h precipitation values in mm for the period 24

- 29 May 1995. The Look
Rock and Elkmont sites are within the Great Smoky Mountain National Park while the Big Meadows site is within the Shenandoah National Park. Model values at the observation sites were obtained by linear horizontal interpolation of the 12

- km gridpoint data.

Day Look Rock

Observed Lo ok Rock

Model Elkmont

Observed Elkmont

Model Big

Meadows
Observed Big Meadows

Model

24 0.00 0.00 0.00 0.00 0.00 0.00

25 0.00 2.90 0.00 1.79 44.50 0.80

26 0.00 1.79 0.00 3.63 0.00 1.29

27 0.80 3.88 8.40 1.73 0.00 0.04

28 1.30 0.09 8.40 0.27 6.90 0.9 8

29 1.50 2.00 0.00 1.81 0.50 0.02

TOTAL 3.60 10.66 16.80 9.23 51.90 3.13

Table 6

- 5. Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid compared against 6

- hour observed values at all National Weather Service sites within the 12

- km grid for the May 1995 episode. Statistic abbreviations and definitions are described in the text. Categories with

- 99 are those where model data did not exist for a calculation. The row labeled "OBS EVENTS" is the sum

of all non

- z

ero precipitation 6

- hour events across all observation sites for each threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 3.11 1.38 0.56 0.54 0.42 0.25 0.00 0.00

- 99.0

POD 0.59 0.23 0.09 0.02 0.00 0.00 0.00 0.00

- 99.0

FAR 0. 81 0.84 0.84 0.96 1.00 1.00

- 99.0

- 99.0

- 99.0

ANR 0.77 0.94 0.99 0.99 1.00 1.00 1.00 1.00 1.00

HK 0.37 0.17 0.08 0.02

- 0.00

- 0.00 0.00 0.00

- 99.0

ET 0.10 0.08 0.05 0.01

- 0.00

- 0.00 0.00 0.00

- 99

OBS EVENTS 311 176 110 46 19 4 1 1 0

### 136

6.3 9

- 18 May 199 3 Episode Results

6.3.1 96

- km Results

Figure 6

- 15 shows the bias and RMSE of 2

- m temperatures for the 96

- km grid using hourly data for the entire episode. Again, because of the surface nudging technique the bias outside of the immediate SAMI region and higher terrain was ± 1 o 

### C. Bias values as large as

- 5 o 

### C were observed over parts of the Appalachians. As mentioned in other episodes the negative bias over higher terrain is an indication of the model being cooler as it should be than the observed analysis which was dominated by sites not in high terrain. The cool bias in lower terrain areas where surface nudging was not performed is, among other things, the result of the inability to specify soil moisture adequately. The RMSE values showed a similar
patt ern with values of 1 o 

### C outside of the immediate SAMI region and values as high as 5 o 

### C in higher terrain areas. In Fig ure

6

- 16 the bias and RMSE of the 2

- m water vapor mixing ratio are expressed as a percentage of the observed values. Bias and RMSE values were generally within 2% of the observed values outside of the immediate SAMI region except near the coastlines of the Great Lakes. The 10

- m wind speed bias in Fig ure

6

- 17 was generally in the range of ± 0.50 m s

- 1 

### but with values as large as +2.5 m

s - 1 

### in southeastern Canada. RMSE values were generally under 2 m s

- 1 

### over most of the eastern United

States. The 10

- m wind direction bias in Fig ure 6

- 18 was generally in the range ± 10 degrees but with values as large as +20 degrees over two regions: one over p arts of Oklahoma, Kansas, Arkansas, and Mississippi, and the other over Iowa. RMSE values were generally in a range of 20

- 50 degrees except for

a west

- east band of near 60 degrees from eastern Oklahoma northeastward to the Mid

- Atlantic. This was

coincide nt with the frontal position which was in that vicinity for much of the week. Winds are typically light near a stationary front and therefore are more difficult to model. The total model and observed precipitation
on the 96

- km grid are shown in Fig ure 6

- 19. The large

- scale patterns were reasonably simulated by the model but precipitation was over

- predicted along the Ohio River Valley across parts of southern Illinois, Indiana and Ohio and northern Kentucky.

### 137 (a)

(b)

Figure 6

- 15. (a) Bias of the model minus the observed temperature at 2 m in degrees Celsius for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field fo r the same time and grid.

### 138 (a)

(b)

Figure 6

- 16 . (a) Bias of the model minus the observed water vapor mixing ratio at 2 m for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

The bias a nd RMSE are expressed as a percentage of the observed value.

### 139 ( a)

(b)

Figure 6

- 17. (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the May 1993 episode
for the 96

- km grid. (b) Root mean square error (RMSE) of the same fie ld for the same time and grid.

### 140 (a)

(b)

Figure 6

- 18. (a) Bias of the model minus the observed wind direction at 10 m in degrees for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 141 (a)

(b)

Figure 6

- 19. (a) Model precipitation in mm for the entire May 1993 episode for the 96

- km grid. (b) Analysis of observed total pre cipitation for the same period for the 96

- km grid. Analyzed values in excess
of 70 mm have been truncated to 70 mm.

### 142

6.3.2 12

- km Results

Figure 6

- 20 shows the bias and RMSE for the near

- surface variables for Knoxville, Tennessee for the May 1993 episode for the 12

- km grid. A fairly consistent bias of near

- 2 o 

### C wa s observed with RMSE values generally from 2

- 6 o 

### C. A dry bias of about 2 g kg

- 1 

### existed across the hours with RMSE values approaching 3 g kg

- 1 

### at times. Wind speed biases were on the order of +1 m s

- 1 

### at night but near zero in the afternoon

with RMSE valu es of 1

- 2 m s

- 1 

### . Wind direction biases had no clear diurnal pattern with values generally in the range of ± 30 degrees. RMSE values were as large as 120 degrees with the largest values at night when winds are light or calm.

The results for Martinsburg, West Virginia in Fig ure 6

- 21 show overall different patterns. Temperature biases were negative at night and decreased to about

- 3 o 

### C in the early morning hours but were near zero for the afternoon. RMSE were as large as 5 o 

### C in the early afternoon. The m ixing ratio bias was near zero for all hours and RMSE values were generally 2 g kg

- 1 

### or less. Wind speed biases were in the range of ± 1 m s

1 

### with a negative bias at night and a positive bias during the day. RMSE values for wind speed were 2 m s

- 1



### or les s. Wind direction biases were the largest around noon with values of +60 to +90 degrees. At other times the wind direction values were in the range of ± 30 degrees. RMSE values also were the largest in the afternoon with values around 90 degrees.

Table 6

- 6 compares the interpolated 12

- km daily precipitation with selected rain gauge sites at GSM and SNP. The model episode totals at Look Rock and Elkmont of about 12 and 17 mm, respectively, were smaller than the observed values at the same respective site s of 24 and 34 mm. At Big Meadows the model episode total near 11 mm was also smaller than the observed value of near 36 mm.

Table 6

- 7 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode.

The "OBS EVENTS" ro w indicates that nearly all the 6

- h observed amounts were 15 mm or less. The bias

values show over

- prediction for the thresholds of 2 mm and less and under

- prediction of the thresholds of 5 mm and higher. The false alarm ratios were 0.72 or higher for al l the thresholds.

### 143 (a)

(b)

Figure 6

- 20. Di

urnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Knoxville, Tennessee for the period 1200 UTC 09 May 1993 to 1200 UTC 19 May 1993. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 144 (a)

(b)

Figure 6

- 21. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation fo r Martinsburg, West Virginia for the period 1200 UTC 09 May 1993 to 1200 UTC 19 May 1993. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. ( a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 145 Table 6

- 6 . Midnight to midnight 24

- h precipitation values in mm for the period 10

- 17 May 1993. The Look Rock and Elkmont sites are within the Great Smoky Mountain National Park wh ile the Big
Meadows site is within the Shenandoah National Park. Model values at the two observation sites were obtained by linear horizontal interpolation of the 12

- km gridpoint data.

Day Look Rock

Observed Look

Rock

Model Elkmont

Observed Elkmont

Model Big

Meadows
Observed Big

Meadows

Model

10 0.0 1.9 0.0 2.6 0.0 1.4

11 2.5 2.7 0.0 2.3 0.5 0.9

12 0.0 2.7 0.0 2.3 10.9 1.8

13 14.0 1.3 29.2 8.1 2.5 3.7

14 0.0 0.8 5.1 0.5 0.0 0.0

15 7.4 0.0 0.0 0.0 0.0 0.0

16 0.0 0.2 0.0 0.0 21.8 3.3

17 0.0 2. 6 0.0 1.6 0.0 0.0

Total 23.9 12.2 34.3 17.4 35.7 11.1

Table 6

- 7 . Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid compared against 6

- hour observed values at all National Weather Service sites within the 12

- k m grid for the May 1993 episode. Statistic abbreviations and definitions are described in the text. Categories
with

- 99 are those where model data did not exist for a calculation. The row labeled "OBS EVENTS" is

the sum of all non

- zero precipitation 6

- h our events across all observation sites for each threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 2.86 1.21 0.72 0.42 0.26 0.00 0.00

- 99

- 99

POD 0.66 0.33 0.18 0.06 0.00 0.00 0.00

- 99

- 99

FAR 0.77 0.72 0.75 0.86 1.00

- 99

- 99

- 99

- 99

ANR 0.80 0.96 0.99 1.00 1.00 1.00 1.00 1.00 1.00

HK 0.46 0.29 0.17 0.05

- 0.00 0.00 0.00

- 99

- 99

ET 0.14 0.15 0.11 0.04

- 0.00 0.00 0.00

- 99

- 99

OBS EVENTS 364 210 111 52 27 11 2 0 0



### 6.4 21 March to 1 April 1993 Episode Results

6.4.1 96

- km

Results

Figure 6

- 22 shows the bias and RMSE of the 2

- m temperatures for the 96

- km grid using hourly data for the entire episode. Again as the result of the surface nudging technique bias values were in the range of ± 1 o 

### C outside of the immediate SAMI reg ion. Bias values approached

- 5 o 

### C over the higher terrain of the southern Appalachians. Consistent with other episodes, the observed analysis used in calculating the bias is based on observations which are almost entirely outside of the higher terrain of the Appalachians. As a result when temperatures on average are decreasing with height one would expect the model to be cooler than the "observed analysis" over mountainous areas. RMSE values were usually 1 o 

### C or less with higher values approaching 5

o 

### C ove r parts of the Appalachians. In Fig ure 6

- 23 the bias and RMSE of the 2

- m water vapor mixing ratio are expressed as a percentage of the observed values. Bias and RMSE values were generally within a range of ± 5 % outside of the immediate SAMI region and area s close to the Great Lakes.

### 146

Bias values over the southern Appalachians were in the range of

- 5 to

- 10%. The RMS E values were generally under 5

% outside the immediate SAMI region and were in the range of 5 to 15 % over the Appalachians. The 10

- m wind spe ed bias in Figure 6

- 24 showed most of the eastern United States with

values of +0.5 to +1.5 m s

- 1 

### . RMSE values for wind speed were generally under 2 m s

- 1 

### . The 10

- m wind direction bias in Fig

ure 6

- 25 shows values generally in the range of +5 to +20 degre es. The RMSE values for wind direction were the largest in a southwest to northeast corridor from Louisiana to New England with largest values being near 70 degrees over the Appalachians in western Pennsylvania. The total model and observed precipitation

for the episode are given in Figure 6

- 26. The model simulated the large

- scale patterns in a reasonable manner with a possible over

- prediction of precipitation across western portions of the Carolinas and Virginia.

### 147 (a)

(b)

Figure 6

- 22. (a) Bias of the model minus the observed temperature at 2 m in degrees Celsius for the March 1993 episode for the 96

- km gri d. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 148 (a)

(b)

Figure 6

- 23 . (a) Bias of the model minus the observed water vapor mixing ratio at 2 m for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the s ame field for the same time and grid. The bias and RMSE are expressed as a percentage of the observed value.

### 149 (a)

(b)

Figure 6

- 24. (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the sa me field for the same time and grid.

### 150 (a)

(b)

Figure 6

- 25 . (a) Bias of the model minus the observed wind direction at 10 m in degrees for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 151 (a)

(b)

Figure 6

- 26. (a) Model precipitation in mm for the entire March 1993 episode for the 96

- km grid. (b) Analysis of observed total precipitation for the same period for the 96

- km grid.

### 152

6.4.2 12

- km Results

Figure 6

- 27 shows the bias and RMSE for the near

- surface variables for Knoxville, Tennessee for the March 1993 episode for the 12

- km grid. A bias of

- 2 to

- 4 o 

### C for the 2

- m tempe rature was evident for most hours except early in the daytime when values were close to zero. RMSE values for the 2

- m temperature were 6 o 

### C or less. Mixing ratio biases at 2 m were fairly consistent, being in the range of

- 0.5

to

- 1 g kg

- 1 

### . RMSE for the mixing ratio were 1 g kg

- 1 

### or less. The wind speed bias at 10 m was usually in the range of +0.50 to +1 m s

- 1 

### with RMSE values as large as 2 m s

- 1 

### . Wind direction biases were usually in the range of ± 30 degrees with RMSE values as large as 90 degrees. T he same plots for Martinsburg, West Virginia in Figure

6

- 28 were similar except for larger RMSE values for temperature.

Table 6

- 8 compares the interpolated 12

- km daily precipitation with selected rain gauge sites at GSM and

SNP. The model over

- predicted total precipitation at Look Rock and Elkmont and under

- predicted total precipitation at Big Meadows.

Table 6

- 9 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode. The "OBS EVENTS" row indicates that nearly all the 6

- h observed amounts were 25 mm or less. The bias

values indicate some skill for thresholds of 10 mm and below.

### 153 (a)

(b)

Figure 6

- 27. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Knoxville, Tennessee for the period 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Model val ues are obtained by horizontal interpolation of the hourly

12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio a t 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 154 (a)

(b)

Figure 6

- 28 . Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks)

for the model minus the station observation for Martinsburg, West Virginia from 1200 UTC 21 March 1993 to 1200 UTC 1 April 1993. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CD T) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 155 Table 6

- 8. Midnight to midnight 24

- h precipitation values in inches for the pe riod 23

- 31 March 1993. The Look Rock and Elkmont sites are within the Great Smoky Mountain National Park while the Big
Meadows site is within the Shenandoah National Park. Model values at the two observation sites were obtained by linear horizontal inter polation of the 12

- km gridpoint data.

Day Look Rock

Observed Look

Rock

Model Elkmont

Observed Elkmont

Model Big

Meadows
Observed Big

Meadows

Model

23 18.03 10.90 25.90 19.40 6.40 13.20

24 1.00 1.40 0.50 2.50 4.80 5.00

25 0.00 12.90 0.50 13.90 0. 00 0.40

26 14.20 17.70 10.20 20.70 0.00 0.30

27 12.40 6.60 15.20 8.60 30.70 27.30

28 3.60 5.70 1.80 6.30 6.90 3.00

29 0.00 0.00 0.00 0.00 18.80 0.00

30 0.00 0.00 0.00 0.00 0.80 0.00

31 6.90 17.2 1.30 14.80 6.40 8.10

TOTAL 56.13 72.40 55.40 86.20 74. 80 57.30

Table 6

- 9. Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid compared against 6

- hour observed values at all National Weather Service sites within the 12

- km grid for the March 1993 episode. Statistic abbreviations and definitions are described in the text. Categories with

- 99 are those where model data did not exist for a calculation. The row labeled "OBS
EVENTS" is the sum of all non

- zero precipitation 6

- hour events across all observation sites for each
threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 1.92 1.35 1.21 0.91 0.60 0.16 0.00 0.00

- 99

POD 0.72 0.49 0.29 0.10 0.03 0.00 0.00 0.00

- 99

FAR 0.63 0.64 0.76 0.89 0.94 1.00

- 99

- 99

- 99

ANR 0.72 0.90 0.94 0.97 0.99 1.00 1.00 1.00 1.00

HK 0.44 0.38 0.24 0.08 0.03

- 0.00 0.00 0.00

- 99

ET 0.19 0.20 0.12 0.04 0.02

- 0.00 0.00 0.00

- 99

OBS EVENTS 1030 577 314 162 87 32 8 1 0



### 6.5 6

- 14 February 1994 Episode Results

6.5.1 96

- km Results

Figure 6

- 29 shows the bias and RM SE of the 2

- m temperatures for the 96

- km grid using hourly data for the entire episode. Bias values were mainly in the range of ± 1 o 

### C in all areas. The typical "cool bias" over the higher terrain in the Appalachians observed in other episodes is not evide nt here. This is possibly because of the nature of strong shallow Arctic air masses where a strong inversion exists most of the time such that temperatures increase rather than decrease with height near the surface. RMSE values for the 2

- m temperature wer

e generally 1 o 

### C or less outside of the immediate SAMI region but were as large as 5 o 

### C over the Appalachians and near the Great Lakes. Figure 6

- 30 shows the bias and RMSE of the 2

- m water vapor mixing ratio expressed as a percentage of the observed values. Bias values were generally in the range of ± 2% away from coastal areas and the Great Lakes. RMSE values were usually below 4% except

### 156

again for coastal areas and the Great Lakes. Reasons for the higher values for the bias and RMSE in the

coastal areas an

d the Great Lakes include the following : 1) the observations used to create the observed analysis may not provide the required res olution in those areas, and 2) for the Great Lakes it was outside the scope of this project to acquire actual water temperatu res and ice coverage which both effect the surface evaporation and, therefore, the 2

- m water vapor mixing ratios. The 10

- m wind speed bias and RMSE in Fig

ure 6

- 31 both show in general values of +2.5 m s

- 1 

### or less. The 10

- m wind direction bias in Fig ure 6

- 32 shows values generally in the range of

- 5 to +25 degrees, with the largest values being over the Appalachians, New York, and parts of Illinois, Missouri, and Iowa. The large differences over the Appalachians are indeterminate because of the lack of ob servations. The wind direction RMSE values were typically 40

- 60 degrees but as large as 90 degrees in some locations. The total model and observed precipitation are given in Fig ure 6

- 33. The model reproduced the large

- scale patterns well with the possib le exception of over

- predicting precipitation over the southern Appalachians.

### 157 (a)

(b)

Figure 6

- 29. (a) Bias of the model minus the observed temperature at 2 m in degrees Celsius for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 158 (a)

(b)

Figure 6

- 30 . ( a) Bias of the model minus the observed water vapor mixing ratio at 2 m for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid. The bias and RMSE are expressed as a percentage of t he observed value. Values in excess of 10%
have been truncated to 10%

### 159 (a)

(b)

Figure 6

- 31 . (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same fie ld for the same time and grid.

### 160 (a)

(b)

Figure 6

- 32 . (a) Bias of the model minus the observed wind direction at 10 m in degrees for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 161 (a)

(b)

Fig ure 6

- 33. (a) Model precipitation in mm for the entire February 1994 episode for the 96

- km grid. (b) Analysis of observed total precipitation for the same period for the 96

- km grid.

### 162

6.5.2 12

- km Results

Figure 6

- 34 shows the bias and RMSE for the near

- surfa ce variables for Knoxville, Tennessee for the February 1994 episode for the 12

- km grid. Temperatures at 2 m showed a fairly consistent warm bias of

about +1 o 

### C with RMSE values of 2

- 4 o 

### C. The 2

- m water vapor mixing ratio showed a consistent dry bias

of abo ut

- 0.50 g kg

- 1 

### with RMSE values of 1 g kg

- 1 

### or less. The 10

- m wind speed revealed a fairly uniform

bias of 1

- 2 m s

- 1 

### with RMSE values of 3 m s

- 1 

### or less. The wind direction bias had no clear pattern with

values of

- 60 to +30 degrees and with RMSE values as large as 90 degrees. The patterns for Martinsbug, West Virginia in Figure

6

- 35 were very similar except for a temperature bias closer to zero.

Table 6

- 10 compares the interpolated 12

- km daily precipitation with selected rain gauge sites at GSM and

SN P. The model under

- predicted total precipitation by about 12 mm at Look Rock and over

- predicted by about 23 mm at Elkmont. The comparison at Big Meadows was good with the model under

- predicting the total precipitation by about 4 mm. Comparisons for each day were also quite reasonable.

Table 6

- 11 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode. The "OBS EVENTS" row indicates that nearly all the 6

- h observed amounts were 25 mm or less. The bias

values i ndicate some skill for thresholds of 10 mm and below. The overall patterns of the bias (BIAS), false alarm ratio (FAR), and accuracy of no rain (ANR) indicate a tendency for the model to develop large

- scale precipitation patterns correctly, but to over

- pr edict the amounts and area of smaller precipitation amounts and the timing and location of heavier amounts.

### 163 (a)

(b)

Figure 6

- 34. Diurnal time series of bias (solid dots) and root mean s quare error (vertical bars with horizontal hash marks) for the model minus the station observation for Knoxville, Tennessee for the 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m i n m s - 1 while the bottom plot is for wind direction at 10 m in degrees.

### 164 (a)

(b)

Figure 6

- 35 . Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Martinsbur g, West Virginia for the period 1200 UTC 6 February 1994 to 1200 UTC 14 February 1994. Model values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees

.

### 165 Table 6

- 10. Midnight to midnight 24

- h precipitation values in mm for the period 8

- 13 February 1 994. The Look Rock and Elkmont sites are within the Great Smoky Mountain National Park while the Big
Meadows site is within the Shenandoah National Park. Model values at the two observation sites were obtained by linear horizontal interpolation of the 1 2

- km gridpoint data.

Day Look Rock

Observed Look Rock

Model Elkmont

Observed Elkmont

Model Big

Meadows
Observed Big

Meadows

Model

8 0.80 1.30 2.00 3.20 6.40 0.60

9 24.90 21.90 24.10 26.40 10.90 0.10

10 52.80 40.00 33.80 47.50 6.90 5.00

11 30.2 0 33.40 28.70 33.80 17.80 34.00

12 0.30 0.00 0.00 0.00 1.30 0.10

13 1.00 1.60 1.30 2.10 1.50 1.10

Total 110.0 98.20 89.90 113.00 44.80 40.90

Table 6

- 11. Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid

com pared against 6

- hour observed values at all National Weather Service sites within the 12

- km grid for the February 1994 episode. Statistic abbreviations and definitions are described in the text. Categories with

- 99 are those where model data did not exis t for a calculation. The row labeled "OBS
EVENTS" is the sum of all non

- zero precipitation 6

- hour events across all observation sites for each
threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 1.57 1.08 0.89 1.09 0.75 0.32 0.14 0.0

- 99

POD 0.76 0.56 0.38 0.29 0.16 0.05 0.00 0.00

- 99

FAR 0.52 0.48 0.57 0.73 0.78 0.83 1.00

- 99

- 99

ANR 0.79 0.93 0.96 0.98 0.99 1.00 1.00 1.00 1.00

HK 0.55 0.49 0.34 0.27 0.15 0.05

- 0.00 0.00

- 99

ET 0.30 0.31 0.22 0.15 0.10 0.04

- 0.00 0.00

- 9 9

OBS EVENTS 790 473 304 116 61 19 7 1 0

### 166

6.6 21 July

- 1 August 1991 Episode Results

6.6.1 96

- km Results

Figure 6

- 36 shows the bias and RMSE of the 2

- m temperatures for the 96

- km grid using hourly data for the entire episode. Again as the result of the surface nudging technique bias values were mainly in the range of

± 1 o 

### C in all areas outside of the immediate SAMI region. Bias values approached

- 4 o 

### C over the higher terrain of the Appalachians. The RMSE values followed a similar pattern with values 1 o 

### C or less outside of the immediate SAMI region but with values up to 4 o 

### C over higher terrain. Again, as mentioned before, the larger magnitude of the bias and RMSE values over the higher terrain of the Appalachians is partially the

result of the observ

ed analysis being dominated by observations not located in high terrain areas. For those areas outside of the highest terrain but still in the area where nudging was not performed with regard to temperature the larger errors are the result, among other th ings, of the inability to specify soil moisture correctly. The bias and RMSE of the 2

- m water vapor mixing ratio in Fig ure 6

- 37 show that most areas

had bias values in the range of

- 2 to

- 4 % of the observed values. RMSE values followed a similar pattern with values generally 2% or less outside the Appalachians and up to 12% in the higher terrain areas. The

10

- m wind speed bias in Fig ure 6

- 38 was generally in the range of ± 0.5 m s

- 1 

### but with values approaching

+3 m s

- 1 

### near the Atlantic coast. RMSE value s followed a similar pattern with most areas having values of 1

m s - 1 

### or less but values as high as 5 m s

- 1 

### on the Carolina coasts. The larger errors near the Atlantic coast could be the result of model winds which were too strong or insufficient data use d for the observed analysis to define the coastal winds adequately. The 10

- m wind direction bias in Figure 6

- 39 showed values in most

areas of ± 10 degrees but with higher values in parts of the Mississippi Valley and along the East coast.

RMSE values fol

lowed a similar pattern with values typically at 60 degrees or less but with values as large as 100 degrees over parts of Virginia and North Carolina. The area with largest wind direction errors was inland from the area with larger wind speed errors. Com parison with the mean observed wind speed at 10 m (not shown) indicates that much of the area with the largest RMSE wind direction errors occurred in regions with mean wind speeds of 2 m s

- 1 

### or less. As indicated before, the wind direction in light wind

r egimes is dependent on local terrain which could not be duplicated at the resolutions used for this project. The total model and observed precipitation are shown in Fig ure 6

- 40. The model simulated the overall precipitation patterns well but overestimate d precipitation amounts compared to the observed analysis. The model precipitation is discussed more fully in Section 6.6.2 for the 12

- km results.

### 167 (a)

(b)

Figure 6

- 36. (a) Bias of t he model minus the observed temperature at 2 m in degrees Celsius for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 168 (a)

(b)

Figure 6

- 37. (a) Bias of the model minus the observed water v apor mixing ratio at 2 m for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid. The bias and RMSE are expressed as a percentage of the observed value.

### 169 (a)

(b)

Figure 6

- 38 . (a) Bias of the model minus the observed wind speed at 10 m in m s
- 1 for the July 1991 episode
for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 170 (a)

(b)

Figure 6

- 39. (a) Bias of the model minus the observed wind direction at 10 m in degrees for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 171 (a)

(b)

Figure 6

- 40. (a) Model precipitation in mm for the entire July 1991 episode for the 96

- km grid. (b) Analysis

of observed total precipitation for the same period for the 96

- km grid. Values in excess of 200 mm have
been truncated to 200 mm.

### 172

6.6.2 12

- km Results

Figure 6

- 41 shows the bias and RMSE for the near

- surface variables for Knoxville, Tennessee for the July 1991 episode for the 12

- km grid. A bias of

- 2 to

- 4 o 

### C for the 2

- m temperature was evident for the nighttime hours and a bias of

- 1 to

- 2 o 

### C du ring the daytime with RMSE values being the largest at night with values up to 4 o 

### C. A consistent dry bias of

- 1 to

- 2 g kg

- 1 

### was observed with RMSE values up to 2 g kg

- 1



### . A positive wind speed bias of about 0.50 m s

- 1 

### was observed at night but with a bia s near zero during the daytime. RMSE values for the wind speed were 1 m s

- 1 

### or less. The wind direction bias had no clear pattern and was typically in the range of ± 30 degrees with RMSE values up to 90 degrees. The patterns for the Martinsburg, West Virg inia location in Fig

ure 6

- 42 were similar with the following exceptions: 1) a moist bias up to 2 g kg

- 1 

### during the daytime, and 2) a positive wind speed bias at night as large as 1 m s

- 1


### and a negative wind speed bias during the day as large as

- 2 m s

- 1



### .

Figure 6

- 43 shows the total model precipitation for the 12

- km grid plus the total observed precipitation observed from the Tennessee Valley Authority network analyzed to portions of the 12

- km grid. The 12

- km model precipitation is reasonable over the nor thern and southern sections of the Tennessee

- North Carolina border but overestimates precipitation over central portions of the latter area where the model had in excess

of 200 mm but observations sh owed only values around 80 mm. The model did duplicate th e observed values in excess of 200 mm over the steeply sloped terrain over extreme western South Carolina. The model area with amounts in excess of 200 mm over parts of Virginia and the Carolinas was probably overdone (as will be seen by the rainfall stat istics later in this section) but locally very heavy rainfall was reported in these areas in the publication Storm Data with amounts of 100

- 150 mm.

Daily rain gauge data were not available for this episode for the Look Rock, Elkmont, and Big Meadows

sit es at GSM and SNP. Table 6

- 12 gives the rainfall statistics for all the NWS sites within the 12

- km domain for the entire episode. The "OBS EVENTS" row indicates that the 6

- h observed amounts covered

all the threshold values. The bias statistic indicates that amounts 10 mm and under were modeled with a high bias . Again the combination of high values for the analysis of no

- rain (ANR) and false alarm ratio (FAR) statistics indicate the model's tendency to get large

- scale patterns of precipitation correct b ut to

over

- predict the mesoscale coverage of precipitation.

### 173 (a)

(b)

Figure 6

- 41. Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) fo r the model minus the station observation for Knoxville, Tennessee for the period 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Model values are obtained by horizontal interpolation of the hourly

12

- km gridpoint values to the observation site. Time (C DT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 1 0 m in degrees.

### 174 (a)

(b)

Figure 6

- 42 . Diurnal time series of bias (solid dots) and root mean square error (vertical bars with horizontal hash marks) for the model minus the station observation for Martinsburg, West Virginia for the period 1200 UTC 21 July 1991 to 1200 UTC 1 August 1991. Mo del values are obtained by horizontal interpolation of the hourly 12

- km gridpoint values to the observation site. Time (CDT) is plotted on the horizontal axis. (a) Top plot is for temperature at 2 m in o C while the bottom plot is for water vapor mixing ratio at 2 m in g kg
- 1 . (b) Top plot is for wind speed at 10 m in m s
- 1 while the bottom plot is for wind direction at 10 m in degrees.

### 175 (a)

(b)

Figure 6

- 43. (a) Model precipitation in mm for the entire July 1991 episode for the 12

- km grid. (b) Analysis of ob served total precipitation for the same period for the 12

- km grid using the Tennessee Valley Authority raingauge network. For both plots values in excess of 200 mm have been truncated to 200 mm.

### 176 Table 6

- 12. Rainfall statistics for various thresholds for 6

- hour model precipitation from the 12

- km grid compared aga inst 6

- hour observed values at all National Weather Service sites within the 12

- km grid for the July 1991 episode. Statistic abbreviations and definitions are described in the text. Categories
with

- 99 are those where model data did not exist for a calcul ation. The row labeled "OBS EVENTS" is

the sum of all non

- zero precipitation 6

- hour events across all observation sites for each threshold. THRESHOLD (mm)

0.2 2 5 10 15 25 35 50 75

STATISTIC

BIAS 2.75 2.59 2.06 1.46 0.82 0.48 0.38 0.42 0.33

POD 0.53 0.34 0.22 0.11 0.05 0.00 0.00 0.00 0.00

FAR 0.81 0.87 0.90 0.92 0.94 1.00 1.00 1.00 1.00

ANR 0.70 0.84 0.91 0.96 0.99 1.00 1.00 1.00 1.00

HK 0.23 0.18 0.13 0.08 0.03

- 0.00

- 0.00

- 0.00

- 0.00

ET 0.07 0.05 0.04 0.03 0.02

- 0.00

- 0.00

- 0.00

- 0.00

OBS EVENTS 577 335 222 127 85 46 29 12 3

### 177

6.7 3

- 12 August 1993 Episode Results

6.7.1 Surface Temperatures

6.7.1.1 Statistical Measures of Near

- Surface Temperatures

Table 6

- 7.1 presents statistical summaries of the modeled and observed near

- surfa ce temperatures on the 12 km and 24 km grids for the 10 modeling days. (In the following analyses we focus on the 12 km results; the 24 km results are presented for completeness.) The daily maximum temperatures during the August episode began at 36.7 ° 

### C on the 3 rd 

### and dropped to a low of 31.7 ° C on the 7 th 

### . Thereafter, a warming trend ensued with the daily maximum temperature rising up to 34.4 ° C on the 10 h 

### . As indicated in Table 6

- 7.1, RAMS did a good job of reproducing the daily maximum temperatures on e ach day. The episode

averages of the daily maximum observed and predicted temperatures were 33.6 ° C and 34.8 ° C, respectively, for a 3.6% discrepancy. Also from Table 6

- 7.1, the mean bias in predicted hourly surface temperatures across the 12 km domain ran ges from

- 0.7 ° C to 0.1 ° C. On average across the August 1993 episode, the mean bias in surface temperature prediction for the RAMS models is

- 0.4 ° C. The average gross errors in surface temperature predictions for the 10 days were 1.6 ° C.

6.7.1.2 Spat

ial Mean Surface Temperatures

Figures 6

- 44 and 6

- 45 present the spatial mean near

- surface temperatures and mean normalized bias in hourly temperatures across the 12 km and 24 km domains as a function of time of day. Considering first the spatial mean tem

perature plot (

Figure 6

- 44 ), we see that RAMS tends to follow the hourly mean temperature measurements fairly well except for a few hours around the time of the daily maximum temperature. At this time, the spatial mean RAMS temperature prediction ‘clips’ the afternoon peaks by a one to two degrees C. RAMS also exhibits a slight temperature overestimation at night. This very high degree of temporal correlation in the spatial mean time series is not normally seen in prognostic model

evaluations.

The exac t cause(s) for the negative daytime (underestimation) and positive nighttime (overestimation) biases are difficult to isolate. Because the near surface predictions from RAMS are taken from grid layer 1 (approximate height of 13 m) one might expect RAMS ni ghttime estimates to be slightly warmer than the measurements (at 2 m) which are influenced by nocturnal radiational cooling at the ground. At midday, the RAMS predictions would be expected to be slightly lower than the measurements during this super

adia batic period, all other factors being equal.

6.7.1.3 Mean Normalized Bias in Surface Temperatures

In Figure 6

- 45 , the mean normalized bias in RAMS’ estimates of hourly near

- surface temperature predictions on the 12 km grid are fairly small throughout th e episode. Typically, RAMS tends to slightly overestimate (~ 3%

- 5%) hourly temperatures in the morning hours and under predict (~2%

- 4%) later in the day. These periods of under and overestimation of near

- surface temperatures, revealed in the hourly bias

### 178

time series plots, are ‘masked’ through the computation of daily average bias statistics (see Table 6

- 7.1) because of the cancellation of positive and negative biases within the diurnal cycle.

6.7.1.4 Surface Temperature Fields

Hourly averaged ground

- level temperature fields for the 12 km domain are shown in Figure 6

- 46 for four representative hours on 3 August 1993. In the figures, the bold numerals represent the measured temperatures in degrees C. Evident in the figures is the cooler air temperatur es over the high terrain of the Appalachian mountains and the warmer areas in coastal environments, particularly during the afternoon periods.

6.7.2 Surface Wind Speed and Direction

6.7.2.1 Statistical Measures of Near

- Surface Wind Speed and Direction

Table 6

- 7.2 presents a variety of statistical measures of RAMS model performance for the winds during the

3

- 12 August 1993 episode. To begin, the table lists the daily mean observed and modeled surface wind speed for the 10 days during the episode. On th e 12 km grid, RAMS estimates mean wind speeds (2.66 m

s - 1 

### ) that are on average more than twice the magnitude of the episode mean observations (1.11 m s - 1 

### ). RAMS overestimates the daily average observed wind speed on each day. Over the entire episode,

RAMS over

- predicts surface wind speeds by 140%. However, it should be noted that on half the days, the average observed wind speed across the 12 km domain is less than 1 ms

- 1 

### . Part of this over

- prediction is expected because the height of the first RAMS grid layer (~20 m) exceeds that of standard anemometer height (10 m). Since wind speeds typically increase rapidly with height within the lowest portion of the boundary layer, the modeled winds in layer 1 should be systematically higher than the measurements.

Modeled wind directions show fairly good agreement with the observations on most days of the August episode. Across the full 10

- day period the mean modeled (178.9 degrees) and observed surface wind directions (203.5 deg

rees ) differ by only 25 degrees. F rom day

- to

- day, the difference between daily average and observed wind direction varies from 4 to 107 degrees. The daily average index of agreement parameter for MM5 modeling ranges between 0.64 and 0.84 with a 10

- day mean of 0.75. These results

are consi stent with those achieved with the MM5 results for summertime ozone episode applications. Table

6

- 7.2 also lists the systematic and unsystematic components of the RMSE errors. For all days the systematic component is larger than the unsystematic component suggesting that more of the RMSE errors are due to systematic biases that might be reduced through further refinement of model algorithms and/or application procedures. One factor contributing to the systematic RMSE is the difference in height above

grou nd between the lowest RAMS level and standard instrument heights.

6.7.2.2 Vector Mean Wind Speeds

Figures 6

- 47 through 6

- 48 present various surface wind speed and wind direction summary plots for the 3

- 12 August 1993 episode. To begin, Figure 6

- 47 giv es the vector mean modeled wind speed (the solid line)

### 179

and the vector mean observed wind speed (the dashed line) for the RAMS model over the 12 km domain. The plot shows that RAMS systematically overestimates the observed mean wind speeds for every day of the episode. For many of the days the vector mean winds on both the 12 km and 24 km grids possess temporal variability similar to that exhibited in the measurements event though the predicted values are systematically biased high. On many of the days, t he model represents the afternoon wind speed increase fairly well notwithstanding the over

- prediction.

6.7.2.3 Mean Wind Direction

Figure 6

- 48 presents the hourly variation in modeled versus observed surface wind directions over the 10 days. Generall y, there is very good agreement with the exception of 8 August and early on the 9 th 

### when the model predicts winds that are 100 degrees or more out of phase with the observations. Overall, however, the agreement with mean wind direction is very good.

6.7 .2.4 Root Mean Square Error in Wind Speeds

The RMSE errors and index of agre ement are shown in Figures 6

- 49 and 6

- 50, respectively. In Figure 6

- 49 , three lines are plotted. The dotted line depicts the unsystematic component of the RMSE (i.e., RMSE U 

### )

wh ile the dashed line (the middle line) corresponds to the systematic component (RMSE S 

### ). The total RMSE error is given by the solid line, always the topmost line in the figure. The episode average values of the unsystematic, systematic, and total RMSE erro rs are 1.28 m s - 1 

### , 1.72 m s - 1 

### and 2.18 m s - 1 

### , respective ly. Examination of Figure 6

- 49 reveals that there does not appear to be any significant error growth throughout the simulation with the exception of 7

- 8 August when the errors are somewhat larger tha n for the rest of the episode days. Also, the fact that most of the RAMS RMSE error is from the systematic component (model physics

- related) is graphically evident in the figure.

6.7.2.5 Index of Agreement in Surface Wind Speeds

The index of agreemen

t (I
a 

### ) results for the A

ugust 1993 episode (Figure 6

- 50 ) are quite consistent with results in other MM5 and RAMS performance evaluations conducted elsewhere in the U.S. The RAMS results exhibits typical hourly variation in the agreement index parameter. The index is lowest during the morning period when wind speeds are lowest and the directions are more variable. In the afternoon when speeds are greater, the index increases. The mean value of I
a 

### over the entire episode is 0.75.

6.7.2.6 Ground

- Level Win d Fields

Figures 6

- 51 presents afternoon RAMS surface wind field comparisons over the 12 km domains on four different days during the August 1993 episode. The bold red vectors in the plots correspond to the observed surface winds; lighter black vectors c orrespond to modeled winds. While there are obviously some locations where the modeled and observed winds do not agree well, for the most part both the RAMS modeled winds do a reasonable job of replicating the observed values.

### 180

6.7.3 Mixing Ratios

6.7 .3.1 Statistical Measures of Near

- Surface Mixing Ratios

Statistical results from the operational evaluation of near surface specific humidity (or mixing ratios) are listed

in Table 6

- 7.3 for the August 1993 episode for the 12 km and 24 km grids. Across the 12 km domain the agreement between the episode mean daily maximum observed (20.1 g/Kg) and modeled (17.2 g/Kg) mixing ratios is fairly good. RAMS systematically underestimates the maximum ratio on each day by 2 to 3 g/Kg. The mean bias and error in m ixing ratios are also quite good, with 10

- day mean values of

- 0.6 g/Kg and 1.1 g/Kg respectively. These results suggest that the model is doing a fairly good job of reproducing the daily maximum and hourly specific humidity across the 12 km domain.

6.7.3 .2 Spatial Mean Surface Mixing Ratios

Figure 6

- 52 presents the spatial mean surface mixing ratios for the August 1993 episode. At both 12 km and 24 km scales, the model does a very good job of estimating the spatial trends in specific humidity

across t he entire episode. The slight tendency to underestimate mixing ratio is evident in the plots. A portion of this underestimation may be attributed to the vertical mismatch between the height of the mixing ratio measurement and the first RAMS grid level fr om which the predicted mixing ratio is derived.

6.7.3.3 Bias in Surface Mixing Ratios

The systematic underestimation of near

- surface mixing ratio is again seen in the normalized bias time series for the 12 km and 24 km gri d domains shown in Figure 6

- 55 . The largest biases in mixing ratio tend to occur during the nighttime hours. At midday, the mixing ratio bias estimates are close to zero on most days. No apparent diurnal performance problems are evident in the mixing ratio results.

6.7.3.4 Surface Mixing Ratio Fields

Figure 6

- 54 presents examples of ground

- level mixing ratio fields across the 12 km domain at two time periods (0300 and 1500 EST) on 7 August. These plots are fairly typical of the entire set of mixing ratio fields for the August 199 3 episode. No strong gradients in mixing ratios are evident in these or the other hourly fields for this episode and there is generally good agreement between the predicted humidity and those observed at the various monitoring stations (represented by the numerals in the figures).

6.7.4 Precipitation

6.7.4.1 Statistical Measures of Total Daily Precipitation

Rain occurred on 8 of the 10 days of the August 1993 epi sode as indicated in Table 6

- 47

. The maximum observed total daily precipitation at any rain gauge varied between 9.0 mm and 119 mm with an episode mean of 50 mm. RAMS predicted a range of rainfall of 11.5 mm to 102.7 mm with an episode mean of

### 181

40.3 mm. The mean observed and predicted precipitation across all rain gauges was 5.3 mm and 6.7 mm, yielding an overall discrepancy of 26% which represents excellent agreement for a mesoscale prognostic model. The bias in daily rainfall predictions ranges from

– 0.30 mm to 10.38 mm with an episode mean of

1.9 mm. Only on 5 August did the model experienc e significant difficulty reproducing the daily rainfall totals. The daily gross errors in rainfall ranged between 0.41 mm and 13.91 mm with an episode mean of 5.8 mm. Overall, the model did an excellent job of simulating the daily and episode average rai nfall totals.

6.7.4.2 Temporal Distribution of Total Daily Rainfall Across All Sites

Figure 6

- 55 presents time series plots of the daily precipitation totals derived from the measured and predicted values averaged across all reporting stations in the 12 km and 24 km domains. With the exception of the modest over

- prediction on 5 August, the day

- to

- day rainfall predictions match the observations very closely in this comparison. We believe, however, that the excellent agreement suggested in the daily to

tal spatial time series plot (Figure 6

- 55 ) paints too optimistic a picture of the model’s replication of the precipitation fields for this episode. The following graphical tools indicate that there are in fact some larger differences between daily average prediction and observation.

6.7.4.3 Spatial Distribution of Daily Total Measured and Observed Rainfall

One of the challenging problems in evaluating meteorological model precipitation predictions is devising a scheme for comparing point rainfall measur ements with spatially distributed (i.e., gridded) mode l predictions. In Figure 6

- 56 we make this comparison by presenting in the top panel the gridded daily precipitation totals (in mm) across the 12 km domain for 7 August 1993. In the bottom panel, we p resent the daily total measurements (also in mm) utilizing the same color coding scheme. Comparing the predicted and observed rainfall totals on 7 August, we see that there is a broad band of predicted precipitation (> 25 mm) across

Tennessee and central K

entucky extending to the east coast. Such a broad swath of precipitation is not evident in the monitors although there are many areas where large voids in the measurement network exist.

From Table 6

- 7.4 we note that on the 7 th 

### , there was a 4.34 mm over

- p rediction bias in the model which might explain a portion of the apparent discrepancy between the modeled field and observed values in the 12 km grid results.

6.7.4.4 Correlation of Daily Maximum Rainfall Across All Sites

Figure 6

- 57 presents a scatter plot of predicted and observed daily total precipitation on 7 August 1993. Comparison of this plot with the spatial t ime series plot in Figure 6

- 55 clearly underscores the need for use of multiple statistical and graphical tools when evaluating the perfo rmance of complex atmospheric models. While the spatial t ime series plot in Figure 6

- 55 suggests very good model performance based on averages across all monitoring stations, t he Scatter Plot in Figure 6

- 57 reveals that this seemingly good agreement

comes as the result of cancellation of model over

- and under

- predictions at the numerous individual monitors. Indeed, visual inspection of the Scatter Plot suggests little if any correlation between the various reporting sites. Thus, while RAMS appears to do a credible job of estimating the total precipitation across the 12 km domain on each day during the August 1993 episode, the spatial distribution of rainfall events

exhibits much less skill.182

Table 6

- 7.1.

RAMS Model Evaluation Results for the 3

- 12 Augus t 1993 SAMI Episode

– Surface Temperatures (deg C). (a) 12 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Max Observ ed Temp. 36.7 33.9 33.9 35.0 31.7 32.2 32.8 34.4 33.3 31.7 33.6

Max. Predicted Temperature 35.0 31.7 34.1 32.4 33.4 36.0 37.1 37.5 37.7 33.2 34.8

Mean Bias (deg C)

- 0.4

- 0.7

- 0.4

- 0.5

- 0.5 0.1

- 0.3

- 0.7

- 0.7 0.5

- 0.4 Gross Error (deg C) 1.9 1.9 1.3 1.6 1.6 1.5 1.7 1.7 1.7 1.3 1.6

Table 6

- 7.1.

RAMS Model Evaluation Results for the 3

- 12 August 1993 SAMI Episode

– Surface Temperatures (deg C). (b) 24 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Max Observed Temperature 37.2 37.2 36.1 36.7 37.2 36.7 36.7 37.2 37.2 36.1 36.8

Max Predicted Temperature 33.5 38.6 36.2 34.6 35.1 35.4 36.8 35.6 36.9 34.8 35.8

Mean Bia s (deg C)

- 0.7

- 0.7

- 0.6

- 0.6

- 0.5

- 0.2

- 0.4

- 0.7

- 0.8 0.2

- 0.5 Gross Error (deg C) 2.0 2.0 1.8 1.9 1.7 1.7 1.8 1.8 1.9 1.4 1.8183

Table 6

- 7.2.

RAMS model evaluation results for the 3

- 12 A ugust 1993 episode

– surface wind speeds (meters/sec). (a) 12 K m Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Mean Observed Wind Speed 2.05 2.02 1.01 0.96 0.74 0.53 1.13 1.16 0. 89 0.65 1.11

Mean Predicted Wind Speed 3.98 3.92 2.92 2.58 2.76 0.93 1.59 2.19 2.62 3.12 2.66

Observed Standard
Deviation 1.91 1.88 1.79 2.38 1.94 1.57 1.58 1.84 1.70 1.38 1.80

Predicted Standard
Deviation 1.74 1.25 1.39 3.07 2.31 1.61 1.73 2.06 1.61 1. 17 1.79

RMSE 2.37 2.21 1.92 2.56 2.54 1.73 1.78 1.96 2.10 2.58 2.18

RMSE

S 1.96 1.84 1.60 1.84 1.91 1.36 1.31 1.51 1.60 2.31 1.72

RMSE

U 1.30 1.20 1.05 1.76 1.66 1.02 1.15 1.22 1.33 1.12 1.28

Index of Agreement, I 0.73 0.70 0.79 0.84 0.79 0.77 0.70 0. 78 0.74 0.64 0.75

Skill E 0.73 0.67 0.62 0.78 0.90 0.66 0.79 0.72 0.84 0.82 0.75

Skill VAR 0.98 0.68 0.81 1.30 1.23 1.06 1.18 1.17 0.97 0.85 1.02

Mean Observed Wind Direction ( O ) 225.7 250.3 299.0 184.8 283.8 82.3 76.2 110.2 128.7 147.8 178.9

Mean Pred icted Wind Direction ( O ) 229.4 231.9 258.5 210.0 267.3 188.9 121.5 150.7 181.1 195.5 203.5184

Table 6

- 7.2.

RAMS model e valuation results for the 3

- 12 A ugust 1993 episode

– surface wind speeds (meters/sec). (b) 24 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Mean Observed Wind Speed 1.99 1.67 1.09 0.57 0.24 0.78 1.27 1.24 1.08 0.90 1.08

Mean Predicted Wind Speed 3.91 3.27 2.56 1.65 0.72 1.61 2.36 2.65 2.65 2.96 2.43

Observed Standard
Deviation 2.21 2.31 2.12 2.33 2.03 1.91 2.13 2.15 2.00 1.53 2.07

Predicted Standard
Deviation 2.37 2.25 1.99 2.85 2.94 2.11 2.30 2.37 2.13 1.93 2.32

RMSE 2.71 2.50 2.21 2.42 2.64 2.00 2.10 2.29 2.39 2.84 2.41

RMSE

S 1.74 1.93 1.78 1.62 1.57 1.27 1.24 1.53 1.68 2.30 1.67

RMSE

U 0.83 1.54 1.28 1.76 2.10 1.51 1.63 1.65 1.69 1.63 1.56

Index of Agreement, I 0.73 0.76 0.79 0.85 0.82 0.81 0.82 0.80 0.76 0.63 0.78

Skill E 0.83 0.67 0.63 0. 79 1.10 0.85 0.79 0.79 0.86 1.07 0.84

Skill VAR 1.11 0.99 0.97 1.26 1.52 1.19 1.11 1.13 1.28 1.27 1.18

Mean Observed Wind Direction ( O ) 235.4 258.8 271.0 231.9 169.2 150.3 153.9 154.4 183.6 148.8 195.7

Mean Predicted Wind Direction ( O ) 227.5 235.0 243.4 218.2 239.0 188.9 180.3 189.5 192.2 188.6 210.3185

Table 6

- 7.3 . RAMS model e valuation results for the 3

- 12 A ugust 1993 SAMI episode

– surface mixing ratios (gm/kg). (a) 12 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

D ay 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Maximum Observed Mixing Ratio 21.8 21.0 19.7 20.6 20.5 20.3 19.6 18.3 18.2 20.8 20.1

Maximum Predicted Mixing Ratio 18.4 17.7 17.4 17.3 17.4 16.9 16. 8 15.7 16.8 17.2 17.2

Mean Bias (gm/Kg) - 0.8

- 0.6

- 0.4

- 0.5

- 0.4

- 0.6

- 0.8

- 0.7

- 0.5

- 0.3

- 0.6

Gross Error (gm/Kg) 1.4 1.1 1.0 1.1 1.0 1.0 1.2 1.1 1.2 1.0 1.1

Table 6

- 7.3 . RAMS model e valuation results for the 3

- 12 A ugust 1993 SAMI episode

– surface mixing ratios (gm/kg). (b) 24 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Maximum Observed Mixing Ratio 24.2 24.0 24.0 24.1 24.1 24.0 23.3 22.0 22.8 22.9 23.5

Maximum Predicted Mixing Ratio 19.2 18.4 18.4 18.3 18.7 18.3 18.5 18.6 19.3 19.7 18.7

Mean Bias (gm/Kg) - 0.5

- 0.4

- 0.3

- 0.3

- 0.4

- 0.5

- 0.6

- 0.5

- 0.5

- 0.1

- 0.4

Gross Error (gm/Kg) 1.2 1.1 1.0 1.0 1.0 1.1 1.2 1.2 1.2 1.2 1.1186

Table 6

- 7.4. RAMS model evaluation results for the 3

- 12 A ugust 1993 SAMI episode

– daily total precipitation (mm). (a) 12 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug

Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Maximum Observed Precipitation 34.0 52.0 85.0 26.0 119.0 52.0 32.0 9.0

-- -- 50.0

Maximum Predicted Precipitation 11.5 37.6 51.6 26.1 102.7 22.9 40.0 30.0

-- -- 40.3

Mean Observed Precipitation 1.5 4.9 4.8 1.2 25.6 3.5 0.9 0.1

-- -- 5.3

Mean Predicted Precipitation 2.4 3.3 14.7 2.5 27.7 2.4 0.6 0.3

-- -- 6.7

Mean Bias (mm) 0.93

- 1.27 10.38 1.33 4.34

- 0.81

- 0.30 0.23

-- -- 1.9

Gross Error (mm) 2.89 6.44 13.91 3.00 16.4 2.35 1.3 0 0.41

-- -- 5.8187

Table 6

- 7.4. RAMS model e valuation results for the 3

- 12 A ugust 1993 SAMI episode

– daily total precipitation (mm). (b) 24 Km Grid

Performance
Attribute 3 Aug

Day 215 4 Aug

Day 216 5 Aug

Day 217 6 Aug

Day 218 7 Aug

Day 219 8 Aug

Day 220 9 Aug Day 221 10 Aug

Day 222 11 Aug

Day 223 12 Aug

Day 224 Mean

Value

Maximum Observed Precipitation 67.0 52.0 85.0 67.0 119.0 67.0 47.0 74.0

-- -- 72.3

Maximum Predicted Precipitation 13.3 31.4 49.3 46.7 97.5 30.0 32.2 24.6

-- -- 40.6

Mean Observe d Precipitation 1.8 3.6 4.1 1.9 18.3 3.6 1.1 0.9

-- -- 4.4

Mean Predicted Precipitation 1.8 2.7 9.4 2.6 18.5 2.1 0.5 0.5

-- -- 4.8

Mean Bias (mm) 0.03

- 0.65 5.92 0.67 2.04

- 1.36

- 0.58

- 0.38

-- -- 0.7 Gross Error (mm) 2.67 4.94 9.96 3.24 12.34 2.91 1.40 1.20

-- -- 4.8

### 188 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 44. Spatial Mean Ground

- Level Temperatures for the 3

- 12 August 1993 Episode.

### 189 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 45. Mean Normalized Bias in Ground

- Level Temperatures for the 3

- 12 August 1993 Episode.

### 190 (a) 0100 UTC (2100 EST)

(b) 0700 UTC (0300 EST)
Figure 6

- 46. Ground

- Level Temperature Fields for 3 August 1993



### .

### 191 (c) 1300 UTC (0900 EST)

(d) 1900 UTC (1500 EST)

Figure 6

- 4 6

. Concluded



### .

### 192 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 47

. Vector Mean Wind Speed for the 3

- 12 August 1993 Episode.

### 193 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 4 8

. Mean Wind Direction for the 3

- 12 August 1993 Episode.

### 194 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 49

. Root Mean Square Error in Ground

- Level Wind Speeds for the 3

- 12 August 1993 Episode.

### 195 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 5 0

. Index of Agreement in Surface Wind Speed for the 3

- 12 August 1993 Episode.

### 196 (a) 4 August 1993, 1900 UTC (1500 EST)

(b) 6 August 1993, 1900 UTC (1500 EST)

Figure 6

- 5 1

. Ground

- Level Wind Fields for the 3

- 12 August 1993 Episode



### .

### 197 (c) 8 August 1993, 1900 UTC (1500 EST)

(d) 10 August 1993, 1900 UTC (1500 EST)

Figu re 6

- 51

. Concluded.

### 198 (a) 12 Km Grid

(b) 24 Km Grid

F igure 6

- 5 2

. Spatial Mean Ground

- Level Mixing Ratios for the 3

- 12 August 1993 Episode.

### 199 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 53

. Mean Normalized Bias in Ground

- Level Mixing Ratios for the 3

- 12 August 1993 Episode.

### 200 (a) 0700 UTC (0300 EST)

(b) 1900 UTC (1500 EST)

Figure 6

- 54

. Ground

- Level Mixing Ratio Fields for 7 August 1993.

### 201 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 55

. Spatial Mean Daily Precipitation (mm) for the 3

- 12 August 1993 Episode.

### 202 (a) Predicted Precipitation (mm )

(b) Measured Precipitation (mm)

Figure 6

- 56

. Daily Precipitation Fields for 7 August 1993.

### 203 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 57

. Scatter Plot of Daily Maximum Rainfall (mm) for 7 August 1993.

### 204

6.8 22

- 29 June 1992 Episode Results

6.8.1 S urface Temperatures

6.8.1.1 Statistical Measures of Near

- Surface Temperatures

Table 6

- 8 gives the statistical results for predicted and observed near

- surface temperatures for the eight modeling days of the 22

- 29 June 1992 episode. The daily maximum t emperatures during the June episode began at 27.8 ° C on the 22 nd 

### and increased to a high of 36.1 ° C on the 26 th 

### , whereupon a cooling trend ensued with the maximum temperatures dropping down to 32.2 ° C on the 28 th 

### . As indicated in Table 6

- 8

, RAMS did a very j ob of reproducing the daily maximum temperatures on each day with the exception of 26 June where the peak was underestimated by 3.4 ° C. The episode

- averages of the daily maximum observed and predicted temperatures were 32.7 ° C and 32.8 ° C, respectively, for a 0.3% discrepancy. Al so from

Table 6

- 8 , the mean bias in predicted hourly surface temperatures across the 12 km domain ranges from

- 1.7 ° C to

- 0.2 ° C. On average across the June 1992 episode, the mean bias in RAMS surface temperature prediction

– 1.1 ° C. The average gross errors in surface temperature predictions for the 8 days were 1.8 ° C.

6.8.1.2 Spatial Mean Surface Temperatures

Figures 6

- 5 8 and 6

- 59 show the spatial mean near

- surface temperatures and mean normalized bias in hourly temperatures as a f

unction of time of day. From the spatial mea n temperature plot (Figure 6

- 58 ), RAMS generally follows the hourly mean temperature measurements fairly well. However, the model systematically underestimates the afternoon spatial mean peak values on each day and this discrepancy appears to worsen as the episode ensues. By the 29 th 

### , the mean under

- prediction at midday is roughly

6 ° C. Recall from Table 6

- 8.1 that the model reproduced the domain

- wide maximum temperature on this day to within 0.3 ° C. In additio n to this systematic tendency to underestimate the average high temperatures across the monitoring network, the model also tends to underestimate the nighttime lows by a degree or so, at least on the 12 km grid. The under

- prediction of the afternoon peaks is more pronounced than the August episode where RAMS clipped the afternoon peaks by a one to two degrees C. The temporal correlation shown in the spatia l mean time series (Figure 6

- 58. ) is more typical of that normally seen in prognostic model evaluation s compared with the August 19 93 episode results (Figure 6

- 44 ). As with the August episode, the cause of daytime underestimation biases is difficult to identify unambiguously but we attribute part of this discrepancy to the difference in heights between me asurement and layer 1 model predictions.

6.8.1.3 Mean Normalized Bias in Surface Temperatures

In Figure 6

- 59 , the mean normalized bias in RAMS estimates of hourly near

- surface temperature predictions on the 12 km grid are fairly small throughout the e pisode although they are slightly larger than

those in the August episode (Figure 6

- 45 ). For the June episode RAMS tends to slightly underestimate (~ 5% to 10%) hourly temperatures in the morning hours and on a couple of days to over predict (~ 2%

- 3%)

mid day. The sustained periods of underestimation of near

- surface temperatures shown in the hourly bias

### 205

time series plots are corroborated by the systematic under

- prediction biases for each day listed in Table 6

- 5 8.

6.8.1.4 Surface Temperature Fields

Hourly averaged ground

- level temperature fields for the 12 km domain are shown in Figure 6

- 60 for four representative hours on 25 June 1992. On this day, the general afternoon warming trend in the southern

states is evident.

6.8.2 Surface Wind Speed and Direction

6.8.2.1 Statistical Measures of Near

- Surface Wind Speed and Direction

Table 6

- 8.2 presents a variety of statistical measures of RAMS model performance for the winds during the

22

- 29 June 1992 episode. On the 12 km grid, RAMS estimates mean wi nd speeds (2.55 m s - 1 

### ) that are on average 66% greater than the magnitude of the episode mean observations (1.54 m s - 1 

### ). RAMS overestimates the daily average observed wind speed on all but one day (28 June). Mean wind speeds for the June episode are high er than those encountered in the August 1993 period discussed previously. Part of

the over

- prediction in surface wind speed is due to the difference between the heights of the surface wind measurements and the height of the first RAMS grid layer.

Modeled wind directions show reasonable agreement with the observations on most days. Across the 8

- day episode, the mean modeled (229.7 deg rees ) and observed surface wind directions (250.3 deg rees ) differ by only 21 degrees. From day

- to

- day, the difference betwee n daily average and observed wind direction varies from 10 to 150 degrees. On the day with the largest wind discrepancy (28 June), a front moved through the region.

The daily average index of agreement parameter for the MM5 model ranges between 0.66 an d 0.89 with an

8

- day mean of 0.75. These results are consistent with those achieved in the August 1993 episode and with other prognostic model applications (see Chapter 8). Table 6

- 8.2 also lists the systematic and unsystematic components of the RMSE erro rs. For all but the first and last days in the episode the systematic component is larger than the unsystematic component suggesting that more of the RMSE errors are due to systematic biases that might be reduced through further refinement of model algori thms and/or application procedures. The episode average RMSE error is 1.89 ms

- 1



### .

6.8.2.2 Vector Mean Wind Speeds

Figures 6

- 61 through 6

- 62 present various surface wind speed and wind direction summary plots for the

22

- 29 June 1992 episode. The vector mean modeled wind speed (Figure 6

- 61 ) is systematically overestimated compared to the observed mean wind speeds for every day of the June episode and this overestimation tendency is quite similar to that encountered in the August episode (Figure 6

- 47 ). For many of the June episode days the vector mean winds on both the 12 km and 24 km grids possess temporal variability similar to that exhibited in the measurements event though the predicted values are systematically

### 206

biased high. On some of the days, the mo del represents the afternoon wind speed increase fairly well notwithstanding the over

- prediction.

### 207

6.8.2.3 Mean Wind Direction

Figure 6

- 62 presents the hourly variation in modeled versus observed surface wind directions over the eight June modeling days. As with the August episode, there is generally very good agreement with the exception of 28 June and early on the 29 th 

### when the model predicts winds that are about 80 to 90 degrees more southerly than the observations. Overall, however, the agreeme nt with hourly mean wind direction across the various monitoring sites is quite good.

6.8.2.4 Root Mean Square Error in Wind Speeds

The RMSE errors and index of agreement are shown in Figures 6

- 63 and 6

- 64 , respectively. The episode average values of

the unsystematic, systematic, and total RMSE errors are 1.24 m s - 1 

### , 1.38 m s - 1 

### and 1.89

m s - 1 

### , respectively. These results are an improvement over the August 1993 episode. From Figure 6

- 63 there does not appear to be any significant error growth through out the latter two thirds of the simulation. Slight error growth occurs during the first three days of the episode when the errors are somewhat larger than for the rest of the episode days. That the preponderance of the RAMS RMSE error is from the
system atic component (model physics

- related) is gra phically evident in Figure 6

- 63

.

6.8.2.5 Index of Agreement in Surface Wind Speeds

The index of agreement results for the June 1992 episode (Figure 6

- 64 ) are quite consistent with other prognostic model e valuations. For this episode, RAMS exhibits typical hourly variation in the agreement index parameter with the lowest values occurring during the morning period when wind speeds are lowest and the directions are more variable. In the afternoon when speed s are greater, the index increases. The mean value of the index of agreement varies diurnally throughout the episode; the mean over the whole period is 0.75

6.8.2.6 Ground

- Level Wind Fields

Figures 6

- 65 presents afternoon RAMS surface wind field compa risons over the 12 km domains on four different days during the June 1992 episode. Generally, the RAMS modeled winds on these days are in reasonable agreement with the observed values. In examining the full set of hourly surface wind plots for this (and the August 1993) episode, we did not find instances where there were substantial discrepancies in the predicted versus observed surface wind fields, which would have raised concerns about the fidelity of the modeling.

6.8.3 Mixing Ratios

6.8.3.1 Stati stical Measures of Near

- Surface Mixing Ratios

Statistical results for near surface mixing ratios are listed in Table 6

- 60 for the June 1992 episode. Across the 12 km domain the agreement between the episode mean daily maximum observed (19.3 g/Kg) and

mode led (16.8 g/Kg) mixing ratios is fairly good. RAMS systematically underestimates the maximum ratio on each day except 22 June by 2 to 4 g/Kg. The mean bias and error in mixing ratios are also quite good,

### 208

with 8

- day mean values of

- 0.3 g/Kg and 1.0 g/Kg r espectively. As with the August 1993 episode, these results suggest that RAMS is doing a fairly good job of reproducing the daily maximum and hourly specific humidity across the 12 km domain.

6.8.3.2 Spatial Mean Surface Mixing Ratios

Figure 6

- 66 is a

spatial mean surface mixing ratio plot for the June 1992. RAMS does an excellent job at both 12 km and 24 km scales in estimating the mean trend in specific humidity across the entire episode. As with the August 1993 episode, there is a slight tendency to underestimate mixing ratio.

6.8.3.3 Bias in Surface Mixing Ratios

The minor underestimation of near

- surface mixing ratio is also apparent in the normalized bias time series for the 12 km and 24 km gri

d domains shown in Figure 6

- 67 . The largest bia ses in mixing ratio tend to occur at night or late in the afternoon or evening. At midday, the mixing ratio bias estimates tend to change from positive to negative during the first five days of the episode. No significant diurnal performance problems

are evident in the mixing ratio results.

6.8.3.4 Surface Mixing Ratio Fields

Figure 6

- 6 8 presents examples of ground

- level mixing ratio fields across the 12 km domain at two time periods (0300 and 1500 EST) on 26 June 1992. These plots are typical of the mixing ratio fields for the June episode. Similar to the August 1993 episode, no strong gradients in mixing ratios are evident in these or the other hourly fields. There is generally good agreement between the predicted and observed mixing

ratios at the various monitoring stations.

6.8.4 Precipitation

6.8.4.1 Statistical Measures of Total Daily Precipitation

Rain occurred on 5 of the 8 days during the June 1992 episode (see Table 6

- 8.4). The maximum daily precipitation at any rain gauge varied betwe en 4.0 mm and 122 mm with an episode mean of 53.8 mm. RAMS predicted a range of daily maximum rainfall totals from 9.0 mm to 110.5 mm with an episode mean of 41.7 mm. The mean observed and predicted precipitation across all rain gauges was 2.9 mm and 3.9 mm, yielding an overall discrepancy of 35% which constitutes good agreement for a mesoscale model. The bias in daily rainfall predictions ranges from

– 0.60 mm to 4.82 mm with an episode mean of 0.9 mm. Only on 27 June did the model experience significan t difficulty reproducing the daily rainfall totals as evidenced by bias and error scores of 4.82 mm and 11.57 mm, respectively. The daily gross errors in rainfall ranged between 0.23 mm and 11.57 mm with an episode mean of 4.0 mm. Overall, the model did a good job of simulating the daily and episode average rainfall totals when averaged across all monitoring stations.

6.8.4.2 Temporal Distribution of Total Daily Rainfall Across All Sites

### 209

Figure 6

- 69 presents time series plots of the daily precipitat ion totals derived from the measured and predicted values averaged across all reporting stations in the 12 km and 24 km domains. With the exception of the 50% over

- prediction on 27 June (see Table 6

- 8.4), the day

- to

- day rainfall predictions

match the obse

rvations very closely in this comparison. However, as with the August 1993 episode, the very good agreement seen in the daily total spatial time series plot (Figure 6

- 69 ) masks much larger errors when the model’s skill at individual monitoring stations on specific days is concerned (see below).

6.8.4.3 Spatial Distribution of Daily Total Measured and Observed Rainfall

Figure 6

- 70 compares the gridded daily precipitation predictions (in mm) across the 12 km domain for 27 June 1992 while the bottom pane l presents the daily total measurements (also in mm). On 27 June 1992, RAMS predicts band s of predicted precipitation (> 25 mm) across portions of northern Alabama, Tennessee and southeastern Kentucky, western Virginia and south Carolina. Data from the spa rse rain gauge network does not show such high rainfall amounts in the same areas as predicted. On the 27 th 

### , (see

Table 6

- 8.4) the model did overestimate the domain

- wide precipitation by 50% so this may explain in part why there are few monitors that are associated with precipitation above 25 mm (i.e., colored red).

6.8.4.4 Correlation of Daily Maximum Rainfall Across All Sites

Figure 6

- 71 presents a Scatter Plot of predicted and observed daily total precipitation on 27 June 1992. While the spatial t

ime series plot in Figure 6

- 69 suggests good model performance (based on averages across all monitoring stations), the Scatter Plot reveals that this good agreement derives from cancellation of

over

- and under

- predictions at the various rain gauges. The Scatter Plot suggests poor correlation between prediction and observation at the individual reporting sites. Thus, as with the August 1993 episode, RAMS does a credible job of estimating the total precipitation when averaged across the monitoring network on each episode day but the model’s ability to predict the exact rainfall amounts paired in time and space with specific monitors is generally poor.211

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAMI Episode

– Surface Temperatures (de g C).

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Temperature 27.8 31.1 33.3 35.0 36.1 32.8 32.2 33.3 32.7

Maximum Pred icted Temperature 30.2 31.5 33.4 33.4 32.7 33.8 34.0 33.0 32.8

Mean Bias (deg C)

- 0.2

- 0.7

- 1.2

- 1.1

- 1.1

- 1.6

- 1.1

- 1.7

- 1.1 Gross Error (deg C) 1.4 1.6 1.8 1.9 2.0 1.9 1.8 2.3 1.8

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAM I Episode

– Surface Temperatures (deg C). (b) 24 Km Grid

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Temperature 38.9 3 4.4 36.1 36.1 36.1 36.7 37.8 35.6 36.5

Maximum Predicted Temperature 32.1 33.2 34.5 33.8 32.3 31.5 32.0 33.8 32.9

Mean Bias (deg C)

- 0.2

- 0.7

- 0.7

- 1.0

- 1.0

- 1.1

- 1.2

- 1.3

- 0.9 Gross Error (deg C) 1.5 1.7 1.7 1.9 2.0 1.9 2.0 2.3 1.9

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 Episode

– Surface Wind Speeds (meters/sec). (a) 12 Km Grid

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Mean Observed Wind Speed 1.92 1.33 2.55 1.66 0.78 1.67 1.31 1.07 1.54

Mean Predicted Wind Speed 2.01 2.54 4.27 3.62 2.68 2.06 1.14 2.06 2.55

Observed Standard
Deviation 2.47 1.65 1.91 1.87 2.04 1.84 1.73 1.61 1.89

Predicted Standard

Dev iation 2.17 1.57 1.48 1.25 1.56 1.47 1.44 1.52 1.56

RMSE 1.52 1.89 2.38 2.12 2.08 1.70 1.61 1.84 1.89

RMSE

S 0.77 1.49 1.86 1.72 1.61 1.36 1.33 0.91 1.38212

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 Episode

– Surface Wind Speeds (meters/sec). (a) 12 Km Grid

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value Index of Agreement, I 0.89 0.74 0.66 0.74 0.81 0.69 0.68 0.79 0.75

Skill E 0.52 0.71 0. 79 0.68 0.70 0.55 0.52 0.78 0.66

Skill VAR 0.88 1.01 0.79 0.69 0.79 0.81 0.84 0.91 0.84

Mean Observed Wind Direction ( O ) 303.1 187.3 210.8 242.5 227.7 312.1 27.3 131.5 250.3

Mean Predicted Wind Direction ( O ) 248.2 222.3 221.0 224.3 224.5 235.4 237.0 224. 5 229.7

Tab le 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 Episode

– Surface Wind Speeds (meters/sec). (a) 12 Km Grid. (b)
24 Km Grid

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Mean Observed Wind Speed 1.33 0.97 1.38 1.30 0.83 0.91 0.57 1.10 1.05

Mean Predicted Wind Speed 2.15 2.41 2.88 2.95 1.84 1.61 1.92 2.65 2.30

Observed Standard
Deviation 2.97 2.35 2.33 2.19 2.34 2.26 2.21 2.15 2.35

Predicted Standard
Deviation 2.81 2.65 2.42 2.07 2.50 2.21 2.39 2.21 2.41

RMSE 1.88 2.21 2.46 2.21 2.30 1.95 2.14 2.20 2.17

RMSE

S 0.80 1.42 1.62 1.56 1.50 1.40 1.37 1.55 1.40

RMSE

U 1.69 1.67 1.81 1.54 1.72 1.35 1.60 1.53 1.61

Inde x of Agreement, I 0.92 0.86 0.79 0.80 0.83 0.84 0.86 0.81 0.84

Skill E 0.57 0.75 0.79 0.75 0.77 0.63 0.74 0.74 0.72

Skill VAR 0.95 1.17 1.05 0.96 1.11 1.10 1.12 1.08 1.07

Mean Observed Wind Direction ( O ) 299.5 231.4 216.2 243.6 277.3 316.5 245.1 182.4 251 .5

Mean Predicted Wind Direction ( O ) 269.6 243.8 233.2 232.4 235.0 241.5 240.2 235.1 241.4213

Table 6

- 8. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAMI Episode

– Surface Mixing Ratios (gm/Kg). (a) 12 Km Grid

Performance
Attribute 22 June

D ay 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Mixing Ratio 12.6 17.9 19.9 21.3 20.7 19.2 19.3 23.4 19.3

Maximum Predicted Mixing Ratio 15.9 16.1 16.4 16. 8 17.9 17.4 17.3 16.7 16.8

Mean Bias (gm/Kg) - 0.3

- 0.2

- 0.2 0.4

- 0.3

- 0.5

- 0.3

- 0.5

- 0.3

Gross Error (gm/Kg) 0.7 0.9 1.1 1.0 1.1 1.0 1.0 1.2 1.0

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAMI Episode

– Surface Mixing Ratios (gm /Kg). (a) 12 Km Grid. (b) 24 Km Grid

Performance
Attribute 22 June Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Mixing Ratio 21.9 24.3 21.3 22.7 21.9 24.5 22.1 23.4 22.8

Maximum Predicted Mixing Ratio 19.8 19.4 19.3 19.3 19.0 19.1 19.2 20.4 19.4

Mean Bias
(gm/Kg) - 0.2

- 0.1

- 0.2

- 0.2

- 0.1

- 0.3

- 0.2

- 0.3

- 0.2

Gross Error
(gm/Kg) 0.7 0.9 1.0 1.0 1.0 0.9 0.9 1.1 0.9214

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAMI Episode

– Daily Total Precipitation (mm). (a) 12 Km Grid

Performance
Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Precipitation -- -- 4.0 27.0 48.0 122.0 68.0

-- 53.8

Maximum Predicted Precipitation -- -- 9.0 20.8 34.0 110.5 34.1

-- 41.7

Mean Observed Precipitation 0.0 0.8 3.5 8.9 1.4 2.9

Mean Predicted Precipitation 0.6 1.9 3.1 13.4 0.7 3. 9

Mean Bias (mm)

-- -- - 0.17 1.06

- 0.60 4.82

- 0.60

-- 0.9 Gross Error (mm)

-- -- 0.23 1.95 4.46 11.57 1.71

-- 4.0

Table 6

- 8

. RAMS Model Evaluation Results for the 22

- 29 June 1992 SAMI Episode

– Daily Total Precipitation (mm). (b) 24 Km Grid

Perform ance Attribute 22 June

Day 174 23 June

Day 175 24 June

Day 176 25 June

Day 177 26 June

Day 178 27 June

Day 179 28 June

Day 180 29 June

Day 181 Mean

Value

Maximum Observed Precipitation -- -- 14.0 35.0 98.0 122.0 68.0

-- 67.4

Maximum Predicted Precipitati

on -- -- 10.1 65.1 56.3 101.0 125.9

-- 71.7

Mean Observed Precipitation 0.2 1.8 3.2 7.0 1.8 2.8

Mean Predicted Precipitation 0.8 1.9 2.7 9.6 0.7 3.1

Mean Bias (mm)

-- -- 0.18 0.00

- 0.65 3.09

- 0.91

-- 0.3 Gross Error (mm)

-- -- 0.42 2.41 4.12 9.01 2.12

-- 3.6215 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 58

. Spatial Mean Ground

- Level Temperatures for the 22

- 29 June 1992.216 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 59

. Mean Normalized Bias in Ground

- Level Temperatures for the 22

- 29 June 1992 Episode.217 

### (a) 0100 UTC (2100 EST)

(b) 0700 UTC (0300 EST)

Figure 6

- 6 0

. Ground

- Level Temperature Fields for 25 June 1992

.218 

### (c) 1300 UTC (0900 EST)

(d) 1900 UTC (1500 EST)

Figure 6

- 60

. Concluded.219 

### (a) 12 Km Grid

(b) 24 Km Gri d

Figure 6

- 61

. Vector Mean Wind Speed for the 22

- 29 June 1992 Episode.220 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 62

. Mean Wind Direction for the 22

- 29 June 1992 Episode.221 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 6 3

. Root Mean S quare Error in Ground

- Level Wind Speeds for the 22

- 29 June 1992 Episode.222 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 64

. Index of Agreement in Surface Wind Speed for the 22

- 29 June 1992 Episode.223 

### (a) 23 June 1992, 1900 UTC (1500 EST)

(b) 25 Ju ne 1992, 1900 UTC (1500 EST)224

Figure 6

- 65

. Ground

- Level Wind Fields for the 22

- 29 June 1992 Episode.



### (c) 27 June 1992, 1900 UTC (1500 EST)

(d) 29 June 1992, 1900 UTC (1500 EST)225

Figure 6

- 6 5

. Concluded.



### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 66

. Spatial Mean Ground

- Level Mixing Ratios for the 22

- 29 June 1992 Episode.226227 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 6 7. Mean Normalized Bias in Ground

- Level Mixing Ratios for the 22

- 29 June 1992 Episode.228 

### (a) 0700 UTC (0300 EST)

(b) 1900 UTC (1500 EST)

Figure 6

- 68

. Ground

- Level Mixing Ratio Fields for 26 June 1992.229 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 69

. Spatial Mean Daily Precipitation (mm) for the 22

- 29 June 1992 Episode.230 

### (a) Predicted Precipitation (mm)

(b) Measured Precipitation (mm)

Figure 6

- 70 . Daily Precipitation Fields for 27 June 1992.231 

### (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 71

. Scatter Plot of Daily Maximum Rainfall (mm) for 27 June 1992.232



### 6.9 24 April

–3 May 1995 Episode Results

6.9.1 Surfa ce Temperatures

6.9.1.1 Statistical Measures of Near

- Surface Temperatures

Table 6

- 72 presents the statistics for predicted and observed near

- surface temperatures for the ten modeling days of the 24 April

–3 May 1995 SAMI episode. The daily maximum temperatures during the episode began at 22.8 ° C on the 24 th 

### and increased to a high of 31.1 ° C on the 30 th 

### . Later, a strong cooling trend developed and the maximum daily temperatures dropped down to 23.3 ° C by 3 May. RAMS did a very job of reproducing the daily maximum temperatures on each day. The episode

- averages of the daily maximum observed and predicted temperatures were 26.9 ° C and 26.6 ° C, respectively, for a 1.1%

dis crepancy. Also from Table 6

- 9 , the mean bias in predicted hourly surface temperature s across the 12 km domain ranges from

- 1.9 ° C to 0.7 ° C. On average across the episode, the mean bias in surface temperature prediction

– 0.8 ° C. The average gross errors in surface temperature predictions for the 10 days were 1.8 ° C, the same as the June 1992 episode.

6.9.1.2 Spatial Mean Surface Temperatures

Figures 6

- 72 and 6

- 73 depict time series plots of the spatial mean surface temperatures and normalized bias in hourly temperatures. From the spatial mea n temperature plot (Figure 6

- 72 ), RAMS fol lows the hourly mean temperatures fairly well but, as with the August 1993 and June 1992 episodes, the model systematically underestimates the afternoon peaks in the latter part of April, but does a good job on the 1 st



### and 2 nd 

### of May. These discrepancies at midday are typically on the order of 2 to 3 ° 

### C. The under

prediction of the afternoon peaks is more pronounced than the August 1993 episode but about the same as the June 1992 episode.

6.9.1.3 Mean Normalized Bias in Surface Temperatures

In Figure 6

- 73 , the mean normalized bias in RAMS’ estimates of hourly near

- surface temperature predictions on the 12 km grid is quite large on the first three days of the episode but begins to diminish and level out as the episode ensues. The hourly temperature bia ses for the April

- May episode are significantly larger than those for the August and June episodes. RAMS tends to underestimate (~ 10% to 15%) hourly temperatures in the afternoon and evening hours and during the morning as well on several days. The

sust ained periods of underestimation of near

- surface temperatures shown in the hourly bias time series plots are corroborated by the systematic under

- prediction biases for most days listed in Table 6

- 9.1.

6.9.1.4 Surface Temperature Fields

Hourly average d ground

- level temperature fields for the 12 km domain are shown in Figure 6

- 74 for four representative hours on 29 April 1995. On this day, lower temperatures are evident in regions of elevated terrain compared to the coastal and interior sections of the Midwest.233



### 6.9.2 Surface Wind Speed and Direction

6.9.2.1 Statistical Measures of Near

- Surface Wind Speed and Direction

Statistical measures of model performance for the winds during the 24 April

- 3 May 1995 episode are listed

in Table 6

- 9 . On the 1 2 km grid, RAMS estimates mean wind speeds (2.98 m s - 1 

### ) that are on average 60% greater than the magnitude of the episode mean observations (1.86 m s - 1 

### ). RAMS overestimates the daily average observed wind speeds during this episode by about the same amoun t as the June 1992 episode. Mean wind speeds are also higher than those encountered in the August 1993 period.

Modeled wind directions show reasonable agreement with the observations on the first five days of the episode and fairly poor agreement on th e last five days. Across the 10

- day episode, the mean modeled (257.0 deg

rees ) and observed surface wind directions (261.4 deg rees ) differ by only 4 degrees. From day

to

- day, however, the discrepancies between daily average and observed wind direction vari es from 2 to 158 degrees. On the 29 th 

### through the 3 rd 

### of May, the mean (absolute) discrepancy between daily averaged modeled and observed wind direction is 131 deg rees . During the latter half of the episode a weak front moved through the eastern U.S. prod ucing rain and unsettled weather, contributing in part to the generally poorer model performance for daily averaged wind direction.

The daily average index of agreement parameter for the MM5 model ranges between 0.72 and 0.88 with a

10

- day mean of 0.81. These results are better than those achieved with the August 1993 and June 1992 episodes. Table 6

- 8.2 also lists the systematic and unsystematic components of the RMSE errors. For all but 1 May, the systematic component is larger than the unsystematic c omponent. The episode average

RMSE error is 1.84 m s - 1 

### , nearly identical to the June 1992 episode.

6.9.2.2 Vector Mean Wind Speeds

Figures 6

- 75 through 6

- 78 present various surface wind speed and wind direction summary plots for the April

- May 1995 epi sode. As with the August and June episodes, the vector mean modeled wind speed (Figure 6

- 75 ) is systematically overestimated compared to the observed mean wind speeds for every day of the episode except 1 May. This overestimation tendency bears several s imilarities to that encountered in the August 1993 and June 1992 episodes (see Figures 6

- 47 and 6

- 61 ). On several of the days, the model represents the afternoon wind speed increase fairly well notwithstanding the over

- prediction.

6.9.2.3 Mean Wind D irection

Figure 6

- 76 presents the hourly variation in modeled versus observed surface wind directions over the ten April

- May modeling days. There is generally good agreement for the first five days, but beginning with 29 April, the model’s wind direction predictions begin to diverge significantly from the observations.234



### 6.9.2.4 Root Mean Square Error in Wind Speeds

The RMSE errors and index of agre ement are shown in Figures 6

- 77 and 6

- 78 , respectively. The episode average values of the unsystematic , systematic, and total RMSE errors are 1.46 m s - 1 

### , 1.84 m s - 1 

### and 2.35

m s - 1 

### , respectively. These results are poorer than the August 1993 and June 1992 episodes. From Figure

6

- 77 there does not appear to be any significant error growth during the simula tion but the RMSE errors are greater then the June and August episodes. The systematic component (model physics

- related) of the RMSE error is clearly the larger contributor to the total RMSE error in this episode as s hown graphically in Figure 6

- 77

.

6 .9.2.5 Index of Agreement in Surface Wind Speeds

The index of agreement results for the Apri l

- May 1995 episode (Figure 6

- 78 ) are quite good. This is somewhat surprising given the poorer wind direction and RMSE error performance of the model for this

ep isode relative to the August 1993 and June 1992 periods. For this episode, RAMS exhibits typical hourly variation in the agreement index parameter with the lowest values occurring during the morning period. In the afternoon when speeds are greater, the i ndex increases. The mean value of the index of agreement varies diurnally throughout the episode; the mean over the whole period is 0.81, an improvement over the 0.75 figure obtained with the August and June episodes.

6.9.2.6 Ground

- Level Wind Fields

Figures 6

- 79 presents afternoon RAMS surface wind field comparisons over the 12 km domain on four different days during the April

- May 1995 episode. The modeled winds on these days are in reasonable agreement with the observed values. When examining the f ull set of hourly surface wind plots for the episode we did not find instances where there were major regional discrepancies in the predicted versus

observed surface wind fields.

6.9.3 Mixing Ratios

6.9.3.1 Statistical Measures of Near

- Surface Mixing Ratios

Statistical results for near surface mixing ratios are listed in Table 6

- 9 for the June 1992 episode. Across the 12 km domain the agreement between the episode mean daily maximum observed (13.1 g/Kg) and modeled (13.5 g/Kg) mixing ratios is quite g ood. RAMS slightly underestimates the maximum ratio on most days. The mean bias and error in mixing ratios are also quite good, with 10

- day mean values of

- 0.1 g/Kg and 0.7 g/Kg respectively. As with the August 1993 and June 1992 episodes, these results indicate that RAMS does a fairly good job of reproducing the daily maximum and hourly specific humidity across the 12

km domain.

6.9.3.2 Spatial Mean Surface Mixing Ratios235



### Figure 6

- 80 is a spatial mean surface mixing ratio plot for the April

- May 1995 episode. The agreement between prediction and observation is excellent at both 12 km and 24 km scales. Except on the night of 29 April, there is no discernable tendency in this plot to underestimate mixing ratio.236



### 6.9.3.3 Bias in Surface Mixing Ratios

The minor underestimation of hourly mixing ratio is evident in the normalized bias ti me series shown in Figure

6

- 81 . The largest biases in mixing ratio tend to occur at night or in the early morning hours although on 1 and 2 May there is a tendency to o verestimate during the middle of the day. No significant diurnal performance problems are evident in the mixing ratio results.

6.9.3.4 Surface Mixing Ratio Fields

Figure 6

- 82 presents examples of ground

- level mixing ratio fields across the 12 km domai n at two time periods (0300 and 1500 EST) on 30 April 1995. These plots show fairly low humidity and weak spatial gradients in the fields, predominantly in a latitudinal direction. There is generally good agreement between the predicted and observed mixi ng ratios at the various monitoring stations.

6.9.4 Precipitation

6.9.4.1 Statistical Measures of Total Daily Precipitation

Rain occurred on 8 of the 10 days during the 24 April

–3 May 1995 episode (see Table 6

- 9 ). The maximum daily precipitation a t any rain gauge varied between 3 mm and 48 mm with an episode mean of 21 mm. RAMS predicted a range of daily maximum rainfall totals from 2.0 mm to 38.6 mm with an episode mean of 20.1 mm. The mean observed and predicted precipitation across all rain ga uges was 2.5 mm and 4.1 mm, yielding an overall discrepancy of 64% which constitutes fair agreement. RAMS’ performance in predicting rainfall for this episode is poorer than for the August 1993 and July 1992 episodes.

The bias in daily rainfall predict ion ranges from

– 5.95 mm to 10.23 mm with an episode mean of 1.5 mm. On May 1 st 

### and 2 nd 

### , RAMS over

- predicted the mean daily rainfall amounts by 10.5 mm and 5.9 mm, respectively. The daily gross errors in rainfall ranged between 0.02 mm and 10.63 mm with an episode mean of 3.5 mm. Overall, the model did a fair job of simulating the daily and episode average rainfall totals when averaged across all monitoring stations.

6.9.4.2 Temporal Distribution of Total Daily Rainfall Across All Sites

Figure 6

- 83 presents time series plots of the daily precipitation totals derived from the measured and predicted values averaged across all reporting stations in the 12 km and 24 km domains. With the exception of the significant over

- prediction on 1 May and 2 May (s ee Table 6

- 9 ), the day

- to

- day rainfall predictions match the observations closely in this comparison.

6.9.4.3 Spatial Distribution of Daily Total Measured and Observed Rainfall

Figure 6

- 84 compares the gridded daily precipitation predictions (in mm) a cross the 12 km domain for 2 May 1995 while the bottom panel presents the daily total measurements (also in mm). On 2 May, RAMS predicts bands of moderate precipitation (> 21 mm) across central Tennessee, southeastern Kentucky, and237



### northern Alabama. Data

from the rain gauge network corroborates the general features of this predicted distribution but the network is far too sparse to make definitive spatial comparisons.

6.9.4.4 Correlation of Daily Maximum Rainfall Across All Sites

Figure 6

- 8 5 present s a Scatter Plot of predicted and observed daily total precipitation on 2 May 1995

. While the spatial ti

me series plots in Figure 6

- 83 suggests fair model performance based on averages across all monitoring stations, the scatter plots again reveal that thi s agreement derives from cancellation of over

and under

- predictions at the various rain gauges. The Scatter Plot suggests generally poor correlation between prediction and observation at the individual reporting sites. Thus, as with the August 1993 and June 1992 episodes, RAMS does a credible job of estimating the total precipitation when averaged across the monitoring network on each episode day but the model’s ability to predict the exact rainfall amounts paired in time and space with specific monitors is generally poor.238

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Episode

– Surface Temperatures (deg C). 

### (a) 12 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed Temperature 22.8 23.9 26.1 27.2 28.3 28.8 31.1 30.0 27.2 23.3 26.9

Maximum Predicted Temperature 24.4 25.8 24.8 26.0 28.4 27.8 29.0 29.2 26.7 24.0 26.6

M ean Bias (deg C) 0.7

- 0.7

- 0.5

- 1.1

- 1.9

- 1.8

- 1.2

- 0.5 0.0

- 1.3

- 0.8 Gross Error (deg C) 1.8 1.5 1.6 1.9 2.3 2.3 2.0 1.4 1.5 1.9 1.8

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Episode

– Surface Temperatures (deg C). (b) 24 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed Temperature 33.9 29.4 28.3 31.1 31.1 37.8 32.2 34.4 32.2 32.2 32.3

Maximum Predicted Temperature 29.8 27.5 28.0 28.4 29.3 29.5 30.4 31.4 29.9 30.5 29.5

Mean Bias (deg C)

- 0.5

- 0.7

- 0.6

- 0.7

- 1.0

- 0.9

- 0.5

- 0.6

- 0.7

- 1.0

- 0.7 Gross Error (deg C) 1.8 1.7 1.7 1.8 1.8 1.9 1.7 1.6 1.8 2.0 1.8239

T able 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 Episode

– Surface Wind Speeds (meters/sec). (a) 12 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 12 0 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Mean Observed Wind Speed 2.53 2.41 1.22 2.91 2.36 0.91 0.83 2.28 1.83 1.36 1.86

Mean Predicted Wind Speed 2.46 3.77 3.52 5.55 3.77 1.48 2.89 1.55 2.25 2.58 2.98

Observed Standard
Deviation 2.67 2.06 2.22 2.42 2.37 2.18 2.58 2.41 3.40 1.88 2.42

Predicted Standard
Deviation 3.22 2.00 2.00 2.43 2.69 2.37 2.99 3.12 4.27 2.29 2.73

RMSE 2.15 2.18 2.24 3.09 2.46 2.13 2.71 1.95 2.26 2.31 2.35

RMSE

S 1.53 1.66 1.73 2.73 1.94 1.75 2.07 1.21 2.08 1.68 1.84

RMSE

U 1.49 1.38 1.31 1.40 1.45 1.15 1.70 1.51 1.63 1.57 1.46

Index of Agreement, I 0.83 0.74 0.82 0.72 0.78 0.81 0.82 0.82 0.88 0.76 0.81

Skill E 0.56 0.68 0.60 0.62 0.63 0.54 0.70 0.63 0.49 0.92 0.64

Skill VAR 1.20 1.01 0.95 0.99 1.15 1.16 1.18 1.31 1.25 1.27 1.15

Mean Observed Wind Direction ( O ) 346.6 292.5 237.0 199.5 287.8 26.2 106.6 35.1 12.0 351.1 261.4

Mean Predicted Wind Direction ( O ) 318.3 290.2 260.6 240.2 250.2 246.7 235.0 237.4 240.0 250.9 257.0240

Table 6

- 9. RAMS Model Evaluation Resul ts for the 24 April

– 3 May 1995 Episode

– Surface Wind Speeds (meters/sec). (b) 24 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Da y 123 Mean

Value

Mean Observed Wind Speed 2.0 1.53 0.97 1.20 1.51 0.48 1.23 1.86 1.40 0.76 1.29

Mean Predicted Wind Speed 2.13 2.70 3.19 3.18 2.81 1.52 0.92 1.51 1.42 1.24 2.06

Observed Standard
Deviation 3.17 2.58 2.98 3.38 2.99 2.62 2.50 2.76 2.97 2.3 1 2.83

Predicted Standard
Deviation 2.97 2.70 3.41 3.77 3.54 3.09 3.09 3.55 3.59 3.17 3.29

RMSE 2.04 2.38 2.83 3.00 2.67 2.36 2.42 2.47 2.38 2.54 2.51

RMSE

S 1.32 1.83 2.13 2.22 1.67 1.80 1.69 1.45 1.40 1.59 1.71

RMSE

U 1.55 1.49 1.83 1.91 2.04 1.49 1 .67 1.97 1.90 1.96 1.78

Index of Agreement, I 0.88 0.81 0.86 0.88 0.86 0.89 0.82 0.83 0.88 0.83 0.85

Skill E 0.49 0.60 0.64 0.57 0.70 0.58 0.68 0.73 0.64 0.90 0.65

Skill VAR 0.94 1.09 1.18 1.14 1.20 1.21 1.28 1.32 1.21 1.45 1.20

Mean Observed Wind

Dire ction ( O ) 328.5 291.7 202.6 228.0 273.0 56.4 62.0 40.8 7.2 44.4 261.9

Mean Predicted Wind Direction ( O ) 303.7 285.2 254.4 241.2 243.8 237.4 225.4 221.3 229.7 233.2 247.5241

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Epi sode

– Surface Mixing Ratios (gm/Kg). (a) 12 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed

Mix ing Ratio 15.7 9.9 9.8 13.0 13.6 13.5 16.0 14.6 15.2 9.7 13.1

Maximum Predicted Mixing Ratio 15.5 13.8 8.6 12.8 13.2 12.3 13.5 14.5 15.5 15.0 13.5

Mean Bias (gm/Kg) 0.1

- 0.3

- 0.4 0.3

- 0.2

- 0.5

- 0.1 0.2 0.1

- 0.4

- 0.1

Gross Error (gm/Kg) 0.8 0.5 0.6 0.8 0 .7 0.7 0.9 0.8 0.8 0.6 0.7

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Episode

– Surface Mixing Ratios (gm/Kg). (b) 24 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Da y 118 29 Apr Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed Mixing Ratio 19.9 18.0 16.6 17.9 17.3 16.7 18.5 19.9 18.6 18.6 18.2

Maximum Predicted Mixing Ratio 17.2 17.1 16.8 16.8 15.8 15.4 16.3 17.4 17.0 17.7 16.8

Mean Bias (gm/Kg) - 0.1

- 0.2

- 0.1 0.2

- 0.1 0.0 0.1 0.2 0.2

- 0.1 0.0

Gross Error (gm/Kg) 0.6 0.6 0.6 0.8 0.7 0.8 0.8 0.7 0.7 0.6 0.7242

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Episode

– Daily Total Precipitation (mm). (a) 12 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed Precipitation -- -- 10.0 3.0 37.0 3 .0 7.0 27.0 33.0 48.0 21.0

Maximum Predicted Precipitation -- -- 2.0 7.2 12.9 9.7 26.6 38.6 28.5 35.3 20.1

Mean Observed Precipitation -- -- 0.1 0.0 0.2 0.0 0.0 3.2 7.6 8.5 2.5

Mean Predicted Precipitation -- -- 0.1 0.0 2.0 0.2 0.5 13.7 13.5 2.8 4.1

Me an Bias (mm)

-- -- - 0.07

- 0.01 1.50 0.20 0.29 10.23 5.71

- 5.95 1.5 Gross Error (mm)

-- -- 0.14 0.02 1.75 0.22 0.34 10.63 7.96 6.50 3.5

Table 6

- 9. RAMS Model Evaluation Results for the 24 April

– 3 May 1995 SAMI Episode

– Daily Total Precipitation (mm). (b) 24 Km Grid

Performance
Attribute 24 Apr

Day 114 25 Apr

Day 115 26 Apr

Day 116 27 Apr

Day 117 28 Apr

Day 118 29 Apr

Day 119 30 Apr

Day 120 1 May

Day 121 2 May

Day 122 3 May

Day 123 Mean

Value

Maximum Observed Precipitation -- -- 10.0 53.0 41.0 24.0 1 97.0 27.0 45.0 48.0 55.6

Maximum Predicted Precipitation -- -- 17.8 27.3 32.8 25.3 43.6 37.1 40.6 34.3 32.4

Mean Observed Precipitation -- -- 0.2 1.1 1.2 0.4 1.3 2.9 6.3 5.7 2.4

Mean Predicted Precipitation -- -- 0.2 0.8 2.4 0.6 1.7 9.1 10.2 2.2 3.4

Me an Bias (mm)

-- -- - 0.10

- 0.40 0.97 0.02

- 0.14 6.58 3.88

- 3.64 0.9 Gross Error (mm)

-- -- 0.32 0.77 2.50 0.46 1.71 7.42 6.37 4.64 3.0

### 243 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 72

. Spatial Mean Ground

- Level Temperatures for the 24 April

– 3 May 1995 Episode.

### 244 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 73

. Mean Normalized Bias in Ground

- Level Temperatures for the 24 April

– 3 May 1995 Episode.

### 245 (a) 0100 UTC (2100 EST)

(b) 0700 UTC (0300 EST)

Figure 6

- 74

. Ground

- Level Temperature Fields for 29 April 1995.

### 246 (c) 1300 UTC (0900 EST)

(d) 1900 UTC (1500 EST)

Figure 6

- 74

. Concluded.

### 247 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 75

. Vector Mean Wind Speed for the 24 April

– 3 May 1995 Episode.

### 248 (a) 12 Km Grid

(b) 24 Km

Grid

Figure 6

- 76

. Mean Wind Direction for the 24 April

– 3 May 1995 Episode.

### 249 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 77

. Root Mean Square Error in Ground

- Level Wind Speeds for the 24 April

– 3 May 1995 Episode.

### 250 (a) 12 Km Grid

(b) 2 4 Km Grid

Figure 6

- 78

. Index of Agreement in Surface Wind Speed for the 24 April

– 3 May 1995 Episode.

### 251 (a) 26April 1995, 1900 UTC (1500 EST)

(b) 28 April 1995, 1900 UTC (1500 EST)

Figure 6

- 79

. Ground

- Level Wind Fields for the 24 April

– 3 May 1995 Episode

.

### 252 (c) 30 April 1995, 1900 UTC (1500 EST)

(d) 1 May 1995, 1900 UTC (EST)

Figure 6

- 79

. Concluded.

### 253 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 80

. Spatial Mean Ground

- Level Mixing Ratios for the 24 April

– 3 May 1995 Episode.

### 254 ( a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 81

. Mean Normalized Bias in Ground

- Level Mixing Ratios for the 24 April

– 3 May 1995 Episode.

### 255 (a) 0700 UTC (0300 EST)

(b) 1900 UTC (1500 EST)

Figure 6

- 82

. Ground

- Level Mixing Ratio Fields for 30 April 19 95.

### 256 (a) 12 Km Grid

(b) 24 Km Grid

Figure 6

- 83

. Spatial Mean Daily Precipitation (mm) for the 24 April

– 3 May 1995 Episode.

### 257 (a) Predicted Precipitation (mm)

(b) Measured Precipitation (mm)

Figure 6

- 84

. Daily Precipitation Fields for 2 May 1995.

### 258 (a) 12 Km Grid

(b) 24 Km Grid

### 259

Figure 6

- 85

. Scatter Plot of Daily Maximum Rainfall (mm) for 2 May 1995.



### 7.0 EVALUATION OF ALOFT METEOROLOGICAL FIELDS

The NCEP/NCAR reanalysis data (Kalnay et al. 1996) was processed as described in s ection 3.2 and in the SAMI meteorological modeling protocol (see Appendix 4) for use in the RAMS model. For each episode coarse grid upper

- air model results are compared with the 6

- h reanalysis fields. The primary

metrics are the bias and RMSE of the mod el minus the reanalysis at 850 mb for temperature, mixing ratio, and wind speed. Three issues need to be remembe red for these comparisons: 1) the reanalysis data have an original longitudinal resolution of 1.875 o 

### (corresponds to about 165 km at 37 o 

### north latitude) and the RAMS model is capable of resolving scales smaller than this even on the coarse grid; 2) no nudging was performed below 2 km above the surface so the 850

- mb fields are less constrained by the nudging towards the reanalysis fields, and 3) winds and temperatures are strongly constrained by upper

- air observations over the United States and to a lesser extent the water vapor mixing ratios as discussed in section 3.1. Because

of these issues not all differences between the model and the reana lysis data can be attributed to model error. Another comparison made here is between the model and observed afternoon mixing heights for the coarse grid. This comparison is an important one because of its implications for the air quality modeling phase b ut also a difficult one because of the nature of mixing heights. In the RAMS model the mixing height is not a prognostic variable. It was diagnosed from the vertical profile of the vertical exchange coefficient (K h 

### ) for heat at each grid point. In most cases the mixing height was set at the height where K h 

### dropped

below a value of 1 m 2 

### s - 2 

### . More involved procedures were used when there were multiple maxima in the K h 

### profile. Likewise an estimate of an observed mixing height has to be diagnosed from oth er quantities. The procedure chosen for this report was to take the average of two estimates of the observed mixing height. Both used the reanalysis data as a proxy for rawinsonde data and one used observed analyses of temperature. One estimate was to t ake the height where the dry

- adiabat with the maximum hourly temperature at 2 m between 1800

- 000 UTC intersected the potential temperature profile at 1200 UTC of the same day. The second estimate diagnosed the mixing height at 0000 UTC by locating an inve rsion with specified criteria. The actual comparison at each grid point is then the difference between the average model mixing height between 1800

- 0000 UTC and the observed mixing height (as just described) for the

same time. This difference was then av eraged over all days of the episode. This type of difference should give a broad representation of the model performance for afternoon mixing heights for the coarse grid. An exception to this procedure was for the February 1994 case, where the model mixi ng heights were averaged only for the period 1800

- 2200 UTC and the 0000 UTC inversion

- derived mixing height was not used as part of the estimate of the observed height. These changes were made for this episode given the

earlier sunset times for this case.

This section also presents a brief evaluation of MM5 and RAMS model performance in simulating the upper level horizontal winds and temperatures for the 26

- 28 June 1991 and 17

- 19 July 1991 episodes. Due to data and resource constraints, this evaluation w as limited to a brief comparison of daily averaged winds and temperatures which are vertically integrated from the surface to the top of the meteorological model domains. Thus, this analysis is at best a summary indication of the coarser performance chara cteristics of the two models in reproducing the vertical wind and thermodynamic structures of the two episodes.

### 260

7.1 11

- 19 July 1995 Episode

Figure 7

- 1 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the July 1995 episode. Bias values were g

enerally in the 0

- 1 o 

### C range from the Ohio Valley northwestward to the northern Plains and 0

to

- 2 o 

### C range everyplace else. RMSE values were generally 1

- 1.5 o 

### C except over New England where higher values were observed. The 850

- mb mixing ratio patterns in F ig ure 7

- 2 can be explained in part by the surface temperature biases described in section 6.1 and the mixing height differences shown in Fig ure 7

- 4. The warm bias of surface temperatures in Figure 6

- 1 from the northern Plains southeastward to Ohio

and the n southward to the Gulf coast correspond well to a high bias of model mixing heights in the same

areas of 200

- 400 m. Although the mixed layer values of mixing ratio in these same areas were generally too dry compared to surface observations, the fact that the model mixing heights were apparently too high implies the usual sharp transition to lower mixing ratios above the PBL was also elevated. This probably explains the positive bias in mixing ratio of +2 g kg

- 1 

### and higher over parts of the Ohio and the m id

Mississippi valleys. The RMSE of mixing ratio in Figure 7

- 2 had almost the same pattern and magnitude as

the bias. The bias of the 850

- mb wind speeds in Figure 7

- 3 shows general negative values of 0 to

- 1 m s

- 1


### over the Southeast, the northern Plains, and the eastern Great Lakes area and 0 to +0.50 m s

- 1 

### elsewhere. The same high bias seen in other surface and 850

- mb fields is seen in the 850

- mb wind speed over parts of New England. RMSE values of the wind speed at 850 mb were mostly in the 1

- 2 m s

- 1 

### range.

### 261 

## (a)

(b)

Figure 7

- 1 . (a) Bias of the model minus the NCAR/NCEP reanalysis temperature at 850 mb in degrees C for the July 1995 episode for the 48

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 262 

## (a)

(b)

Figure 7

- 2. (a) Bias of the model minus the NCAR/NCEP reanalysis water vapor mixing ratio at 850 mb in g kg
- 1 for the July 1995 episode for the 48

- km grid. (b) Root mean square error (RMSE) of the same field

for the same time and grid.

### 263 

## (a)

(b)

Figure 7

- 3. (a) Bias of the model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the July 1995 episode for the 48

- km grid. (b) Root mean square error (RMSE) of t he same field for the same time and grid.

### 264

7.2 22

- 30 May 1995 Episode

Figure 7

- 5 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the May 1995 episode. A cool bias as large as

- 2.5 o 

### C was observed over the southeastern Un ited States which was in contrast to a warm bias as large as +1.5 o 

### C over the central Great Plains and extending northeastward to Canada. The largest RMSE values were over the southeastern United States and approached 2.5 o 

### C. The bias and RMSE of

the 850

- m b water vapor mixing ratio in Fig ure 7

- 6 show a general positive bias as large as 1.5 g kg

- 1 

### over the central Great Plains. RMSE values were generally under 2 g kg

- 1 

### with a maximum along the Gulf coast of near 3.5 g kg

- 1



### . Figure 7

- 7 shows the bias and RM SE of the 850

- mb wind speed. A large area had bias values in the range of ± 0.50 m s

- 1 

### and RMSE values of 2.5 m s

- 1 

### or less. The maximum bias and RMSE values were over an area extending northeastward from the central Great Plains and coincided in part wit h the area of positive bias values for temperature and mixing ratio. These coincident patterns are near the maximum of the convective precipitation (not shown) and may be related to deficiencies in the Kuo convective scheme for this case. Figure 7

- 8 give s the bias in the afternoon mixing heights which shows values generally in the range of ± 200 m. The large negative values over the southern Appalachians are in an

area where there is little observed data. Figure 7

- 4 . Bias of the mean model mixing height during the period 1800

- 000 UTC minus the observed mixing height for the period averaged over the entire July 1995 episode for the 48

- km g rid. Differences are
in units of m. See the text for additional details.

### 265 

## (a)

(b)

Figure 7

- 5 . (a) Bias of the model minus the NCAR/NCEP reanalysis temperature at 850 mb in degrees C for the May 1995 episode for the 96

- km grid. (b) Root mea n square error (RMSE) of the same field for the same time and grid.

### 266 

## (a)

(b)

Figure 7

- 6. (a) Bias of the model minus the NCAR/NCEP reanalysis water vapor mixing ratio at 850 mb in g kg
- 1 for the May 1995 episode for t he 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 267 

## (a)

(b)

Figure 7

- 7 . (a) Bias of the model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the May 1995 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 268

7.3 9

- 18 May 1993 Episode

Figure 7

- 9 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the May 1993 episode. A cool

bias of

- 0.5 to

- 2 o 

### C was observed east of the Mississippi River. RMSE values in the same area were generally in the range of 1.5 to 2.5 o 

### C. The bias and RMSE of the 850

- mb water vapor mixing ratio in Figure 7

- 10 reveal a general moist bias of 0.5 to 1 g kg

- 1 

### over much of the eastern United States with RMSE values up to 2 g kg

- 1 

### . Figure 7

- 11 shows the bias and RMSE of the 850

- mb wind speed. Bias values were generally in the range of

- 0.5 to +1 m s

- 1 

### but with portions of Iowa and Kentucky having values

of 1.5 m s

- 1 

### . RMSE of the wind speed were generally under 2 m s

- 1 

### except for three areas where values in

excess of 3 m

s - 1 

### were observed: northeastern Iowa, the Kansas

- Missouri border, and the Kentucky, West Virginia, and Virginia border. Figure 7

- 12 gives the bias in the afternoon mixing heights which shows values generally in the range of ± 200 m with some values at or above 400 m over areas near the Great Lakes, the lower Ohio Valley, and the lower Mississippi Valley. Again the large negative values over the southern Appalachians are in an area where there is little observed data to determine an estimate of the observ ed mixing height. The bias patterns in this case are harder to explain than the ones for the July 1995 and May 1995 episodes. However, all the bias magnitudes are reasonable given the model capabilities and

observation limitations. Figure 7

- 8 . Bias of the mean model mixing height during the period 1800

- 000 UTC minus the observed mixing height

for the period averaged over the entire May 1995 episode for the 96

- km grid. Differences
are in units of m. See the text for additional details.

### 269 

## (a)

(b)

Figure 7

- 9 . (a) Bias of the model minus the NCAR/NCEP reanalysis temperature at 850 mb in degrees C for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 270 

## ( a)

(b)

Figure 7

- 10. (a) Bias of the model minus the NCAR/NCEP rean alysis water vapor mixing ratio at 850 mb in g kg
- 1 for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 271 

## (a)

(b)

Figure 7

- 11 . (a) Bias of th e model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the May 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 272

7.4 21 March to 1 April 1993 Episode Results

Figure 7

- 13 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the March 1993 episode. A

cool bias of

- 0.5 to

- 1.5 o 

### C covered much of the eastern United State s. RMSE values for temperature were

2 o 

### C or less over most areas. The bias and RMSE for water vapor mixing ratio in Fig ure 7

- 14 show a general bias of +0.5 to +1 g kg

- 1 

### with RMSE values of 2 g kg

- 1 

### or less. The results for wind speed in Fig ure 7

- 15 show values generally in the range of ± 1 m s

- 1 

### with RMSE values usually 2 m s

- 1 

### or less. The mixing height bias in Fig

ure 7

- 16 shows values in the range of ± 200 m with the following exceptions: over the Appalachians where observations are rare, along coastal a reas, and over parts of Iowa and Wisconsin. Figure 7

- 12. Bias of the mean mode l mixing height during the period 1800

- 000 UTC minus the observed mixing height for the period averaged over the entire May 1993 episode for the 96

- km grid. Differences
are in units of m. See the text for additional details.

### 273 

## (a)

(b)

Figure 7

- 1 3. (a) Bias of the model minus the NCAR/NCEP reanalysis temperature at 850 mb in degrees C for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 274 

## (a)

(b)

Figure 7

- 14. (a) Bias of the model minus the NCAR/NCEP reanalysis water vapor mixing ratio at 850 mb in g kg
- 1 for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 275 

## (a)

(b)

Figure 7

- 15. (a) Bias of the model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the March 1993 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and gr

id.

### 276

7.5 6

- 14 February 1994 Episode Results

Figure 7

- 17 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the February 1994 episode.

A wes t

- east band extending from the Iowa

- Missouri border eastward to the mid

- Atlantic had a warm bias

of 2 o 

### C or more with the maximum values near 4 o 

### C over the central Mississippi Valley. The RMSE values followed the same pattern with maximum values near 7 o 

### C. Explanations for this behavior are not evident without further investigation but may be related to the need for better vertical resolution at altitudes above 5 km. This episode had the strongest average jet

- stream level winds and it is in these situations that vertical resolution can be especially important. An increase in vertical resolution can affect both mass and wind fields. This issue was evident early in the creation of this simulation but time constraints did not allow for the exploration of diff

erent grids. The biases of 850

- mb water vapor mixing ratio (Figure 7

- 18) were mostly in the range of ± 0.50 g kg

- 1 

### with RMSE values of 1.5 g kg

- 1 

### or less. A general positive bias of +1 to +3 m s

- 1


### was observed for wind speed (Figure 7

- 19). The area of la rgest bias was over the Great Lakes, New England, and along the Appalachians. The RMSE values for wind speed followed the same pattern with values up to 6.5 m s

- 1 

### . Mixing height bias (Fig ure 7

- 20) was in the range of ± 200 m southeast of the Appalachians and values were generally

- 200 to

- 400 m to the west. The area with larger under

predictions coincides in part with the area of positive temperature biases which may imply the model created an Arctic layer which was too shallow compared with observations. Figure 7

- 16. Bias of the maximum model mixing height minus the maximum observed mixing height for the period 1800

- 0000 UTC averaged over the entire March 1993 episode for the 96

- km grid. Differences
are in units of m. See the text for additional details.

### 277 

## (a)

(b)

Figure 7

- 17. (a) Bias of the model minus the NCAR/NCEP reanalysis temperature at 850 mb in degrees C

for the Februa ry 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 278 

## (a)

(b)

Figure 7

- 18 . (a) Bias of the model minus the NCAR/NCEP reanalysis water vapor mixing ratio at 850 mb

in g kg
- 1 for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 279 

## (a)

(b)

Figure 7

- 19. (a) Bias of the model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the February 1994 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 280

7.6 21 July

- 1 August 1991 Episode Results

Figure 7

- 21 shows the bias and RMSE of the 850

- mb temperature in o 

### C for the July 1991 episode. A cool bias ranging from

- 0.50 to

- 1.50 o 

### C was observ ed over much of the eastern United States. RMSE for temperature were generally 2 o 

### C or less. Fig ure 7

- 22 shows a general moist bias at 850 mb of +0.50 to +1 g kg

- 1 

### with RMSE values usually 1.5 g kg

- 1 

### or less. The wind speed at 850 mb in Fig ure 7

- 23 had t ypical

bias values of

- 0.50 to +1.50 m s

- 1 

### with higher values along the Atlantic coast. RMSE values demonstrated a similar pattern with values as large as 4.5 m s

- 1 

### . The bias of the mixing heights in Fig ure 7

- 24 showed values typically in the range of ± 200 m. The larger negative values over the higher terrain of the Appalachians are in areas where the observed analysis has a small observational density. Figure 7

- 20. Bias of the model mixing height averaged over the period 1800

- 2 200 UTC minus the observed mixing height averaged over the entire February 1994 episode for the 96

- km grid. Differences
are in units of m. See the text for additional details.

### 281 

## (a)

(b)

Figure 7

- 21 . (a) Bias of the model minus the NCAR/ NCEP reanalysis temperature at 850 mb in degrees C for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 282 

## (a)

(b)

Figure 7

- 22 . (a) Bias of the model minus the NCAR/NCEP reanalysis water vapor mixing ratio at 850 mb in g kg
- 1 for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 283 

## (a)

(b)

Figure 7

- 23. (a) Bias of the model minus the NCAR/NCEP reanalysis wind speed at 850 mb in m s
- 1 for the July 1991 episode for the 96

- km grid. (b) Root mean square error (RMSE) of the same field for the same time and grid.

### 284

7.7 3

- 12 August 1993 Episode Results

7.7.1 Statistics of Aloft Predictions of Temperatures and Winds

Table 7

- 7 summarizes the predicted and observed, vertically

- integrated horizontal winds and temperatures

for the 3

- 12 August 1993 episode. The measurements are derived from the various NWS upper air reporting sites over the eastern U.S. From the table there is good agreement between the predicted and observed vertically averaged temperatures for each simulation day. For example, the vertically

- averaged predicted and observed temperatures o n the 12 km domain are 14.0 ° 

### C and 12.5 ° 

### C, respectively. The predicted and observed wind speeds and directions are also in very good agreement. Specifically, the mean aloft predicted and observed wind speeds are 6.9 m s - 1 

### and 6.7 m s - 1 

### , yielding a net 3% discrepancy. Part of this good agreement is due to the fact that aloft temperature and wind observations from the NWS radiosondes were employed in the RAMS FDDA nudging schemes. However, the actual weighting coefficients used in the nudging were small so that the RAMS fields were not under a heavy constraint to match the observations locally. While this apparent good agreement in the estimation of aloft temperatures, wind speeds, and wind directions shown in the summary statistics of Ta ble 7

- 7 is encoura ging and gives some confidence that the modeled wind patterns are a reasonable approximation to the conditions that actually occurred, this evaluation is insufficient by itself to judge the reasonableness of the model predictions

aloft. Figure 7

- 24. Bias of the model mixing height averaged over the period 1800

- 0000 UTC minus the observed mixing height averaged over the entire July 1991 episode for the 96

- km grid. Differences are in
units of m. See the text for additional details.

### 285

7.7.2 Vertical Profiles of Mixing Ratios, Temperatures and Winds

Further insight into the aloft model performance can be developed by examining so

- called ‘skew

- T’ plots of the modeled and observed wind and thermodynamic profiles. Such plots were developed for the RAMS output for every available rawinsonde sounding in the SAMI domain.) The full set of plots are contained o n the CD archive). Figure 7

- 24 is an example of a skew

- T plot at the Greensboro, SC site at 1300 EST on 7 August 1993. The solid blue line represent s the RAMS upper air temperature profile while the solid red line corresponds to radiosonde observations. The thin blue and red lines denote the mixing ratio predictions and observations, respectively. Modeled and observed horizontal winds are shown in t he stick plots to the right. Perusal of the results from the Greensboro site midday on 7 August reveals that there is quite good agreement between modeled and observed temperatures through the planetary boundary layer (pbl). Also, above 900 mb, there is fairly good agreement between predicted and observed winds. Within the first 1000m of the atmosphere, however, there is about a 90 degree discrepancy between the predicted and observed horizontal winds. Also, the model produced larger mixing ratios throu gh the pbl compared with the measurements although close to the ground they are in good agreement. As noted above, a full set of these plots have been developed and archived; however, project resources have not allowed a more detailed analysis of this int riguing data set. A systematic inter

- comparison of the modeled vertical structures within the lowest thousand meters would be very interesting, particularly in regions of complex topography.

These data sets have been archived for SAMI and are available t o parties interested in conducting further analyses.

7.8 23

- 29 June 1992 Episode Results

7.8.1 Statistics of Aloft Predictions of Temperatures and Winds

Table 7

- 8 summarizes the aloft wind comparisons for the 23

- 29 June 1992 episode. As with the Augu st episode, there is good agreement between the predicted and observed vertically averaged temperatures for each simulation day. The vertically

- averaged predicted and observed temperatures on the 12 km domain

are13.5 ° 

### C and 13.8 ° 

### C, respectively. The mean predicted and observed wind aloft wind speeds are 7.3 m

s - 1 

### and 6.6 m s - 1 

### , giving a 11% discrepancy.

7.8.2 Vertical Profiles of Mixing Ratios, Temperatures and Winds

Figure 7

- 25 gives a skew

- T plot at the Greensboro, SC at 2000 EST on 25 June 1992. On this evening at Greensboro, there is good agreement between modeled and observed temperatures above about 300m agl. At the surface, the model estimate the onset of a shallow nocturnal temperature inversion while the data show the remainder of a slightly superadiabatic layer extending to ground. Throughout the depth of the model domain, there is fairly good agreement in the modeled and observed horizontal winds. As with the August 1993 episode, RAMS gives larger mixing ratios through the pbl compared with the measurements although close to the ground they are again in good agreement.

### 286

7.9 24 April

–3 May 1995 Episode Results

7.9.1 Statistics of Aloft Predictions of Temperatures and Winds

Table 7

- 9 summarizes the aloft wind comparisons for the April

- Ma y 1995 episode. There is reasonably good agreement between the predicted and observed vertically averaged temperatures for each simulation day. The vertically

- averaged predicted and observed temperatures on the 12 km domain are 6.6 ° C and

6.0 ° C, respectiv ely. The mean predicted and observed wind aloft wind speeds are 10.0 m s - 1 

### and 9.1 m s -

1 

### , producing a 10% discrepancy.

7.9.2 Vertical Profiles of Mixing Ratios, Temperatures and Winds

Figure 7

- 26 shows a skew

- T plot at Greensboro at 0800 EST on 29 Apr il 1995. On this morning at Greensboro, there is good agreement between modeled and observed temperatures at the ground and

above about 2000m. Within the boundary layer , however, there is about a 3 ° C positive (warm) bias in the modeled temperatures. At the surface, the model estimates the remnants of the nocturnal temperature inversion and this feature is also evident in the data. Throughout the depth of the model domain, there is fairly good agreement in the modeled and observed horizontal winds. Unli ke the August 1993 and June 1992 episodes, RAMS gives lower mixing in the first 2500 m of the atmosphere compared with the measurements; above this height, the RAMS fields are more moist compared with the observations.

Table 7 .7

. Mean of the Vertical ly Averaged Profiles of Modeled and Predicted Temperatures and Winds for the 3

- 12 August 1993 Episode

— 12 Km Grid.

Date Observed

Temp. (deg C) Predicted

Temp. (deg C) Observed

Wind Speed
( 

### ms - 1 ) Predicted

Wind Speed
(ms - 1 ) Observed

Wind
Direction (de g) Predicted

Wind
Direction (deg)

11 Aug 13.8 13.6 3.8 4.2 191 219 10 Aug 13.7 13.5 4.5 3.5 68 127 9 Aug 13.5 13.5 4.3 3.2 25 344 8 Aug 13.0 13.2 5.5 5.1 273 265 7 Aug 12.6 13.4 9.2 10.7 282 269 6 Aug 13.2 13.4 8.2 9.6 252 243 5 Aug 13.8 14.0 8.2 8. 2 284 270 4 Aug 15.6 15.4 8.8 9.1 255 244 3 Aug 16.7 16.4 7.9 8.5 251 248

AVE 12.5 14.0 6.7 6.9

-- --

### 287

Table 7.8

. Mean of the Vertically Averaged Profiles of Modeled and Predicted Temperatures and Winds for the

22

- 29 June 1992 Episode

— 12 Km Grid.

Date Observed

Temp. (deg C) Predicted

Temp. (deg C) Observed

Wind Speed
( 

### ms - 1 ) Predicted

Wind Speed
( 

### ms - 1 ) Observed

Wind
Direction (deg) Predicted

Wind
Direction (deg)

23 June 10.7 11.0 5.9 7.5 262 242

24 June 13.2 13.2 8.5 10.5 248 251

25 June 14 .9 14.2 8.2 9.1 261 252

26 June 15.2 14.7 7.0 8.3 258 249

27 June 13.9 13.7 6.8 6.6 300 295

29 June 14.1 13.3 5.4 4.3 331 288

29 June 14.9 14.3 4.1 4.7 219 216

AVE 13.8 13.5 6.6 7.3

-- --

Table 7.9

. Mean of the Vertically Averaged Profiles of Model ed and Predicted Temperatures and Winds for the 24 April

– 3 May 1995 Episode

— 12 Km Grid.

Date Observed

Temp. (deg C) Predicted

Temp. (deg C) Observed

Wind Speed
( 

### ms - 1 ) Predicted

Wind Speed
( 

### ms - 1 ) Observed

Wind
Direction (deg) Predicted

Wind
Direct ion (deg)

25 April 2.5 2.8 9.7 10.0 310 298 26 April 4.6 4.4 6.8 8.2 282 255 27 April 7.7 7.5 8.8 11.0 216 217 28April 6.2 9.6 9.6 11.1 277 278 29 April 6.9 6.4 6.2 6.6 313 269 30 April 8.7 8.3 8.5 10.2 232 227 1 May 7.4 8.4 10.2 10.6 292 263 2 May 5.9 7.0 13.4 13.9 247 254 3 May 4.1 4.9 8.8 9.1 336 316

AVE 6.0 6.6 9.1 10.0

-- --

### 288

Figure 7

- 24. Comparison of Observed (red) and Predicted RAMS (blue) Upper Air Wind, Temperature and Mixing Ratios on 7 August 1993 at Greensboro, NC: 1300 EST.

### 289

Figure 7

- 25. Comparison of Observed (red) and Predicted RAMS (blue) Upper Air Wind, Temperature and Mixing Ratios on 25 June 1992 at Greensboro, NC: 2000 EST UTC.

### 290

Figure 7

- 26. Comparison of Observed (red) and Predicted RAMS (blu e) Upper Air Wind, Temperature and Mixing Ratios on 29 April 1995 at Greensboro, NC: 0800 EST

.

### 291

8.0 RAMS PERFORMANCE ACROSS SAMI EPISODES

In addition to performing evaluations of RAMS model performance for the specific episodes assigned them,

the UAH and AG team elected to add an additional analysis aimed at portraying model performance with a unified set of statistical and graphical procedures. Although outside the scope of UAH and AG work efforts, this supplement work was performed in order that SAMI mi ght be able to interpret the performance of the model using a common suite of evaluation tools. This was accomplished, in part, by using AG’s MAPS evaluation software, previously introduced in Chapter 5. An added benefit is that the SAMI episodes can the n be compared with other RAMS and MM5 evaluations carried out by Alpine Geophysics across the U.S. over the past five years.

In this section we present the results of six (6) UAH episodes and three (3) AG episodes using two distinct software products. Th e first, developed by UAH, computes episode

- composite near

- ground level bias and error performance statistics for temperature, wind speed and mixing ratio. The UAH algorithms utilize similarity theory to develop scaling relationships to extrapolate Layer 1 RAMS temperatures, winds, and mixing ratios (at 20 m agl) to the heights at which these parameters are commonly measured (i.e. 2 m for temperatures and mixing ratios; 10 m for winds). The second product is MAPS which produces a variety

of statistical an

d graphical evaluation products based on the model

- produced output at Layer 1. MAPS is used to develop sixteen (16) different statistical measures of performance in addition to a variety of graphical displays. Finally, we present summary tables and figur es that compare the SAMI RAMS

evaluation results with other studies.

8.1 Episode Composite Near

- Ground Level Performance Measures

Table 8

- 1 presents near

- ground level episode composite bias and error statistics for temperature, wind speed, wind directio n, and mixing ratio. These statistics were calculated by UAH using code developed to extrapolate RAMS Layer 1 output to stan dard measurement heights

.

8.1.1 Bias and Error in Mean 2 m Temperatures

The bias and error composite statistics for 2 m tempe ratures for nine (9) SAMI episodes are presented in

Table 8

- 1 and Figure 8

- 1. The range in overall bias in near surface temperature is

– 2.55 ° F to 1.17 ° F with a mean over all episodes of

– 1.20 ° F (one degree F=0.55 degrees C) . For the root mean square erro r (RMSE), the range over the episodes is 3.82 ° F to 5.38 ° F with a mean value of 4.45 ° F. These bias and error statistics are displayed graphically in Figure 8

- 1. Only the February 1994 and July 1995 episode exhibited a tendency to overestimate near

- surface t emperatures and the RMSE errors for all episodes are fairly similar to the mean value.

8.1.2 Bias and Error in Mean 10 m Wind Speeds

Bias and error composites for 10 m surface winds are given in Table 8

- 1 and Figure 8

- 2. The range in bias

of near surfa ce wind speeds across the 9 SAMI episodes is 0.18 m s - 1 

### to 1.33 m s - 1 

### with a mean over all episodes of 0.82 m s - 1 

### . Thus, RAMS overestimated surface wind speeds consistently in every SAMI

### 292

episode even when the model predictions are extrapolated down to the nominal height of the observations. For RMSE, the range over the episodes is 1.51 to 2.75 with a mean value of 2.05 m s - 1 

### . The wind speed bias and error statistics are shown in Figure 8

- 2. There is considerable variability in the degree to which

RAMS ove restimates surface wind speeds across the episodes. This over

- prediction is the least with the May 1995 episode and is greatest for the February 1994 and April

- May 1995 episodes. Less variability across episodes is seen in the RMSE plots; interestingly, here the May 1995 episode is not the one with the lowest error even though it had the lowest speed bias, suggesting the presence of cancellation of errors in the bias calculation for this episode.

8.1.3 Bias and Error in Mean 10 m Wind Directions

Wind direction bias and error estimates for the 10 m surface winds are presented in Table 8

- 1 and Figure

8

- 3. The bias in episode composite near surface wind directions across the 9 episodes is

– 3.76 deg rees to 12.30 deg rees with a mean over all episodes of

– 0 .76 deg rees . Only the March 1993 and May 1995 episodes had positive directional biases but there is really little significance to the sign of this bias quantity given the scale of the SAMI domain. More meaningful is the RMSE in wind direction. Here the r ange over the episodes is 63 deg

rees to 83 deg

rees with a mean value of 72.2 deg rees . As shown in Figure 8

- 3, there is actually very little variability in wind direction RMSE across the 9 SAMI episodes based on these near

surface wind statistics. Of cour se from an air quality modeling perspective, the bias needs to be examined at shorter times scales than the episode composite and this will be done in a later section.

8.1.4 Bias and Error in 2 m Mixing Ratios

The bias and error composite statistics f or 2 m mixing ratios are listed in Table 8

- 1 and presented in Figure

8

- 4. The range in overall bias in near surface mixing ratio is

– 1.76 g/Kg to

– 0.00 g/Kg with a mean of

– 0.60 g/Kg. over all episodes. The RMSE r anges from 0.59 g/Kg to 4.12 g /Kg with a mean value of 1.55 g/Kg. As noted in the table, we suspect that the particularly large mixing ratio bias and error statistics for the April 1995 episode may be the result in a breakdown in the surface layer vertical extrapolation scheme

rather than an indi

cation of poor model performance. As noted in Section 6.7 wherein a more detailed evaluation of surface mixing ratios was carried out, we did not see performance this poor. It is also possible that the extrapolation scheme may have experienced difficulti es with the July 1995 episode as well but this episode was not examined intensively with the MAPS software. Aside from the suspected problems with the bias and error statistics for these two episodes, the overall summaries in Figure 8

- 4 suggest good and

c onsistent performance across all 7 remaining SAMI episodes.

8.2 Time Series of Spatial Mean Surface Winds, Temperature and Mixing Ratios

Two

- thirds of the UAH RAMS modeling episodes were re

- evaluated using AG’s MAPS evaluation software and then compared with the three RAMS runs previously discussed in sections 6.7 through 6.9. Below, we present statistical and graphical results of these comparisons. The statistical results are presented in Tables 8

- 2 and 8

- 3 in terms of episode average statistics for t he 12 km and 24 km grid domains, respectively. Here we focus on the 12 km results. Figures 8

- 5 through 8

- 10 display the hourly results in the form of spatial mean time series, first introduced in Chapter 7. Note also that the results

### 293

presented below are derived from Layer 1 RAMS predictions (~20 m) and do not exactly correspond to the height of the measured surface temperatures, winds and mixing ratios.

8.2.1 Spatial Mean Temperatures

Spatial mean ground

- level temperature time series are presented in Figure 8

- 5 for seven (7) SAMI episodes. Three of these (3

- 12 August 1993, 22

- 29 June 1992, and 24 April

- 3 May 1995) have already been discussed in sections 6.7 through 6.9. Ground

- level bias and gross error statistics for temperature are

listed in Table 8

- 2a. The bias in episode average ground level temperatures across the 7 episodes ranges

from

– 1.46 ° C to 0.51 ° C with a mean over all episodes of 0.79 ° C. The range in gross error over the episodes is 1.60 ° C to 2.24 ° C with a mean value of 1.94 ° C. As show n in Figure 8

- 5, RAMS matches the diurnal variation in temperature fairly well with two exceptions. First, there is a systematic tendency to underestimate the afternoon temperatures due to mismatch between 2m measurement height and the 20 m height of the l

owest RAMS prediction. More obvious is the systematic underestimation of temperature in the February 1994 episode for the 8

- 10 February period. For these three days, the model systematically

under

- predicts the ground level temperatures, day and night, by 4 to 8 ° C. Otherwise, the time series plots are fairly consistent across the episodes and similar to other mesoscale model applications.

8.2.2 Vector Mean Wind Speeds

Hourly spatial mean ground

- level wind speed time series are presented in Figure 8

- 6 an d the episode average bias and gross error statistics are listed in Table 8

- 2b. The mean predicted wind speeds are all positively biased (i.e., they all overestimate the observations) and the average discrepancy between modeled and observed episode averag e wind speed ranges from 35% to 140% with a mean over all episodes of 67%. As shown in Figure 8

- 6, the hourly spatial mean wind times series for predictions and observations have large day

- to

- day variability with each episode and from episode to episode. RAMS tendency to over

- predict the 10 m winds is evident in these plots. The greatest discrepancies occur during the early part of the 8

- 13 February 1994 episode (especially 9 February). Despite the seemingly large differences between predicted and obser ved spatial mean winds from day

- to

- day and episode

- to

- episode, these results are generally consistent across the episodes and similar to other mesoscale model applications.

8.2.3 Mean Wind Direction Differences

The spatial mean wind direction time serie s are shown in Figure 8

- 6 and the episode average bias and gross

error statistics are listed in Table 8

- 2b. The episode mean differences between predicted and observed surface winds ranges from 4 to 103 deg rees with a composite mean of 39 deg rees . From F igure 8

- 7 there is generally good agreement in the day

- to

- day and hour

- to

- hour wind directions for most of the episodes. The greatest wind direction discrepancies occur for the March 1993 and February 1994 episodes.

8.2.4 Index of Agreement

Spatial m ean index of agreement scores for the seven (7) SAMI episodes are listed in Table 8

- 2 and presented graphically in Figure 8

- 8. The index ranges from 0.72 to 0.81 with a composite mean over all

### 294

episodes of 0.76. While each of the index of agreement time s eries exhibit unique diurnal features, the similarity of the mean index scores is quite remarkable. There is actually very little variation in the index scores across the episodes even though the episodes themselves cover a broad range of synoptic and

cli matic conditions.

8.2.5 RMSE Errors

Table 8

- 2 presents the RMSE errors for the seven SAMI episodes. The episode average RMSE errors range from 1.90 m s - 1 

### to 2.76 m s - 1 

### with a mean across the seven episodes of 2.18 m s - 1 

### . For all but the May 1995 episo de, the systematic component exceeds the systematic component of the RMSE error and for the May episode the two components are nearly identical. Thus, there is consistency in the finding that (because the systematic component is nearly always the larger o f the two) the preponderance of the RAMS modeling uncertainty tends to result from input uncertainties and the inherent difficulties in prescribing model inputs from sparse measurements. The spatial mean RMSE error plots again identify the 8

- 10 February

1 994 period as one of co ncern due to the large (i.e., > 5 m s - 1 

### ) RMSE errors during the early portion of the episode.

8.2.6 Spatial Mean Mixing Ratios

Spatial mean ground

- level mixing ratio time series are presented in Figure 8

- 10 and the episode average statistical summaries are given in Table 8

- 2c. The bias in episode average ground level mixing ratios across

the 7 episodes ranges from

– 0.60 g/Kg to 0.03 g /Kg with a mean over all episodes of

– 0.10 g/Kg. The range in gross er

ror over the episodes is 0.4 4 g/Kg to 1.10 g/Kg with a mean value of 0.78 g /Kg. As shown in Figure 8

- 10, RAMS provides an excellent match of the diurnal variation in mixing ratios for each episode studied.

8.3 Comparison with Other Prognostic Model Evaluation Studies

Table 8

- 4 su mmarizes episode composite temperature, wind speed, wind direction and mixing ratio statistics for nearly thirty (30) MM5 and RAMS model applications over the past five years. The table focuses on results from prognostic model applications on 12 km grid m eshes since this the scale most commonly reported. Most of the studies, however, also included model evaluation at 36 km and 4 km scales as well. This information has also been compiled and is available from AG. While these statistics may be helpful in making general comparisons between studies and episodes, it is clear that the calculation of an episode mean statistic often conceals important day

- to

- day and/or hour

- to

- hour variations that may be quite important in judging the adequacy of a meteorologica l or air quality model simulation.

8.3.1 Bias and Error in Mean Temperatures

From Table 8

- 4a the mean bias and gross errors in the ensemble of RAMS and MM5 studies reviewed are

– 0.5 ° C and 1.9 ° C, respectively. The standard deviations for these quanti ties are 0.6 ° C and 0.5 ° C, respectively. Comparing the seven (7) SAMI episodes with the broader set of evaluations, we find that for gross error, all SAMI episodes fall within at least + one standard deviation (i.e., 1 sigma) of the ensemble

### 295

mean. Four of

the seven SAMI episodes yield mean bias statistics just outside the, + 1 sigma of

– 0.8 ° C to

0.4 ° C. However, two points are worth noting. First, a number of the studies reported in Table 8

- 4 employed vertical grid structures that shallower first layers c ompared with the 20 m first atmospheric layer in RAMS. Indeed, many of the MM5 runs used first grid cells of 9

- 10 m in thickness. Thus, one cannot rigorously apply the statistical summaries without recognizing the impact that the height mismatch between measurement and prediction has on the statistics. Second, the whether or not a particular episode falls

within or outside of the + 1 sigma range should not be taken as an indicator of model acceptance or rejection. This range is used here simply to facili tate comparisons between modeling studies and is explicitly not suggested as a model performance criterion to be rigidly applied.

8.3.2 Bias and Error in Mean Mixing Ratios

The mean bias and gross errors in mixing ratios are presented in Table 8

- 4b for the ensemble of RAMS and

MM5 studies. The mean values for bias and error are

- 0.4 g/Kg and 1.4 g/Kg, respectively. The standard deviations for these quantities are 0.7 g/Kg and 0.5 g/KG, respectively. For mixing ratio bias, we find that all SAMI episodes fall well within at least + one standard deviation (i.e., 1 sigma) of the ensemble mean. For gross error in mixing ratio, for of the seven SAMI episodes yield smaller errors that

–1 sigma range of 0.9 g/Kg and the other two episodes are at the low end o f the range. Thus, RAMS performs better that average to mixing ratio.

8.3.3 Average Discrepancy in Mean Wind Speed

Table 8

- 4c lists the average discrepancy (i.e. accuracy) of wind speed prediction for the ensemble of

RAMS and MM5 studies. The mean an d standard deviation of wind speeds across these studies are
44.3% and + 42.8%, respectively. With the exception of the 3

- 12 August episode, all of the SAMI episodes had wind speed accuracies that were well within the 1 sigma range. One of the reasons wh y the

3

- 12 August episode produced larger percentage differences between predicted and observed winds was due to the systematically lower wind speeds that occurred during this episode and the fact that lower values in the denominator produce higher percent age values for the accuracy measure. When this fact is taken into account, we see that the RAMS performance for wind speed is also quite comparable to other recent

studies.

8.3.4 RMSE Errors

The mean and standard deviations of the RMSE errors, listed i n Table 8

- 4c, are 2.07 m s - 1 

### and + 0.38 m s -

1 

### , respectively. With the exception of the 8

- 13 February 1994 episode, all of the SAMI episodes had wind speed RMSE errors that were well within the 1 sigma range. Even considering the February episode, we

conc lude that the model’s performance based on the RMSE errors is also quite comparable to other recent studies. However, as discussed earlier, there remains concern over the model’s performance for the 8

- 10 February 1994 portion of the modeling period and th is interval is largely responsible for the larger than expected RMSE error for this episode.

### 296

8.3.5 Index of Agreement

The mean and standard deviations of the Index of Agreement are 0.74 and + 0.06, respectively. With the exception of the 24 April

–3 M ay 1991 episode, all of the SAMI episodes had wind speed RMSE errors that were well within the 1 sigma range. The April

- May episode is just outside the + 1 sigma range. Thus, for the index of agreement statistic as well, RAMS’ performance is quite consis tent with other MM5 and

RAMS simulations elsewhere in the U.S.

8.3.6 Average Discrepancy in Mean Wind Direction

Table 8

- 4c shows that the mean and standard deviations episode average wind direction difference are 24.6 degrees and 30.7 deg, respectively. Only the 23

- 31 March 1993 and 8

- 13 February 1994 episode fall

outside of the + 1 sigma range. Each episode has a mean wind direction difference of approximately 100 degrees. The remaining six episodes are well within the range. The directional biases in these two episodes should be examined further to assess their potential impact on air quality model calculations. Recall from Figures 8

- 7c and 8

- 7d that there were several periods during each episode, lasting from 1 to 3 days, when significant biases o

ccurred in the spatial mean predicted and observed wind directions. With these two exceptions, we believe that RAMS’ performance for wind direction is consistent with other MM5 and

RAMS studies.

### 297

Table 8

- 1. Statistical Summaries of RAMS Performance f or All SAMI Episodes Using the UAH Surface Layer Algorithms: 12 Km Grid Results. (a) Bias Statistics

Performance Attribute
Feb ‘94 July ‘91 July ‘95 Mar ‘93 May ‘93 May ‘95 June ‘92 Aug ‘93 April ‘95 Mean
Value Surface Temperature (deg F) 0.54

- 1.27 1.17

- 2.28

- 2.55

- 1.75

- 2.20

- 0.80

- 1.65

- 1.20

Surface Mixing Ratio (gm/Kg) - 0.00

- 0.37

- 1.45

- 0.16

- 0.30

- 0.29

- 0.32

- 1.76 +

- 0.77

- 0.60

Surface Wind Speed
( 

### ms - 1 ) 1.33 0.94 0.49 0.88 0.38 0.18 0.66 1.16 1.33 0.82

Surface Wind Direction (deg) - 3.76

- 2.29

- 8.16 12.30

- 3.68 1.30

- 0.08

- 1.11

- 1.39

- 0.76

Table 8

- 1. Statistical Summaries of RAMS Performance for All SAMI Episodes Using the UAH Surface Layer Algorithms: 12 Km Grid Results. (b) Root Mean Square Error Statistics

Performance Attribute
Feb ‘94 July ‘91 July ‘95 Mar ‘93 May ‘93 May ‘95 June ‘92 Aug ‘93 April ‘95 Mean
Value Surface Temperature (deg F) 5.04 4.01 4.37 5.38 4.80 4.17 4.24 3.82 4.26 4.45

Surface Mixing Ratio (gm/Kg) 0.59 0.90 2.56 0.67 1.06 1.03 1.07 4.12 + 1.96 1.5 5

Surface Wind Speed
( 

### ms - 1 ) 2.75 2.26 1.51 2.15 1.73 1.75 1.85 2.08 2.35 2.05

Surface Wind Direction (deg) 73.0 77.0 83.0 70.0 67.0 63.0 74.0 78.0 65.0 72.2

+ 

### Note: We suspect these particularly large mixing ratio statistics are the result of a breakd own in the surface layer vertical extrapolation scheme more than an indication of poor model performance as confirmed by the mixing ratio results for this episode described in section

6.7.

### 298

Table 8

- 2. RAMS Model Evaluation Statistics for Seven SAMI Episo des on the 12 Km Grid using the AG MAPS Model

Evaluation Software. 

### (a) Temperatures (deg C).

Performance
Attribute 24

- 29

May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value Mean Bias (deg C)

- 0.96

- 1. 46

- 1.33 0.51

- 0.40

- 1.10

- 0.80

- 0.79 Gross Error (deg C) 1.88 2.12 2.24 2.13 1.60 1.80 1.80 1.94

Table 8

- 2. RAMS Model Evaluation Statistics for Seven SAMI Episodes on the 12 Km Grid using the AG MAPS Model

Evaluation Software. 

### (b) Winds ( 

### ms - 1 

### ).

P erformance
Attribute 24

- 29

May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value

Mean Obs. Speed 2.15 1.56 1.89 2.37 1.11 1.54 1.86 1.78

Mean Pred. Speed 2.90 2.36 2.89 3.86 2.66 2.55 2.98 2.89

Speed Ac curacy (%) 35 51 53 63 140 66 60 67

RMSE 1.90 1.90 2.27 2.76 2.18 1.89 2.35 2.18

RMSE

S 1.30 1.38 1.69 2.15 1.72 1.38 1.84 1.64

RMSE

U 1.34 1.27 1.46 1.64 1.28 1.24 1.46 1.38

Index of Agreement 0.76 0.76 0.74 0.72 0.75 0.75 0.81 0.76

Skill E 0.63 0.68 0. 68 0.69 0.75 0.66 0.64 0.68

Skill VAR 0.88 0.97 1.03 1.03 1.02 0.84 1.15 0.99

Mean Obs. Dir. ( O ) 179 234 64 344 179 250 261

-- Mean Pred. Dir. ( O ) 192 228 164 241 204 230 257

-- Diff in Mean Dir. ( O ) 13 6 100 103 25 20 4 39

Table 8

- 2. RAMS Model Eva luation Statistics for Seven SAMI Episodes on the 12 Km Grid using the AG MAPS Model

Evaluation Software. 

### (c) Mixing Ratios (gm/Kg).

Performance
Attribute 24

- 29

May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value Mean Bias (gm/Kg) 0.00

- 0.01

- 0.02 0.00

- 0.60 0.03

- 0.10

- 0.10 Gross Error (gm/Kg) 0.81 0.83 0.55 0.44 1.10 1.00 0.70 0.78

### 299

Table 8

- 3. RAMS Model Evaluation Statistics for Seven SAMI Episodes on the 24 Km Grid using the AG MAPS Model

Eval uation Software. 

### (a) Temperatures (deg C).

Performance
Attribute 24

- 29

May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value Mean Bias (deg C)

- 0.97

- 1.24

- 0.29 0.80

- 0.50

- 0.90

- 0.70

- 0.54

Gross Error (deg C) 2.11 2.28 2.08 2.46 1.80 1.90 1.80 2.06

Table 8

- 3. RAMS Model Evaluation Statistics for Seven SAMI Episodes on the 24 Km Grid using the AG MAPS Model Evaluation Software. (b) Winds (ms
- 1 ).

Performance
Attribute 24

- 29 May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value

Mean Obs. Speed 1.68 1.21 1.09 1.81 1.08 1.05 1.29 1.32

Mean Pred. Speed 2.53 2.45 1.32 3.07 2.43 2.30 2.06 2.31

Speed Accuracy (%) 51 102 21 70 125 119 60 78

RMSE 2.60 2.5 0 2.57 3.43 2.41 2.17 2.51 2.60

RMSE

S 1.58 1.67 1.79 2.35 1.67 1.40 1.71 1.74

RMSE

U 2.03 1.82 1.81 2.44 1.56 1.61 1.78 1.86 Index of Agreement 0.80 0.82 0.83 0.77 0.78 0.84 0.85 0.82

Skill E 0.77 0.74 0.68 0.82 0.84 0.72 0.65 0.75

Skill VAR 1.11 1.06 1. 19 1.23 1.18 1.07 1.20 1.15 Mean Obs. Dir. ( O ) 163 255 58 1 196 252 262

-- Mean Pred. Dir. ( O ) 187 236 140 242 210 241 248

-- Diff in Mean Dir. ( O ) 24 19 82 119 14 11 14 40

Table 8

- 3. RAMS Model Evaluation Statistics for Seven SAMI Episodes on the 2 4 Km Grid using the AG MAPS Model

Evaluation Software. 

### (c) Mixing Ratios (gm/Kg).

Performance
Attribute 24

- 29

May ‘95 11

- 17

May ‘93 23

- 31

Mar ‘93 8

- 13 Feb

‘94 3

- 12

Aug ‘93 22

- 29

Jun ‘92 24 Ap

- 3

May ‘95 Mean

Value Mean Bias (gm/Kg) 0.07 0.12 0.21 0.26

- 0.40

- 0.20 0.00 0.01 Gross Error (gm/Kg) 0.89 0.86 0.66 0.65 1.10 0.90 0.70 0.82

### 300

Table 8

- 4

. Summary of prognostic meteorological model performance evaluations by A lpine G eophysics: 12 km grid resolution results. (a) Surface Temperatures (deg C).

Modeling Domain Model Study Ref Episode Mean Bias (deg C) Gross Error

(deg C)

Southeastern U.S. RAMS SAMI 7 24

- 29 May ‘95

- 1.0 1.9

Southeastern U.S. RAMS SAMI 7 11

- 17 May ‘93

- 1.5 2.1

Southeastern U.S. RAMS SAMI 7 23

- 31 Mar ‘93

- 1.3 2.2

Southeaster

n U.S. RAMS SAMI 7 8

- 13 Feb ‘94 0.5 2.1

Southeastern U.S. RAMS SAMI 7 3

- 12 Aug ‘93

- 0.4 1.6

Southeastern U.S. RAMS SAMI 7 22

- 29 Jun ‘92

- 1.1 1.8

Southeastern U.S. RAMS SAMI 7 24 Apr

- 3 May ‘91

- 0.8 1.8

Texas MM5 COAST 11 4

- 11 Sept ‘93 0.2 1.8

Central F lorida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99 0.1 1.5

Central Florida MM5 PFOS

- Episode 2 10 2

- 10 May ‘97 0.2 1.6

Central Florida MM5 PFOS

- Episode 3 10 25

- 30 Aug ‘97 0.2 1.7

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘99

- 0.4 1.3

Central Florida MM5 PFOS

- Episode 5 10 17

- 23 Sept ‘97 0.1 1.6

Central Florida MM5 PFOS

- Episode 9 10 20

- 28 Apr ‘98 0.3 1.3

Midwestern U.S. MM5 Kansas/Missouri 8 11

- 24 Jun ‘95

- 0.1 1.8

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 0.8 2.4

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90 0.2 2.9 Upper Midwest RAMS CRC

- LMO`S 6 26

- 28 Jun ‘91 0.1 1.4 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91

- 0.0 1.9 Upper Midwest MM5 CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.5 1.6 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91

- 0.3 1.7

Eastern U.S. RAMS OTAG 3 13

- 21 Jul ‘91 1.6 2.1

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘91

- 0.1 2.0

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88

- 0.6 3.3

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95

- 0.2 2.0

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94

- 0.7 2.4

Southeastern U.S. MM5 BAMP 9 6

- 1 1 Sep ‘93

- 0.4 2.1

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93

- 0.3 2.4

Maximum Value

- 1.5 1.3

Minimum Value 1.6 3.3

Mean Value

- 0.2 1.9

Standard Deviation 0.6 0.5 1 Sigma Range

- 0.8 to 0.4 1.4 to 2.4

### 301

Table 8

- 4

. Summary of prognostic meteorologi cal model performance evaluations by A lpine G eophysics: 12 km grid resolution results. (b) Surface Mixing Ratio (gm/Kg).

Modeling Domain Model Study Ref Episode Mean Bias (gm/Kg) Gross Error

(gm/Kg)

Southeastern U.S. RAMS SAMI 7 24

- 29 May ‘95 0.0 0 .8

Southeastern U.S. RAMS SAMI 7 11

- 17 May ‘93

- 0.0 0.8

Southeastern U.S. RAMS SAMI 7 23

- 31 Mar ‘93

- 0.0 0.6

Southeastern U.S. RAMS SAMI 7 8

- 13 Feb ‘94 0.0 0.4

Southeastern U.S. RAMS SAMI 7 3

- 12 Aug ‘93

- 0.6 1.1

Southeastern U.S. RAMS SAMI 7 22

- 29 Jun ‘92 0.0 1.0

Southeastern U.S. RAMS SAMI 7 24 Apr

- 3 May ‘91

- 0.1 0.7

Texas MM5 COAST 11 4

- 11 Sept ‘93 0.1 1.4

Central Florida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99

- 0.1 1.2

Central Florida MM5 PFOS

- Episode 2 10 2

- 10 May ‘97 0.1 1.2

Central Florida MM5 P FOS

- Episode 3 10 25

- 30 Aug ‘97

- 2.0 2.3

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘99 0.8 1.5

Central Florida MM5 PFOS

- Episode 9 10 17

- 23 Sept ‘97

- 0.4 1.6

Central Florida MM5 PFOS

- 10 20

- 28 Apr ‘98

- 0.2 0.9

Midwestern U.S. MM5 Kansas/Missouri 8 11

- 24 Jun ‘95

- 0.4 1.3

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 0.2 2.2

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90

- 0.2 1.9 Upper Midwest RAMS CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.1 1.2 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91 0.4 1.4 Upper Midwest M M5 CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.1 1.2 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91

- 0.6 1.5

Eastern U.S. RAMS OTAG 3 13

- 21 Jul ‘91

- 0.0 1.2

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘91

- 0.3 1.4

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88

- 1.4 2.0

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95

- 1.5 2.2

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94

- 1.6 2.2

Southeastern U.S. MM5 BAMP 9 6

- 11 Sep ‘93

- 0.6 1.0

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93

- 1.5 1.9

Maximum Value

- 2.0 0.4

Minimum Value 0.8 2.3

Mean Value

- 0.4 1.4

Standard Deviation 0.7 0.5 1 Sigma Range

- 1.1 to 0.3 0.9 to 1.9

### 302 Table 8

- 4

. Summary of prognostic meteorological model performance evaluations by A lpine G eophysics: 12 km grid resolution

results

. (c) Surface Wind Speeds.

Modeling Domain

Model Study Ref Episode Average

Error (%) RMSE

( 

### ms - 1 ) Index of

Agreement Mean

Wind
Dir.
Diff. (deg)

Southeastern U.S RAMS SAMI 7 24

- 29 May ‘95 35.0 1.90 0.76 13.0

Southeastern U.S RAMS SAMI 7 11

- 17 May ‘93 51.0 1.90 0.76 6.0

Southeastern U.S RAMS SAMI 7 23

- 31 Mar ‘93 53.0 2.27 0.74 100.0

Southeastern U.S RAMS SAMI 7 8

- 13 Feb ‘94 63.0 2.76 0.72 103.0

Southeastern U.S RAMS SAMI 7 3

- 12 Aug ‘93 140.0 2.18 0.75 25.0

Southeastern U.S RAMS SAMI 7 22

- 29 Jun ‘92 66.0 1.89 0.75 20.0

Southeaster

n U.S RAMS SAMI 7 24 Apr

- 3 May ‘91 60.0 2.35 0.81 4.0

Texas MM5 COAST 11 4

- 11 Sept ‘93 61.4 2.20 0.69 15.0

Central Florida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99 20.9 1.94 0.78 10.0

Central Florida MM5 PFOS

- Episode 2 10 2

- 10 May ‘97 21.0 1.95 0.78 32.0

Ce ntral Florida MM5 PFOS

- Episode 3 10 25

- 30 Aug ‘97 30.6 1.86 0.73 32.0

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘97 18.1 1.80 0.80 8.0

Central Florida MM5 PFOS

- Episode 5 10 17

- 23 Sept ‘97 27.9 1.84 0.72 9.0

Central Florida MM5 PFOS

- Episode 9 10 2 0

- 28 Apr ‘98 24.0 1.79 0.78 26.4

Midwestern U.S. MM5 Kansas/Missouri 8 11

- 24 Jun ‘95 16.7 2.20 0.80 20.0

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 12.6 1.78 0.75 8.0

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90 22.6 2.13 0.80 3.0 Upper Midwest RAMS CRC

- LMOS 6 26

- 28 Jun ‘91 11.9 1.82 0.69 16.7 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91 3.5 1.73 0.64 7.4 Upper Midwest MM5 CRC

- LMOS 6 26

- 28 Jun ‘91 5.8 1.70 0.79 14.0 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91 15.6 1.65 0.77 7.4

Eastern U.S. RAMS OTA G 3 13

- 21 Jul ‘91 4.6 1.61 0.74 27.1

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘91 23.0 1.92 0.73 17.0

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88 65.6 3.21 0.64 7.9

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95 21.2 1.91 0.68 15.2

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94 82.4 2.69 0.80 0.1

Southeastern U.S. MM5 BAMP 9 6

- 11 Sep ‘93 89.4 2.36 0.60 21.5

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93 193.6 2.66 0.65 120.0

Maximum Value 3.5 1.61 0.60 0.1

Minimum Value 193.6 3.21 0.81 120.0

Mean Value 44.3 2.07 0.74 24.6

Standard Deviation 42.8 0.38 0.06 30.7 1 Sigma Range 1.5 to

87.1 1.69 to

2.45 0.68 to 0.80 0.0 to

55.3

REFE RENCES for Table 8

- 4.

1. McNally, D. E., and T. W. Tesche, 1996a. “Pittsburgh Regional Ozone Attainment Study: Evaluation of the MM5 Model

for Three Episodes”, prepared for the Pennsylvania Department of Environmental Protection, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

2. McNally, D. E., and T. W. Tesche, 1996b. “Evaluation of the MM5 Model for the 1

- 11 July 1998 OTAG Episode Over the
Northea stern United States”, prepared for the Pennsylvania Power and Light Company, prepared by Alpine Geophysics, LLC, Arvada, CO.

### 303

3. Tesche, T. W., and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study

– Phase II, Work Element 5

Technical R eport: Comparative Evaluation of the MM5 and RAMS Models for the July 1991 OTAG Episode”, prepared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

4. Tesche, T. W., and D. E. McNally, 1997. “The Use of the San Joaquin Valley Meteorological Model in Preparation of a Field Program in the South Coast Air Basin and Surrounding Regions of Southern California. Volume I: Final MM5
Evaluation for the 3

- 6 August 1990 SARMAP Episode”, prepared for the California Air Resources Board, pre pared by Alpine Geophysics, LLC, Ft. Wright, KY.

5. Tesche, T. W., and D. E. McNally, 1998. “Cincinnati

- Hamilton Ozone Attainment Demonstration Study: Volume 5:
Evaluation of the MM5 Model for the 18

- 22 June 1994 Episode”, prepared for the Ohio Environme ntal Protection Agency”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

6. Tesche, T. W., and D. E. McNally, 1999. “Comparative Evaluation of the MM5 and RAMS3c Prognostic Meteorological
Models over the Midwestern U.S. for Two 1991 LMOS Intensive Mea surement Episodes”, prepared for the Coordinating Research Council, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

7. Tesche, T. W., and D. E. McNally, 2000a. “Evaluation of the RAMS3c Prognostic Meteorological Model over the
Southeastern U.S. for Th ree Southern Appalachian Mountain Initiative (SAMI) Episodes”, draft final report prepared for the Tennessee Valley Authority and SAMI, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

8. Tesche, T. W., and D. E. McNally, 200b. “Evaluation of the MM5 M esoscale Model Over the Central U.S. For the 11

- 24 June 1995 Ozone Episode”, prepared for the Kansas Department of Health and Environment and the U.S. Environmental Protection Agency”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

9. Tesche, T. W., and D. E. McNally, 1998. “Evaluation of the MM5 Model for Two 1993 Regional Ozone Episodes over the Gulf Coast”, prepared for the Minerals Management Service and the Offshore Operators Committee, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

10. Tes che, T. W., and D. E. McNally, 2001a. “Evaluation of the MM5 Prognostic Meteorological Model over Central Florida for Nine Peninsular Florida Ozone Study (PFOS) 8

- hr Ozone Episodes”, report prepared for the Florida Department of Environmental Protection Ag ency, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

11. Tesche, T. W., and D. E. McNally, 2001. “Application of the MM5 Prognostic Meteorological Model over the South
Central U.S. for the September 1993 COAST Ozone Episode”, prepared by Alpine Geoph ysics, LLC, Ft. Wright, KY



### .

### 304 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5Temperature Bias(deg F)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (a) Bias

0.0 1.0 2.0 3.0 4.0 5.0 6.0Temperature RMSE(deg F)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (b) RMSE

Figure 8

- 1 . Episode Average 2m Temperature Statistics for all SAMI Episodes

.

### 305 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4Wind Speed Bias (m/s)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (a) Bias

0.0 0.5 1.0 1.5 2.0 2.5 3.0Wind Speed RMSE(m/s)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (b) RMSE

Figure 8

- 2. Episode Average 10 m Wind Speed Statistics for all SAMI Episodes

.

### 306 -10.0 -5.0 0.0 5.0 10.0 15.0Wind Direction Bias(degrees)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (a) Bias

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0Wind Direction RMSE(degrees)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (b) RMSE

Figure 8

- 3. Episode Average 10 m Wind Direction Statist ics for all SAMI Episodes

.

### 307 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0Mixing Ratio Bias(gm/Kg)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (a) Bias

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5Mixing Ratio RMSE(gm/Kg)

Feb '94 July '91 July '95 Mar '93 May '93 May '95 Jun '92 Aug '93 Apr '95 AVERAGE



### (b) RMSE

Figure 8

- 4. Episode Average 2m Mixing Ratio Statistics for all SAMI Episodes. (We suspect the poor mixing ratio statistics for the July 1995 and August 1993 episodes is due to a breakdown in the surface layer vertical extrapolation scheme more than an indication of poor model performance).

### 308 (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

Figure 8

- 5. Spatial Mean Ground

- Level Temperatures for Seven SAMI Episodes

.

### 309 (c) 23

- 31 March 1993

(d) 8

- 13 February 1994

Figure 8

- 5. Continued.

### 310

### 311 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 5. Continued.

### 312 (g) 24 April

–3 May 1995

Figure 8

- 5. Concluded.

### 313 (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

Figure 8

- 6. Vector Mean Ground

- Level Wind Speeds for Seven SAMI Episodes.

### 314 (c) 23

- 31 March 1993

(d) 8

- 13 February 1994

Figure 8

- 6. Continued.

### 315 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 6. Continued.

### 316 (g) 24 April

–3 May 1995

Figure 8

- 6. Concluded.

### 317 (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

Figure 8

- 7 . Mean Ground

- Level Wind Direction for Seven SAMI Episodes.

### 318 (c) 23

- 31 March 199 3

(d) 8

- 13 February 1994

Figure 8

- 7. Continued.

### 319 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 7. Continued.

### 320 (g) 24 April

–3 May 1995

Figure 8

- 7 . Concluded.

### 321 (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

Figure 8

- 8 . Index of Agreement in Ground

- Level Wind Speeds for Seven SAMI Episodes

.

### 322 (c) 23

- 31 March 1993

(d) 8

- 13 February 1994

Figure 8

- 8. Continued.

### 323 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 8. Continued.

### 324 (g) 24 April

–3 May 1995

### 325

Figure 8

- 8. Concluded.



### (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

### 326

Figure 8

- 9. RMSE Errors in Ground

- Level Wind Speeds for Seven SAMI Episodes

.



### (c) 23

- 31 March 1993

(d) 8

- 13 Febr uary 1994

Figure 8

- 9 . Continued.

### 327 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 9. Continued.

### 328 (g) 24 April

–3 May 1995

Figure 8

- 9. Concluded.

### 329 (a) 24

- 29 May 1995

(b) 11

- 17 May 1993

Figure 8

- 10. Spatial Mean Ground

- Level Mixing Ratios for Seven SAMI Episodes.

### 330 (c) 23

- 31 March 1993

(d) 8

- 13 February 1994

Figure 8

- 10. Continued.

### 331 (e) 3

- 12 August 1993

(f) 22

- 29 June 1992

Figure 8

- 10. Continued.

### 332 (g) 24 April

–3 Ma y 1995

Figure 8

- 10. Concluded.

### 333

9.0 SUMMARY AND CONCLUSIONS

This chapter presents the summary and conclusions of the RAMS model performance evaluation for nine (9) SAMI episode. We begin with a distillation of the operational evaluation results for surface winds, temperatures, mixing ratios and aloft winds and temperatures. Subsequently, we summarize the performance of the model in simulating the aloft winds and temperatures. Given the weak meteorological

database available for model performance testing, the only scientific evaluation that was possible involved the comparison of modeled and observed daily average and six

- hourly precipitat ion totals. We conclude with a formal process for assessing the suitability of the RAMS modeling episodes for air quality model applications based , in part, on comparisons with other contemporary prognostic model evaluations that have been accepted by EPA, various state governments and research groups in recent regulatory and applied

research applications.

9.1. Operational Evaluation Summary

The operational summary for the six UAH ep isodes will utilize Tables 9

- 1.1

- 9

- 1.3. Table 9

- 1.1 is a summary of the bias and RMSE statistics for the 12

- km domain. Table 9

- 1.2 gives the index of agreement

for all the near

- surface variables for each episode day. Table 9

- 1.3 gives the bias and RMSE statistics for the grid points of the respective coarse grid which are contained within the 12

- km grid for the 850

- mb values of temperature, water vapor mixing r atio, and wind speed. Although derived by different software and presented differently the same variables will be discussed here as in the operational summary for the three AG episodes.

9.1.1 9

- 20 July 1995

The July 1995 episode did not have the benefi t of the surface nudging technique used on all the other episodes. This is demonstrated especially by the bias and RMSE of the 2

- m mixing ratio of

- 1.45 and 2.56 g kg

- 1 

### , respectively, as shown in Table 9

- 1. The affect of the dry bias was partially mitiga ted by the provision of a boundary layer water vapor mixing ratio correction file to GIT for the air quality modeling phase. However, this did not change the cloud or precipitation deficiencies of this simulation. The RMSE

of the wind direction at 10 m w as also the largest of any of the UAH episodes at 83 degrees. This was due largely to the very light wind conditions which predominated this period. Examination of the daily index of agreement values for this episode in Table 9

- 2 shows the lowest mean va lues of all the episodes for temperature and mixing ratio of 0.69 and 0.62, respectively. Overall the index of agreement values were the lowest for the period 16

- 19 July when clouds and precipitation were more prevalent. The performance

of the RAMS simul ation at 850

- mb for the 48

- km grid points within the 12

- km domain is given in Table 9

- 3. The most outstanding statistic was the RMSE for mixing ratio of 2.18 g kg

- 1 

### which was the largest of all the UAH episodes.

9.1.2 22

- 30 May 1995

Using the results of the surface performance in Table 9

- 1 this episode had a cool bias of about

- 0.98 o 

### C and relatively excellent wind results with a wind speed RMSE value of 1.75 m s

- 1 

### and a wind direction RMSE

### 334

value of 63 degrees. The surface wind performance is also veri fied by the index of agreement values in
Table 9

- 2 where the mean values for wind speed and wind direction were 0.63 and 0.77, respectively, which were the highest or equal for any of the UAH episodes. The mean index of agreement for temperature of 0.84 h

ad a rank of second best while the mean index of agreement for mixing ratio of 0.90 had a rank of fourth best but still at a high value. Temperature and moisture performance was diminished on 25 May when the respective daily index values dropped to 0.69 a nd 0.75. The most outstanding statistic of performance at 850 mb in Table 9

- 3 was the cool bias of

- 1.59 o 

### C which was the second coolest of all the episodes.

9.1.3 9

- 18 May 1993

This episode had the largest surface cool bias of

- 1.43 o 

### C but with relativ ely excellent wind results with a wind speed RMSE value of 1.73 m s

- 1 

### and a wind direction RMSE value of 67 degrees. Again the surface wind performance is verified by the index of agreement values in Table 9

- 2 where the mean values for wind speed and wind direction were 0.62 and 0.77, respectively, which were ranked first or second relative to the other UAH episodes. The mean index of agreement for temperature of 0.74 had a rank of fifth best while the mean index of agreement for mixing ratio of 0.87 had a rank of fifth best as well. Overall behavior with respect to the indices of agreement was reduced for the following days: for 11 May when the temperature index was 0.69 and the wind speed index was 0.57; and for 16 May when the temperature

index was 0.5 5 and the wind direction index was 0.72. The outstanding statistics of performance at 850 mb
in Table 9

- 3 were the temperature had a cool bias of

- 1.78 o 

### C which was the coolest of all the UAH episodes and a RMSE for temperature of 2.11 o 

### C which was also the largest of the UAH episodes.

9.1.4 21 March

- 1 April 1993

Table 9

- 1 shows that this episode had the largest RMSE for temperature at 2 m of 3.01 o 

### C and also the second largest RMSE for wind speed at 10 m of 2.15 m s

- 1 

### . The index of agreement values in Table 9

- 2 show that the mean temperature and mixing ratio values of 0.82 and 0.93, respectively, were ranked third best while the wind speed and wind direction values of 0.54 and 0.76 were ranked second or third. Overall behavior relative to the daily in dices of agreement was reduced for 25

- 26 March because of wind and for 30 March because of temperature and mixing ratio. The outstanding statistic of performance at 850 mb in
Table 9

- 3 was the RMSE value for wind speed of 2.81 m s

- 1 

### which was the second l argest of all the UAH episodes. The large values RMSE at the surface and at 850 mb are a reflection of the same problems revealed by the AG MAPS software as discussed in section 8. Since heavy precipitation fell over much of

the 12

- km grid it is likely t hat a major fraction of the surface temperature problems are related to cloud placement and thickness. The surface and 850

- mb wind issues are harder to identify but may be related to

the need for better vertical resolution for a late

- winter case where the upper

- level jet stream is strong.

9.1.5 6

- 14 February 1994

Table 9

- 1 shows that this episode had the second largest RMSE for temperature at 2 m of 2.82 o 

### C and also the largest RMSE for wind speed at 10 m of 2.75 m s

- 1 

### . The index of agreement values in T able 9

- 2 show that the mean temperature and mixing ratio values of 0.94 and 0.97, respectively, were ranked first while the

### 335

wind speed and wind direction values of 0.53 and 0.73 were ranked third or fourth. As mentioned in the

discussion in section 8 the AG MAPS software showed that other statistical measures show significant problems with the temperatures at 2 m on the 12

- km grid for portions of this episode. Examination of hourly differences between surface observations analyzed to the 12

- km grid and th e model values (not shown) confirmed this behavior. The model was too cold for the period 8

- 10 February for areas in and close to the southern Appalachians and then too warm for the period 11

- 13 February for an area from Georgia northeastward through the Carolinas. Reasons why the index of agreement remain high for temperature for this episode are the fortuitous location of observing sites and the fact that the index of agreement is less sensitive to model departures from observations when the observations themselves have a broad spread around the mean. The latter point is illustrated by the 1200 UTC 9 February 1994 surface analysis in the NOAA publication Daily Weather Maps which shows 2 m temperatures ranging from near

14 o 

### F in central Indiana to near 65 o 

### F over South Carolina. For these reasons the comparison of the daily values of the index of agreement have less value for this episode. The same trends are observed at 850 mb
in Table 9

- 3 where the RMSE values for temperature at 2.51 o 

### C and wind speed at 4.25 m s

- 1 

### were the largest of any UAH episode. As with the March 1993 simulation this episode may have required more vertical resolution at higher levels with such a strong upper

- level jet. At least with respect to surface temperatures the 96

- km grid ap peared to perform better then the 12

- km grid. Reasons for this are not clear but may involve the choice of diffusion parameters for the 12

- km grid.

9.1.6 21 July

- 1 August 1991

Table 9

- 1 shows that this episode had the second largest RMSE for wind spee d at 10 m of 2.26 m s

- 1 

### and as well the second largest RMSE for wind direction at 10 m of 77 degrees. This is likely the result of generally light surface winds in combination with widespread convective outflows from thunderstorms which are handled very p oorly with models. Examination of the daily indices of agreement in Table 9

- 2 for wind direction and wind speed show the highest values were in the middle of the episode with a decrease in the same indices for the period 29

- 31 July. As discussed in secti on 4.6 this is consistent with the timing of the widespread convection east of the Appalachians. The performance at 850 mb as shown in Table 9

- 3 was

close the mean values for the bias and RMSE for all variables.

Table 9

- 1 . B ias and root mean square error (RMSE) for UAH episodes. Statistics based on hourly

12

- km variables interpolated to all available NWS sites within the 12

- km domain. Variables are: temperature at 2 m, T, o C; water vapor mixing ratio at 2 m, Q, g kg
- 1 ; win d speed at 10 m, WSPD, m

s - 1 ; and wind direction at 10 m, WDIR, degrees.

EPISODE T

BIAS T

RMSE Q
BIAS Q
RMSE WSPD

BIAS WSPD
RMSE WDIR
BIAS WDIR

RMSE July 95 +0.66 2.45

- 1.45 2.56 +0.49 1.51

- 8.16 83.0 May 95

- 0.98 2.34

- 0.29 1.03 +0.18 1.75 +1.30 63 .0 May 93

- 1.43 2.69

- 0.30 1.06 +0.38 1.73

- 3.68 67.0

March 93

- 1.28 3.01

- 0.16 0.67 +0.88 2.15 +12.30 70.0

Feb 94 +0.30 2.82

- 0.00 0.59 +1.33 2.75

- 3.76 73.0 July 91

- 1.27 2.25

- 0.37 0.90 +0.94 2.26

- 2.29 77.0

### 336

Table 9

- 2

. Daily index of agreement statistics for temperature at 2 m (T), water vapor mixing ratio at 2 m (Q), wind direction at 10 m (WDIR), and wind speed at 10 m (WSPD) for each of the six episodes modeled by UAH. Statistics are for the day indicated ending at 1200 UTC. Statistics are based on 12

- km model data interpolated to all available NWS sites within the 12

- km grid domain. Cells shaded green have the highest values for a given variable, while cells shaded red have the lowest values. July 1995 Episod

e July
10 July
11 July
12 July
13 July
14 July
15 July
16 July
17 July 18 July

19 July
20 MEAN

T 0.86 0.78 0.76 0.75 0.65 0.64 0.64 0.62 0.60 0.57 0.70 0.69 Q 0.84 0.69 0.64 0.60 0.53 0.50 0.52 0.43 0.44 0.75 0.83 0.62

WDIR 0.73 0.71 0.69 0.65 0.73 0. 69 0.62 0.71 0.66 0.62 0.62 0.68

WSPD 0.42 0.55 0.35 0.47 0.65 0.66 0.59 0.52 0.51 0.48 0.39 0.51 May 1995 Episode May
23 May
24 May
25 May
26 May
27 May
28 May
29 May
30 MEAN

T 0.90 0.79 0.69 0.87 0.92 0.88 0.87 0.85 0.84 Q 0.95 0.90 0.75 0.81 0.93 0.95 0.93 0.94 0.90

WDIR 0.87 0.91 0.78 0.72 0.77 0.66 0.64 0.80 0.77

WSPD 0.61 0.70 0.71 0.47 0.53 0.62 0.70 0.73 0.63 May 1993 Episode May
10 May
11 May
12 May
13 May
14 May
15 May
16 May
17 May
18 MEAN

T 0.78 0.69 0.64 0.79 0.82 0.66 0.55 0.86 0 .90 0.74 Q 0.83 0.74 0.78 0.85 0.97 0.93 0.83 0.90 0.97 0.87

WDIR 0.82 0.72 0.77 0.69 0.94 0.78 0.72 0.74 0.75 0.77

WSPD 0.60 0.57 0.72 0.66 0.55 0.68 0.67 0.59 0.55 0.62 March 1993 Episode

Mar
22 Mar
23 Mar
24 Mar
25 Mar
26 Mar
27 Mar
28 Mar
29 Mar 30 Mar

31 Apr 1 MEAN

T 0.96 0.91 0.92 0.89 0.75 0.77 0.87 0.75 0.72 0.78 0.75 0.82 Q 0.95 0.99 0.97 0.95 0.94 0.96 0.92 0.88 0.85 0.89 0.94 0.93

WDIR 0.77 0.63 0.67 0.71 0.61 0.67 0.93 0.92 0.88 0.86 0.75 0.76

WSPD 0.49 0.60 0.48 0.39 0.52 0.53 0.56 0 .57 0.62 0.65 0.51 0.54 February 1994

Feb

7 Feb
8 Feb
9 Feb
10 Feb
11 Feb
12 Feb
13 Feb
14 MEAN

T 0.93 0.97 0.95 0.97 0.96 0.90 0.92 0.91 0.94 Q 0.98 0.99 0.98 0.99 0.98 0.95 0.97 0.95 0.97

WDIR 0.65 0.82 0.55 0.76 0.65 0.86 0.71 0.86 0.73

WSPD 0.5 6 0.34 0.39 0.56 0.63 0.58 0.62 0.60 0.53 July 1991 Episode July
22 July
23 July
24 July
25 July
26 July
27 July
28 July
29 July
30 July
31 Aug

1 MEAN

T 0.79 0.73 0.80 0.78 0.84 0.84 0.84 0.78 0.82 0.71 0.72 0.79 Q 0.89 0.89 0.94 0.97 0.97 0.98 0.99 0.99 0.98 0.98 0.98 0.96

WDIR 0.74 0.69 0.82 0.83 0.85 0.93 0.81 0.58 0.58 0.82 0.88 0.77

WSPD 0.61 0.56 0.60 0.60 0.56 0.45 0.46 0.42 0.40 0.32 0.39 0.49

### 337

Table 9

- 3 . Bias and root mean square error (RMSE) of the UAH episo des for variables at 850

- mb for coarse grid points within the 12

- km domain. Statistics based on 12

- h model and NCEP/NCAR reanalysis data. Variables are: temperature, T, o C; water vapor mixing ratio, Q, g kg
- 1 ; and wind speed, WSPD, m s
- 1

.

T

- BIAS T

- RMSE Q

- BIAS Q

- RMSE W

- BIAS W

- RMSE July 95

- 0.97 1.86 +1.55 2.18

- 0.08 1.59 May 95

- 1.59 1.83 +0.73 1.30 +0.52 1.94 May 93

- 1.78 2.11 +0.80 1.41 +0.37 1.99

March 93

- 1.05 1.68 +0.61 1.22 +0.84 2.81

Feb 94 +1.30 2.51 +0.49 1.25 +2.70 4.25 July 91

- 1.37 1.73 +0.87 1.60 +0.96 2.55

MEAN

- 0.91 1.95 +0.84 1.49 +0.89 2.52



### 9.1. 7 22

- 29 June 1992

RAMS did a very job of reproducing the daily maximum temperatures on each day with the exception of 26 June where the peak was underestimated by 3.4 ° 

### C. The mean bias i n predicted hourly surface temperatures across the 12 km domain ranged from

- 1.7 ° 

### C to

- 0.2 ° 

### C with an episode mean of

– 1.1 ° 

### C. The average gross errors in surface temperature predictions for the 8 days were 1.8 ° 

### C. RAMS generally follows the hourly mean temp erature measurements fairly well; however, the model systematically underestimated the afternoon spatial mean peak values on each day and this discrepancy worsened as the episode progressed. The under

- prediction of the afternoon peaks was more pronounced than the August episode where RAMS clipped the afternoon peaks by a one to two degrees C. RAMS tended to slightly underestimate (~ 5% to 10%) hourly temperatures in the morning hours and on a couple of days to over predict (~ 2%

- 3%) midday.

RAMS estima ted mean wind speeds (2.55 m s - 1 

### ) that were on average 66% greater than the magnitude of the episode mean observations (1.54 m s - 1 

### ). Mean wind speeds were higher than those encountered in the August 1993 period. Modeled wind directions showed reasonable agreement with the observations on most days. Across the 8

- day episode, the mean modeled (229.7 deg rees ) and observed surface wind directions (250.3 degrees) differed by only 21 degrees. From day

- to

- day, the difference between daily average and observed wind direction varied from 10 to 150 degrees.

Episode average values of the unsystematic, systematic, and total RMSE errors were 1.24 m s - 1 

### , 1.38 m s - 1



### and 1.89 m s - 1 

### , respectively. These results were an improvement over the August 1993 episode. There di d not appear to be any significant error growth throughout the latter two thirds of the simulation. Slight error growth occurs during the first three days of the episode when the errors were somewhat larger than for the rest of the episode days. The prep onderance of the RMSE error was from the systematic component.

The index of agreement results for the June 1992 episode were quite consistent with other prognostic model evaluations. RAMS exhibited typical hourly variation in the agreement index parame ter with the lowest values occurring during the morning period when wind speeds were lowest and the directions were more variable. The index increased in the afternoon when speeds were greater. The mean value of the index of agreement varied diurnally thr oughout the episode; the mean over the whole period was 0.75

### 338

For near surface mixing ratios the episode mean daily maximum observed (19.3 g/Kg) and modeled (16.8 g/Kg) agreement was fairly good. RAMS systematically underestimated the maximum ratio on eac h day except 22 June by 2 to 4 g/Kg. The mean bias and error in mixing ratios was also quite good, with 8

- day

mean values of

- 0.3 g

/Kg and 1.0 g

/Kg respectively.

There was good agreement between the predicted and observed vertically averaged temperatur es for each simulation day. The vertically

- averaged predicted and observed temperatures on the 12 km domain

were13.5 ° 

### C and 13.8 ° 

### C, respectively. The mean predicted and observed wind aloft wind speeds were 7.3

m s - 1 

### and 6.6 m s - 1 

### , giving a 11% discrepancy

.

9.1.8 24 April

–3 May 1995

For the 24 April

–3 May 1995 SAMI episode RAMS did a very job of reproducing the daily maximum temperatures on each day. The episode

- averages of the daily maximum observed and predicted temperatures were 26.9 ° 

### C and 26.6 ° 

### C , respectively, for a 1.1% discrepancy. The mean bias in predicted hourly surface temperatures across the 12 km domain ranged from

- 1.9 ° 

### C to 0.7 ° 

### C. On average across the episode, the mean bias in surface temperature prediction

– 0.8 ° 

### C. The average gross errors in surface temperature predictions for the 10 days were 1.8 ° 

### C, the same as the June 1992 episode. RAMS followed the hourly mean temperatures fairly well but, as with the August 1993 and June 1992 episodes, the model systematically underestimated the afternoon peaks in the latter part of April. However, the model did a good job on the 1 st 

### and 2 nd 

### of May. These discrepancies at midday were typic ally on the order of 2 to

3 ° 

### C. The under

- prediction of the afternoon peaks was more pronounced than the Au gust 1993 episode but about the same as the June 1992 episode. The hourly temperature biases for the April

- May episode were significantly larger than those for the August and June episodes. RAMS underestimated (~ 10% to 15%) hourly temperatures in the af ternoon and evening hours and during the morning as well on several days.

RAMS estimated mean wind speeds (2.98 m s - 1 

### ) that were on average 60% greater than the magnitude of the episode mean observations (1.86 m s - 1 

### ). RAMS overestimated the daily averag e observed wind speeds during this episode by about the same amount as the June 1992 episode. Mean wind speeds were also higher than those encountered in the August 1993 period. Modeled wind directions were in reasonable agreement with the observations on the first five days of the episode and fairly poor agreement on the last five days. Across the 10

- day episode, the mean modeled (257.0 degrees) and observed surface wind directions (261.4 degrees) differed by only 4 degrees. From day

- to

- day, however, th

e discrepancies between daily average and observed wind direction varied from 2 to 158 degrees. On the 29 th 

### through the

3 rd 

### of May, the mean (absolute) discrepancy between daily averaged modeled and observed wind direction

was 131 degrees.

The episode

average values of the unsystematic, systematic, and total RMSE errors were 1.46 m s - 1 

### , 1.84

m s - 1 

### and 2.35 m s - 1 

### , respectively. While there did not appear to be any significant error growth during the simulation, the RMSE errors were greater then the Jun e and August episodes. The systematic component (model physics

- related) of the RMSE error was clearly the larger contributor to the total RMSE error in this episode.

### 339

The index of agreement results for the April

- May 1995 episode were quite good. This is somewhat surprising given the poorer wind direction and RMSE error performance of the model for this episode relative to the August 1993 and June 1992 periods. RAMS exhibited typical hourly variation in the agreement index parameter with the lowest val ues occurring during the morning period. In the afternoon when speeds were greater, the index increased. The mean value of the index of agreement varied diurnally throughout the episode; the mean over the whole period was 0.81, an improvement over the 0.7 5 figure obtained with the August and June episodes.

Agreement between the episode mean daily maximum observed (13.1 g/Kg) and modeled (13.5 g/Kg) mixing ratios was quite good. RAMS slightly underestimated the maximum ratio on most days. The mean

bias a nd error in mixing ratios were also quite good, with 10

- day mean values of

- 0.1 g/Kg and 0.7 g/Kg respectively. As with the August 1993 and June 1992 episodes, these results indicated that RAMS did a fairly good job of reproducing the daily maximum and ho urly specific humidity across the 12 km domain.

From the aloft wind comparisons for the April

- May 1995 episode there was reasonably good agreement between the predicted and observed vertically averaged temperatures for each simulation day. The vertically

- averaged predicted and observed temperatures on the 12 km domain were 6.6 ° 

### C and 6.0 ° 

### C, respectively. The mean predicted and observed wind aloft wind speeds were 10.0 m s - 1 

### and 9.1 m s - 1



### , producing a 10% discrepancy.

9.2 Scientific Evaluation Summary

The precipitation evaluation of the six UAH episodes will be done for the episodes as a whole using the results shown in Fig

ure 9

- 1. Figure 9

- 1 is a plot of the equitable threat scores taken from the rainfall statistics tables from each episode. As expected the best performance was for the February 1994 and March 1993 episodes with maximum scores of 0.20

- 0.30 where the synoptic situation was strongly baroclinic and convective precipitation was minimized across the 12

- km domain. All the other episodes

which were in the warm season had maximum scores of 0.15 or less. Regardless of the episode very little skill was observed as the threshold approached 25 mm. While these are admittedly poor scores they are

consistent with the current state of mesoscale numeric al weather prediction. This is discussed further in
section 9.3.

### 340 

# 0 0.1 0.2 0.3 0.4

0 5 10 15 20 25

Thresholds in mmEquitable ThreatScore 

# Feb-94

Jul-95
May-95
May-93

Mar-93

Jul-91

9.2.1 3

- 12 August 1993

Rain occurred on 8 of the 10 days of the August 1993 episode. The maximum observed total daily precipitation at any rain gauge var ied between 9 mm and 119 mm with an episode mean of 50 mm. RAMS predicted a range of rainfall of 11.5 mm to 102.7 mm with an episode mean of 40.3 mm. The mean observed and predicted precipitation across all rain gauges was 5.3 mm and 6.7 mm, yielding an o verall discrepancy of 26% which represents excellent agreement for a mesoscale prognostic model. The bias in daily rainfall predictions ranges from

– 0.30 mm to 10.38 mm with an episode mean of 1.9 mm. Only on 5 August did the model experience significant difficulty reproducing the daily rainfall totals. The daily gross errors in rainfall ranged between 0.41 mm and 13.91 mm with an episode mean of 5.8 mm. Overall, the model did an excellent job of simulating the daily and episode average rainfall totals. With the exception of

the modest over

- prediction on 5 August, the day

- to

- day rainfall predictions match the observations very closely in this comparison.

Scatter plots of predicted and observed daily total precipitation underscored the need for multip le statistical and graphical tools when evaluating the performance of complex atmospheric models. While the spatial time series plots suggested very good model performance based on averages across all monitoring stations, the scatter plots revealed that t his good agreement came as the result of cancellation of model over

- and

under

- predictions at the numerous individual monitors. Visual inspection of the scatter plots revealed little correlation between the various reporting sites. While RAMS did a credi ble job of estimating the total precipitation across the 12 km domain on each day during the August 1993 episode, the spatial distribution

of rainfall events exhibited much less skill.

Figure 9

- 1

. Equitable threat scores for all UAH episodes based on 6

- h precipitation amounts from the

12

- km grid interpolated to all NWS sites within the 12

- km grid.

### 341

9.2. 2 22

- 29 June 1992

Rain occurred on 5 of the 8 days during th e June 1992 episode. The maximum daily precipitation at any rain gauge varied between 4 mm and 122 mm with an episode mean of 53.8 mm. RAMS predicted a range of daily maximum rainfall totals from 9 mm to 110.5 mm with an episode mean of 41.7 mm. The mea n observed and predicted precipitation across all rain gauges was 2.9 mm and 3.9 mm, yielding an overall discrepancy of 35%. The bias in daily rainfall predictions ranges from

– 0.60 mm to 4.82 mm with an episode mean of 0.9 mm. Only on 27 June did the mo del experience significant difficulty reproducing the daily rainfall totals as evidenced by bias and error scores of 4.82 mm and 11.57 mm, respectively. The daily gross errors in rainfall ranged between 0.23 mm and 11.57 mm with an episode mean of 4 mm. Overall, the model did a good job of simulating the daily and episode average rainfall totals when averaged across all monitoring stations. With the exception of the 50% over

- prediction on 27 June, the day

- to

- day rainfall predictions matched the observati ons very closely in this comparison. While the spatial time series suggested good model performance (based on averages across all monitoring stations), the scatter plots of daily precipitation at specific monitoring stations again revealed poor correlation between prediction and observation at the individual reporting sites. Thus, as with the August 1993 episode, RAMS did a credible job of estimating the total precipitation when averaged across the monitoring network on each episode day

but the model’s abi

lity to predict the exact rainfall amounts paired in time and space with specific monitors was generally poor.

9.2. 3 24 April

–3 May 1995

Rain occurred on 8 of the 10 days during the 24 April

–3 May 1995 episode. The maximum daily precipitation at an

y rain gauge varied between 3 mm and 48 mm with an episode mean of 21 mm. RAMS predicted a range of daily maximum rainfall totals from 2 mm to 38.6 mm with an episode mean of 20.1 mm. The mean observed and predicted precipitation across all rain gauges wa s 2.5 mm and 4.1 mm, yielding an overall discrepancy of 64% which constitutes fair agreement. RAMS’ performance in predicting rainfall for this episode is poorer than for the August 1993 and July 1992 episodes. The bias in daily rainfall prediction

range s from

– 5.95 mm to 10.23 mm with an episode mean of 1.5 mm. On May 1 st 

### and 2 nd 

### , RAMS over

predicted the mean daily rainfall amounts by 10.5 mm and 5.9 mm, respectively. The daily gross errors in rainfall ranged between 0.02 mm and 10.63 mm with an episod e mean of 3.5 mm. Overall, the model did a fair job of simulating the daily and episode average rainfall totals when averaged across all monitoring stations. With the exception of the significant over

- prediction on 1 May and 2 May the day

- to

- day rainfall predictions matched the observations closely in this comparison even though the scatter plots suggested generally poor correlation between prediction and observation at the individual reporting sites.

9.3 Conclusions

Appendix 3 compares some of the SAMI episodes with previous MM5 and RAMS simulations they have performed. It is also instructive to compare the SAMI simulations with some statistics for current

### 342

operational numerical weather prediction models used by the National Centers for Environmental Pr ediction (NCEP). Figure 9

- 2 shows equitable threat and bias scores for precipitation for May 2000 for various

models for the entire United States. The statistics are based on 48

- h forecasts verifying at 0000 UTC of 3

- h precipitation amounts. Maximum equ itable threat scores are on the order of 0.15. Very little skill is observed as the 1 inch threshold is approached. Bias scores show an over

- prediction of 0.10 inch amounts and less and then a general under

- prediction of precipitation for larger values. While operational models do not have the luxury of nudging towards observations, these statistics still have relevance for evaluating the

SAMI simulations. Over the 12

- km domain practically no nudging was performed above the surface and

even the surface n

udging scheme is not able to correct for issues such as cloud location and depth. So for

weak

- flow conditions with convection being the dominant precipitation mode the SAMI simulations face very similar difficulties as the operational models. If one were to investigate the performance of NCEP models for other months ( http://sgi62.wwb.noaa.gov:8080/scores ) one would observe maximum equitable threat scores during the cool season on the order of 0.40 and e ven smaller values than the May plot in Figure 9

- 2 for the summer months. So, in conclusion, the equitable threat precipitation scores in Figure 9

- 1 for the UAH SAMI episodes are very similar to current NCEP model performance. It is likely that a similar analysis of the precipitation results for the three AG episodes would have similar conclusions.

An example of near

- surface temperature performance by operational models is given by Fig ure 9

- 3 for the approximate period of 21 May to 4 June 2001. The stati stics are based on 48

- h forecasts verifying at 0000 UTC for the southern Midwest which covers the following region: all of Arkansas, Alabama, Mississippi, and Louisiana; most of Tennessee; and the extreme eastern portions of Oklahoma and Texas. As an

exa mple the Eta model typically had daily bias errors on the order of ± 3 o 

### C and RMSE values of 2 to 6 o 

### C for this period. The average bias and RMSE for this period and area were near 1 and 3 o 

### C, respectively. Figure 9

- 4 is a similar plot for the same time peri od but for near

- surface wind speed performance. Again as an example the Eta model had daily bias errors on the order of ± 3 m s

- 1 

### and RMSE values of 3 to 8 m s

- 1



### . The average bias and RMSE for this period and area were near 1 and 5 m s

- 1 

### , respectively. T he near

surface bias and RMSE values for the same variables for all the SAMI episodes were near or below these values. The operational models face similar difficulties as research models applications (SAMI, for example) including areas such as correct pre diction of precipitation, soil moisture issues, and cloud location and depth.

Finally, the issue of good model performance in weak surface wind regimes has been mentioned several times in this report. Figure 9

- 5 shows the grand mean observed wind speed at 10 m for all of the UAH episodes. If the AG episodes had been included it is likely that the average over the southeastern United States would be even less than shown since the AG periods were warm season events. Figure 9

- 5 shows that a large portion

of the 12

- km grid had observed mean wind speeds of 2.5 ms

- 1 

### or less. If one considers only the surface geostrophic constraints with respect to temperature, a wind speed of 2.5 m s

- 1 

### corresponds to a temperature change across 12 km on the order of only 0. 0 1 o 

### C. This is one reason why model performance is degraded during weak wind conditions.

### 343

Figure 9

- 2. Equitable threat and bias scores for various NCEP models for May 2000. The statistics are
based on 48

- h forecasts verifying at 0000 UTC of 3

- h precipitation amounts for the entire United States for thresholds up to 1 inch. Curves and their respective meanings and colors are: 1) the Aviation model (AVN), red; 2) the Eta model (ETA), green; and 3) the Nested grid Model (NGM), blue. This graphic was acquired at the following web site: http://sgi62.wwb.noaa.gov:8080/scores.

### 344 Figure 9

- 3 Bias and RMSE values ( o C) for various NCEP models for the approximate period of 21 May to 4
June 2001 for near

- surface temperatures. The statistics are based on 48

- h forecasts verifying at 0000 UTC

for the southern Midwest which covers the following region: all of Arkansas, Alabama, Mississippi, and Louisiana; most of Tennessee; and the extreme eastern portions of Oklahoma and Texas. Curves and their respective meanings and colors are: 1) the Aviation mod el (AVN), purple; 2) the Eta model (ETA), blue; and 3) the Nested grid Model (NGM), green. This graphic was acquired at the following web site: http://sgi62.wwb.noaa.gov:8080/VSDB.

### 345

Figure 9

- 4. Bias and RMSE values (m s
- 1 ) for various NCEP models for the approximate period of 21 May to 4

Jun e 2001 for near

- surface wind speeds. The statistics are based on 48

- h forecasts verifying at 0000 UTC for the southern Midwest which covers the following region: all of Arkansas, Alabama, Mississippi, and Louisiana; most of Tennessee; and the extreme eas tern portions of Oklahoma and Texas. Curves and their respective meanings and colors are: 1) the Aviation model (AVN), purple; 2) the Eta model (ETA), blue; and 3) the Nested grid Model (NGM), green. This graphic was acquired at the following web site:

h ttp://sgi62.wwb.noaa.gov:8080/VSDB.

### 346

9.3.1 Aloft Winds and Temperatures

The predicted and observed vertically averaged temperatures, wind speeds and wind directions for eac h SAMI episode were examined and the agreement between these aloft fields was generally quite good. While part of this good agreement is simply due to the fact that aloft temperature and wind observations from the NWS radiosondes were employed in the RAMS FDDA nudging schemes, the actual weighting coefficients used in the nudging were small so that the RAMS fields were not under a heavy constraint to match the observations locally. The good agreement in aloft performance is quite consistent with several o f the other MM5 and RAMS evaluation studies reported in the recent literature and gives some confidence that the modeled wind patterns are a reasonable.

Lack of high

- resolution special meteorological measurements during the SAMI episodes precluded a

detai led scientific evaluation of the RAMS model performance. Examination of the model’s ability to simulate liquid precipitation on a daily and a six

- hourly basis indicated that for virtually all of the episodes studied the model did a good to excellent job o f simulating the daily and episode average rainfall totals

across the 12

- km domain. While the spatial time series plots suggested very good model performance based on averages across all monitoring stations, the performance of the model at individual moni toring stations and on specific days had much greater uncertainty. In most cases, scatter plots of daily averaged Figure 9

- 5. Grand average o bserved mean wind speed at 10 m in m s
- 1 for all UAH episodes for the 96

- km grid. Hourly observations for all days and all episodes were used.

### 347

precipitation at the individual monitoring stations revealed little correlation between prediction and observation. Nevertheless, the model’ s predictions for precipitation on daily time scales and regional spatial scales was encouraging. There did not appear to be any substantially incorrect features of the precipitation results that would suggest the presence of a seriously flawed prognostic model simulation for any of the SAMI episodes.

Based the operational and scientific evaluation results presented in this report and the findings of our review of other contemporary RAMS and MM5 evaluation studies reported in the recent literature, we fin d that the nine (9) SAMI RAMS simulations are easily representative of the level of performance in wind, temperature, mixing ratio and precipitation exhibited by contemporary state

- of

- science prognostic models. We find no reason to disqualify any of the n ine meteorological episodes at this time for use in supporting regional photochemical and/or aerosol calculations with the SAMI URM

- 1ATM modeling system.

9.4 Adequacy of the SAMI Meteorological Modeling for Air Quality Modeling

One of the most important questions addressed in this report concerns whether the RAMS meteorological fields are adequate for their intended use in supporting the acid deposition modeling in SAMI. For the reasons discussed below, we are not able to answer this question definitive ly, yet a significant amount of information has been develop that, we believe, should be of use to SAMI decision

- makers in their efforts to assess the overall reliability and usefulness of the RAMS meteorological modeling results for public policy making. As demonstrated below, we believe the RAMS modeling results are suitable for use in the UAM

AERO acid deposition modeling although a number of important questions remain to be answered fully.

There is no simply way to answer the question of whether the R AMS fields are adequate as input to the SAMI acid deposition model. First, there are no commonly accepted performance benchmarks for prognostic meteorological models that, if passed, would allow one to declare the RAMS fields appropriate

for use. For com

plex atmospheric modeling problems like the ones being addressed by SAMI, it is quite doubtful that a set of quantitative performance criteria will ever be completely sufficient. The question of meteorological data set adequacy depends, at a minimum, upon the specific host emissions and air quality models (EMS

- 95, URM

- AERO in this instance) and the nature of the modeling episodes being used. Meteorological fields that might be adequate for use in the UAM

- V model for an OTAG episode, for example, may be qu

ite deficient in an episode for SAMI since the specific needs of the air quality model and the particular chemical and physical processes that must be simulated are different. Thus, quantitative statistical and graphical performance criteria, though helpf ul, are inherently insufficient in aiding modelers and

decision

- makers in deciding whether meteorological fields are adequate for air quality modeling. Other considerations must be brought to bear. Below, we present and then work through a process whereby the adequacy of the RAMS fields for use in the SAMI acid deposition modeling can be evaluated. This process builds upon the more general evaluation process outlined by Roth, Tesche and Reynolds (1998) and recent suggestions by Tesche et al., (2001) and E mery et al., (2001) on potentially useful model performance benchmarks.

9.4.1 Framing the Questions to Be Addressed

### 348

Usually air quality simulations are quite sensitive to meteorological fields. Where this sensitivity is anticipated, it is important to make an effort to develop as accurate a representation of meteorological variables as possible. Special features of the flow fields, such as eddies, nocturnal jets, drainage flows, land

sea or land

- bay breezes, and vertical circulations should be adequate ly characterized through the meteorological modeling. In circumstances where there are significant transitions in the meteorological variables over short distances, such as along shorelines or in areas of hilly terrain, the need for finer spatial

resoluti on that is typically specified must be considered. If inadequate attention and care are accorded meteorological modeling, there is a significant risk of developing an inaccurate representation that will be propagated into the emissions and air quality mode ls.

Several questions should be addressed for the specific application. Examples of these questions are as

follows:

Appropriateness of Model Selection:

> Modeling Requirements : Was a carefully written characterization made of the most important physic al and chemical processes relevant to successful air quality modeling of each episode (e.g., a “conceptual model” of each simulation period)?

> Model Selection: Did the model selection process ensure that a suitable modeling system was chosen, properly weighing the need for mature, well

- tested, publicly

- available model(s) against the constraints of the specific modeling problem, characteristics of the episodes to be simulated, and the

limitations of schedule and resources?

> Model Formulation Review: Was a rigorous evaluation and inter

- comparison made between the scientific formulation of the proposed meteorological modeling system (source codes plus pre

- and
post

- processors) versus alternative contemporary prognostic models via an open, thorough

scien tific review process?

> Code Verification : Was the fidelity of the computer coding of the proposed model confirmed with respect to its scientific formulation, governing equations, and numerical solution procedures?

Identification of Air Quality Model Ne eds:

> Air Quality Input Needs: Were the meteorological input needs of the host air quality model and supporting emissions models (e.g., biogenic, motor vehicle, area source processors) clearly identified including specification of the requisite averagi ng times and nested grid scales for the specific modeling episodes?

> Air Quality Model Sensitivities: Was the air quality model’s sensitivity to key meteorological inputs established through careful consideration (including air quality model sensitivi ty/uncertainty

### 349

simulations) of the relevant modeling episodes over the specific domain of interest? Was the effect of uncertainty in those meteorological inputs to which the air quality model is demonstrated to be most sensitive adequately defined through appropriate numerical experiments or from previous

relevant studies?

Note : Identification of air quality model needs is a crucial step in the meteorological model evaluation process, yet it is most often performed superficially i f at all. Pragmatic co nstraints of time and resources necessitate that efforts be directed at achieving the best possible meteorological performance for those variables that matter most to the overall accuracy and reliability of the air quality model. There is little practical benefit to be gained in devoting considerable time to improving the accuracy of a particular meteorological variable if the air quality model

–in the specific application at hand

-- is insensitive to that variable. Particular attention should be given t o those meteorological variables that have the largest uncertainty and to which the air quality model is

most sensitive. This challenge can be particularly formidable when dealing with photochemical/aerosol models whose concentration and/or deposition est imates depend on several meteorological variables (mixing, transport, thermodynamic properties, precipitation) simultaneously.

Availability of Supporting Data Bases:

> Adequate Data Available: Were sufficient data available to test, at the ground and aloft and over all nested grid scales of importance, the model’s dynamic, thermodynamic, and precipitation

- related

fields?

> All Data Used

: Was the full richness of the available data base actually utilized in the input data file development, in FDDA, a

nd in the evaluation of model performance?

Note : One of the main considerations underlying selection of modeling episodes for regulatory

decision

- making is the availability of special data collection programs to supplement the surface and

aloft data rout inely available from state and federal agencies. While attempts are made to select modeling episodes that coincide with intensive field measurement programs, in these situations it is common that the full set of supplemental measurements are not used thor oughly in the model input development and performance testing phases. At times, the availability of ‘high

- resolution’ databases is touted in support of a particular episode selection choice yet when the modeling is actually performed and evaluated, only a fraction of the special studies data are actually used. This is most notably the case with air quality and meteorological data collected by aloft sampling platforms. Unless the high

- resolution data are actually used to enhance the modeling and performan ce testing, their value is severely limited. Equally troublesome, selection of other candidate modeling days (supported by only routine information) may be overlooked which might otherwise be preferable modeling periods if a concerted effort to utilize sp ecial studies data is not made. Finally, as desirable as having supplemental meteorological measurements might be, unless the sampling was performed in the correct regions and includes the variables of primary importance to the air quality model, their po tential to add meaningfully to the rigor of the modeling exercise will

### 350

be limited. Thus, when judging the value of supplemental measurement programs, it is necessary to look beyond just their mere existence (relative to non

- intensively monitored days); on e must establish that these intensive data set indeed contribute to improved model performance and increased reliability. This necessitates a feedback loop to the air quality modeling exercise to ensure that the times, locations, and parameters associated with the supplemental measurements truly add to the overall quality and rigor of the study.

Results of Operational, Diagnostic, and Scientific Evaluations:

> Full Model’s Predictive Performance: Was a full suite of statistical measures, graphical

proce dures, and phenomenological explorations performed with each of the models state variables and diagnosed quantities for each pertinent grid nest to portray model performance against available observations and against model estimates from other relevant pro gnostic simulation exercises?

> Performance of Individual Modules: Was there an adequate evaluation of the predictive performance of individual process modules and preprocessor modules (e.g., advection scheme,

sub

- grid scale processes, closure schemes, planetary boundary layer parameterization, FDDA methodology)?

> Diagnostic Testing: Were sufficient and meaningful diagnostic, sensitivity, and uncertainty analyses performed to assure conformance of the meteorological modeling system with known or

expec ted behavior in the real world?

> Mapping Methods: Were parallel evaluations made of: (a) the output from the prognostic model and (b) the output from the ‘mapping’ routines that interpolate the prognostic model output onto the host air quality model’s grid structure? Were any important differences between the two

reconciled?

> Quality Assurance: Was a credible quality assurance (QA) activity implemented covering both the prognostic modeling activity as well as the mapping programs that generate air quality

- ready meteorological inputs? Was the full set of hourly, three

- dimensional fields examined for reasonableness even though observational data for comparison were lacking or in short supply?

Note: Such an intensive performance evaluation process is rarely, if ever, carried out due to time, resource and data base limitations. Nevertheless, it is useful to identify the ideal evaluation framework so that the

results of the actual evaluation can be judged in the proper perspective. This also allows decision

- makers to establish realistic expectations regarding the level of accuracy and reliability associated with the meteorological and air quality modeling process.

Comparison with Other Relevant Studies:

### 351

> Comparisons with Other Studies: Were the model evaluation results (statistical, graphical, and phenomenological) compared with other similar applications of the same and alternative prognostic models to identify areas of commonality and instances of differences between modeling platforms?

Note: Reflecting limited data sets for performance testing and reliable criteria for judging a model’s performance, meteorological model evaluations in recent years have emphasized comparisons with other RAMS and MM5 simulations over various modeling domains a nd episode types as a means of broadening the scope of the evaluation. While this insight into the model’s performance

–when gauged against other similar applications

–is useful, caution must attend such comparisons which at

times are at best anecdotal. Often the reporting of previous evaluations entails grossly composited performance statistics (episode averages or averages across episodes, for example), data bases and modeling efforts of widely varying and often unreported quality, different mathemati cal definitions of statistical quantities, and so on. Thus, these comparisons with other studies, while occasionally providing useful perspective, are by no means sufficient for declaring a meteorological model’s performance to be reliable and acceptable in a particular application. Moreover, meteorological model evaluation benchmarks developed on the basis of such historical evaluation studies must also be applied thoughtfully with these limitations in mind.

Peer Review of Specific Modeling Exercise(s) :

> Scope of Peer Review : Was an adequate, properly

- funded, independent, in

- depth peer review of

the model set

- up, application, and performance evaluation efforts conducted?

> Findings of Peer Review : Was the effort judged acceptable by the peer

- r eview?

Note: Prognostic modeling requires considerable attention to detail, careful identification of options, and complete involvement in the work. Even with this commitment, critical aspects of a modeling exercise may be treated inadequately or overloo ked, most often as the result of schedule or resource constraints. Consequently, an examination of the meteorological modeling effort conducted at arm’s length by individuals with appropriate expertise and who have no personal

involvement in the work can be essential to avoiding inadvertent oversights and problems. Such a peer review of the effort provides another check on the work as a whole. If concerns are raised about the reliability of the modeling, yet meteorological modeling results are to be used in applying air quality models despite these concerns (e.g., due to project schedule demands), the peer review can assist in suggesting to decision

- makers the weight to be given the overall air quality results the planning and management context.

Often, when a professional paper is written describing the modeling study, it undergoes “peer review” by the journal. Such efforts do not constitute the review suggested here. Journal peer review usually entails a reading of the paper, thoughtful reflection, a nd written commentary, perhaps a 4

- to 12

- hour effort. Moreover, reporting in the professional literature is necessarily condensed, and much of the detail that should be scrutinized is omitted. This is especially true for complex atmospheric modeling pro jects.

### 352

Peer review for pre

- print volumes (e.g., American Meteorological Society or Air and Waste Management Association conferences) is even less rigorous, often consisting of a cursory reading of the paper by the Session Chairperson. Peer review, as use d here, refers to detailed examination and evaluation of the work conducted by experts in the field. Such experts are generally, but not limited to, those with considerable direct experience in the development, evaluation, and application of the same or v ery similar meteorological models. This in depth review entails the independent scientists (a) thoroughly examining the conceptual model(s) and modeling protocols prepared for the study, (b) obtaining and examining the details of the model input and outpu t files, and (c) in many cases even running the pre

- and post

- processor codes and the main simulation programs to corroborate reproducibility of results and to explore inevitable technical issues that arise in such comprehensive reviews. In essence, peer review refers to immersing oneself in the materials provided. Such an effort can take several weeks to carry out properly.

Overall Assessment:

> Overall Reasonableness : Has an adequate effort been made to evaluate the quality of representation of met

eorological fields generated using the meteorological model, as revealed by the full suite of statistical, graphical, phenomenological, diagnostic, sensitivity, and uncertainty investigations? What were the strengths and limitations of the actual model pe rformance evaluation?

> Fulfillment of Air Quality Model Needs : How well are the fields represented, particularly in areas and under conditions for which the air quality model is likely to be sensitive?

> Appropriate Model : Was a sound and suitable m eteorological modeling system adopted?

> Adequate Data Base : Was the supporting database adequate to meet input and evaluation

needs?

> Adequate Application Procedures: Was Four Dimensional Data Assimilation (FDDA) a part of the overall modeling ap

proach and were sufficien

t data available to support the activity adequately?

> Quality Assurance : Were error

- checking procedures instituted, followed, and the results reported?

> Performance Evaluation: Were suitable procedures specified and adopte d for evaluating the quality (e.g., accuracy, precision, and uncertainty) of model estimates?

> Judging the Overall Process : Were the criteria (i.e., benchmarks) used to judge performance appropriate for the specific air quality model application, rig orously applied, and properly

communicated?

### 353

9.4.2 Comparison of SAMI RAMS Performance Against Newly Proposed Meteorological
Model Performance Benchmarks

As discussed previously, there are no currently accepted performance criteria for prognostic

meteo rological models. In addition, there is valid concern that establishment of such criteria, unless accompanied with a careful evaluation process such as the one outline in this section might lead to the misuse of such goals as is occasionally the case with the accuracy, bias, and error statistics recommended by EPA for judging photochemical dispersion models. In spite of this concern, there remains nonetheless the need for some benchmarks against which to compare new prognostic model simulations.

In Table 8

- 4 we previously presented the RAMS episode

- average temperature, wind, and mixing ratio statistics on the 12 km grid for 7 SAMI episodes and c ompared them with corresponding results from
twenty

- one (21) other RAMS and MM5 model evaluation studies we have conducted since 1995. Since the time the draft report was prepared, the MM5/RAMS model evaluation data base has been extended to forty

- one (41) studies (Tesche et al., 2002). The basic conclusions drawn in Chapter 8 based on the comparisons in Table 8

- 4 remain valid with this larger data set.

In two recent studies (Tesche et al., 2001b; Emery et al., 2001), an attempt has been made to formulate a set meteorological model evaluation benc hmarks based on the most recent model evaluation literature. The

pu rpose of these benchmarks is not to assign a passing or failing grade to a particular meteorological model application, but rather to put its results into a useful context. These benchmarks may be helpful to decision

makers in understanding how poor or go od their results are relative to the range of other model applications in other areas of the U.S. Certainly an important criticism of the EPA guidance statistics for acceptable photochemical performance is that they are relied upon much too heavily to est ablish an acceptable (to the EPA) model simulation of a given area and episode. Often lost in routine statistical ozone model evaluations is the need to critically evaluate all aspects of the model via the diagnostic and process

- oriented approaches.

The same must stressed for the meteorological performance evaluation. Thus, the appropriateness and adequacy of the following benchmarks should be carefully considered based upon the results of the specific meteorological model application being examined. Ba sed upon these considerations, the benchmarks suggested from the studies of Emery et al, (2001) and Tesche et al., (2001) are as follows:

Parameter Measure Benchmark

Wind Speed RMSE: < 2 m/s

Bias: < ± 0.5 m/s
IOA: 3 0.6

Wind Direction Gross Error: < 30 deg

Bias: < ± 10 deg

Temperature Gross Error: < 2 K

### 354 Bias: < ± 0.5 K
IOA 3 0.8

Humidity Gross Error: < 2 g/kg

Bias: < ± 1 g/kg
IOA: 3 0.6

Table 9

- 4 presents the results of comparing the seven S AMI RAMS episode average statistical results (for those statistics that were produced in this study) with the proposed meteorological modeling benchmarks. Cells in the table colored salmon correspond to those episodes and meteorological variables that fal l outside of the benchmark ranges. From the table, several of the surface temperature bias and error value lie outside the benchmark ranges as do the RMSE errors for surface winds. Compared with the benchmarks, the SAMI temperature simulations exhibit a larger under

- prediction and somewhat larger errors. Also, for about half the episode, the RMSE winds are above the benchmark ranges. In only two instances (the 23

- 31 March ’93 and 8

- 13 Feb ’94 episodes) are the wind direction statistics noticeably poorer than the benchmarks. The mixing ratio (specific humidity) results are very good and well within the benchmark ranges. The observed temperature under

- prediction bias and the over

- prediction bias (discussed in Chapter 8) are perhaps the most noteworthy ar eas in which potential impacts on the air quality model

calculat ions might be expected.

While one could speculate as to whether theses biases and errors might have an adverse impact on the air quality model, this would actually have to be determined thro ugh air quality model sensitivity/uncertainty analyses. We are unaware of the extent to which formal exercises of this nature were conducted in the SAMI air quality modeling program. Absent this information, judgments about the impacts, if any, on the

re liability of the air quality model estimates would be speculative. Ideally, these temperature and wind speed biases should be considered by the air quality modelers in their model sensitivity and uncertainty analyses.

9.4.3 Concluding Assessment of the SAMI RAMS Application

Table 9

- 5 presents the results of our effort to judge the adequacy of the RAMS meteorological modeling process for SAMI and the specific results for the nine (9) episodes against the set of two

- dozen questions raised in the precedi

ng section. Our overall conclusions about the adequacy of the RAMS modeling and the reliability of the meteorological fields supplied to the URM

- AERO model are as follows:

> The SAMI meteorological modeling activity clearly selected an appropriate regio nal prognostic model for use in the assessment;

> The RAMS modeling was carried out in a logical, sound, well

- documented manner that was consistent with good scientific principles and the procedures commonly used in the application of this sophisticated model;

> The suite of evaluation procedures employed to test the RAMS model were comprehensive and

### 355

reflected several different model testing perspectives;

> The data base available to test the RAMS model was extremely limited, precluding a number of

me aningful, stressful tests of the model to ascertain whether it suffers from internal, compensating errors; as the result, model testing was confined principally to an operational evaluation;

> Generally, the RAMS performance for surface and aloft winds, temperatures, mixing ratios, and precipitation are consistent with contemporary modeling experience and with new proposed evaluation benchmarks;

> In some cases, notably the under

- prediction bias for surface temperature and over

- prediction bias

for surfa ce wind speed, the RAMS model exhibits (for some episodes) features that could have an effect on the air quality model estimates; however, this has not been verified through sensitivity experiments with the URM

- AERO modeling system to demonstrate that thes e biases are indeed important. In other cases, notably mixing ratio, the RAMS performance was much better than is typically encountered with modeling of this complexity; and

> None of the performance testing results conducted have revealed flaws in RAMS performance of such a magnitude as to clearly indicate the presence of errors that would render the model inappropriate for use as input to regional air quality models.

We conclude that the RAMS meteorological fields may be used, with appropriate caution s, to drive the regional emissions and photochemical/aerosol models for each of the episodes selected for the SAMI

assessment.

### 356

Table 9

- 4. SAMI Modeling Results Compared with the Proposed Benchmarks.

Episode Temperature, (deg C) Mix Ratio, (gm/Kg) Sur face Winds (m/s)

Bias Error Bias Error RMSE Indx A WDir Error

1 24

- 29 May '95

- 1.0 1.9 0.0 0.8 1.90 0.76 13

2 11

- 17 May '93

- 1.5 2.1 0.0 0.8 1.90 0.76 6

3 23

- 31 Mar '93

- 1.3 2.2 0.0 0.6 2.27 0.74 100

4 8

- 13 Feb '94 0.5 2.1 0.0 0.4 2.76 0.72 103

5 3

- 12 Aug '93

- 0.4 1.6

- 0.6 1.1 2.18 0.75 25

6 22

- 29 Jun '92

- 1.1 1.8 0.0 1.0 1.89 0.75 20

7 24Ap

- 3My '91

- 0.8 1.8

- 0.1 0.7 2.35 0.81 4

Benchmark < + 0.5 < 2.0 < + 1.0 < 2.0 <

2.00 >

0.60 <

30

SAMI Mean

- 0.8 1.9

- 0.1 0.8 2.18 0 .76 39

U. S. Mean

- 0.1 1.9 0.0 1.9 1.95 0.70 25

### 357

Table 9

- 5. Assessment of the RAMS Meteorological Fields As Input to the SAMI URM

- AERO Acid Deposition Model.

No. Question Assessment Appropriateness of Model Selection

1 Was a careful written char acterization made of the most important physical and chemical processes relevant to successful air quality modeling of each episode (e.g., a “conceptual model” of each simulation period)? No. See the SAMI Air Quality Modeling Protocol by Russell et al. (1 998).

2 Did the model selection process ensure that a suitable modeling system was chosen, properly weighing the need for mature, well

- tested, publicly

- available model(s) against the constraints of the specific modeling problem, characteristics of the epi sodes to be simulated, and the limitations of

schedule and resources? Yes. Given the fact that the episodes selected involved periods of light to heavy precipitation, the RAMS model was a sound choice. While the RAMS model was proprietary at the time SAMI began, it was possible for independent groups to obtain rights to use the code at a cost.

3 Was a rigorous evaluation and inter

- comparison made between the scientific formulation of the proposed meteorological modeling system (source codes plus pre

- and post

- processors) versus alternative contemporary prognostic models via an open, thorough scientific review process? No. We are not aware of any detailed comparisons being performed between RAMS and alternative models (e.g., MM5) including their respective pre

- and post

- processor systems. Model selection was based on general attributes of the RAMS model and the extensive experience of the SAMI contractor (UAH) in exercising this model.

4 Was the fidelity of the computer coding of the proposed model to the scientific formulation, governing equations, and numerical solution procedures adequately examined and confirmed? The RAMS modeling system is well established with a rich development and refinement history spanning more than two decades. The model has se en extensive use worldwide by many agencies, consultants, university scientists and research groups. The current version of the model and its predecessor versions have been extensively "peer

- reviewed" and considerable algorithm development and module

test ing has been carried out with all of the important process components. Identification of Air Quality Model Needs

5 Were the meteorological input needs of the host air quality model and supporting emissions models (e.g., biogenic, motor vehicle, area s ource processors) clearly identified including specification of the requisite average times and nested grid scales for the specific modeling episodes? No. See the SAMI Meteorological Modeling Protocol by Norris and Doty (1998).

6 Was the air quality mo del’s sensitivity to key meteorological inputs established through careful consideration (including air quality model sensitivity/uncertainty simulations) of the relevant modeling episodes over the specific domain of interest? Was the effect of uncertaint y in those meteorological inputs to which the air quality model is demonstrated to be most sensitive adequately defined through appropriate numerical experiments or from previous relevant studies? No. The SAMI URM

- AERO model was still undergoing final de velopment at the time the RAMS model applications were initiated. While there was anecdotal information about the URM’s sensitivity to key model inputs, no domain

- or episode

- specific information was developed to help identify the subset of RAMS outputs t hat were
most critical to the UAM

- AERO gas

- phase, secondary aerosol, or acid deposition predictions.

Availability of Supporting Data Bases

7 Were sufficient data available to test, at the ground and aloft and over all nested grid scales of importance, the model’s dynamic, thermodynamic, and No. Data were adequate to set up, operate, and evaluate (operationally) the RAMS
model with standard surface and aloft NWS data sets and other information from

### 358

precipitation

- related fields? established surface repor ting networks (e.g., TVA precip. sites). No supplemental meteorological data sets, particularly for aloft processes, were available.

8 Was the full richness of the available data base actually utilized in the input data file development, in FDDA, and in the evaluation of model performance? No. Some supplemental data sets were available.

Results of Operational, Diagnostic, and Scientific Evaluations

9 Was a full suite of statistical measures, graphical procedures, and phenomenological explorations per formed with each of the model’s state variables and diagnosed quantities for each pertinent grid nest to portray performance against available observations and estimates from other relevant prognostic modeling exercises? Yes, for the most part. An extensi ve set of operational evaluation statistics and graphical displays were produced focusing on point comparisons, residual analyses, and comparisons between spatial fields of measurements and predictions. The operational evaluations were carried out at all RAMS spatial scales but the level of analysis and reporting varied from one grid scale to the next. The 12 km scale received the greatest attention.

10 Was there an adequate evaluation of the predictive performance of individual process modules and prepr ocessor modules (e.g., advection scheme, sub

grid scale processes, closure schemes, planetary boundary layer parameterization, FDDA methodology)? No. Lack of data to perform these experiments and SAMI schedule and resource allocations prevented these expl orations from being carried out.

11 Were sufficient and meaningful diagnostic, sensitivity, and uncertainty analyses performed to assure conformance of the meteorological modeling system with known or expected behavior in the real world? Very limited. D etailed diagnostic sensitivity experiments were performed with a few episodes (especially the first one) but subsequently, little diagnostic or sensitivity experimentation was performed as the result of schedule and budget considerations.

12 Were parallel

evaluations made of: (a) the output from the prognostic model and (b) the output from the ‘mapping’ routines that interpolate the prognostic model output onto the host air quality model’s grid structure?
Were sources of differences between the two reconc iled? No. SAMI schedule and resource constraints precluded an in

- depth comparison of “raw” RAMS output fields vs. the URM

- ready meteorological fields resolved to the air quality model grid mesh.

13 Was a credible quality assurance activity implemented c overing both the prognostic modeling activity as well as the mapping programs that generate air quality

- ready meteorological inputs? Was the full set of hourly, three

dimensional fields examined for reasonableness even though observational data for compar

ison are lacking or in short supply? Partially. Quality assurance activities consisted principally of routine plotting of surface fields, calculation of summary statistics (to reveal outliers or anomalies), and related graphical display methods to provide a cursory check of the model inputs and outputs. However, once the data input preparation procedures were established, the RAMS input files were constructed mostly in a hands

- off manner. Quality assurance activities of the RAMS output fields was perform ed as an integral part of the statistical and graphical performance examinations. Comparison with Other Relevant Studies

14 Were the model evaluation results (statistical, graphical, and phenomenological) compared with other similar applications of the same and alternative prognostic models to identify areas of commonality and instances of differences between modeling platforms? Partially. Episode average statistics over the 12 km grid were compared with thirty four (34) RAMS and MM5 model applications elsewhere in the U.S., primarily involving summertime ozone episodes with typical grid scales in the 4

– 16 km range. (See Table 8

- 4). No detailed comparisons were performed between the SAMI episodes and others reported in the literature to elucidate ar eas of similar performance and areas of disparate performance. Peer Review of Specific Modeling Exercise(s)

15 Was an adequate, properly funded, independent, in

- depth peer review of the No. SAMI schedule and resource constraints precluded a rigorous, independent

### 359

model set

- up, application, and performance evaluation efforts con ducted? peer

- review of the RAMS meteorological modeling. Only a very limited

- scope “peer” review was conducted as the result of Alpine Geophysics modelers engagement in
the SA MI meteorological process to assist in the development of the three final episodes. This allowed an independent group to corroborate the UAH base case and modeling methods. No substantive irregularities in the UAH RAMS modeling were
uncovered in the work carried out by Alpine scientists.

16 Was the effort judged acceptable by the peer

- review? Not applicable since no formal peer review was performed.

Overall Assessment

17 Has an adequate effort been made to evaluate the quality of representation of

me teorological fields generated using the meteorological model, as revealed by the full suite of statistical, graphical, phenomenological, diagnostic, sensitivity, and uncertainty investigations? What were the strengths and limitations of the actual model p erformance evaluation? Generally yes. One consequence of two different groups performing the meteorological modeling was that complimentary perspectives were brought to bear on the performance evaluation portion. This involved: (a) ‘synoptic’ or broad

- sca le perspective and (b) examination of the fidelity of model predictions in the vicinity of surface and aloft measurement sites within the planetary boundary layer. A rich variety of analytical procedures, statistical metrics and graphical tools employed b y the two groups reflecting their particular perspective and experience in prognostic model evaluation, led to complimentary and corroborating examinations of performance and produced insights into the model’s operation that would not be as thoroughly exam

ined via one approach alone. All of the statistical and graphical presentation methods employed were used extensively and effectively in past evaluations reported broadly in the literature.

18 How well are the fields represented, particularly in areas a nd under conditions for which the air quality model is likely to be sensitive? Uncertain. The SAMI schedule and resources precluded detailed exchange between the meteorological and air quality modeling teams. The process was largely serial
with the meteo rology being produced, followed by the air quality modeling. With the exception of the first episode, there was very little opportunity for interaction among the meteorological and air quality modelers to produce a sustained cycle of RAMS diagnosis and pe rformance improvement, followed by an investigation of the URM

AERO model response, producing yet another round of meteorological model diagnosis and performance improvement.

19 Was a sound and suitable meteorological modeling system adopted? Yes. The RA MS model used in the SAMI application is clearly representative of the

state

- of

- the

- science in mesoscale prognostic modeling for air quality applications.

20 Was the supporting database adequate to meet input and evaluation needs? No. While the available data base was sufficient to set up, exercise, and evaluate operationally the model, it was clearly deficient in supporting rigorous testing, aimed at identifying potential sources of internal, compensating errors.

21 Was Four Dimensional Data Assimila tion (FDDA) a part of the overall modeling approach and were sufficient data available to support the activity adequately? Partially. The routinely available data were sufficient to utilize data assimilation in the RAMS simulations. However, lack of h igh

- resolution data (e.g., radar wind profilers) and budget/time constraints precluded the infusion of this information into the routine FDDA methodologies that were ultimately used.

22 Were error

- checking procedures instituted, followed, and the results Partially. The results of quality assurance activities were reported anecdotally during

### 36 0

reported? conference calls and project meetings but no formal chronology was archived of the problems encountered and solutions rendered.

23 Were suitable procedure s specified and adopted for evaluating the quality (e.g., accuracy, precision, and uncertainty) of model estimates? Partially. Very useful statistical measures and graphical procedures were employed to quantify performance for key dynamic and thermodyna mic variables (e.g., bias, gross

errors, root

- mean

- square

- error, Index of Agreement, threat scores). However, little quantitative information was produced relative to model uncertainty. No formal uncertainty analysis was conducted of the RAMS simulations . Hence, the estimates of “uncertainty” in the RAMS outputs are based on the ranges in the various statistics (e.g., the range in the model’s surface temperature bias over 9 episodes) as compared to quantitative estimate of model uncertainty arising due t o: (a) formulation, (b) procedures for developing inputs or processing outputs, and (c) measurement error and spatial representativeness issues.

24 Were the criteria (benchmarks) used to judge performance appropriate for the specific air quality model app lication, rigorously applied, and properly

communicated? Partially. A credible effort was made to identify the key components of the meteorological model evaluation process and to address each one subject to the constraints of project schedule, resource s, and the information available from the meteorological and air quality modeling activities. While the present SAMI RAMS evaluation is arguably more comprehensive and systematic than any other previous prognostic model evaluation study supporting air qua lity applications, many areas of the evaluation were not adequately explored, principally as the result of these
limitations.

### 361

9. 5 In Summary

One of the most important questions addressed in this report concerns whether the RAMS meteorological

fields are adequate for their intended use in supporting the acid deposition modeling in SAMI. For the reasons discussed in Chapter 9, we are not able to answer this question definitively, yet a significant amount of information was developed in this study that sup ports our finding that the RAMS modeling results are

suitable for use in the UAM

- AERO acid deposition modeling although a number of important questions remain to be answered fully.

There is no simply way to answer definitively the question of whether the RAMS fields are adequate as input to the SAMI acid deposition model. There are no universally accepted performance benchmarks that, if passed, would allow one to declare unequivocally that the RAMS fields are appropriate for use. For complex atmospheric modeling problems like the ones being addressed by SAMI, it is quite doubtful that such a set of definitive performance criteria will ever be completely sufficient. The question of meteorological data set adequacy depends, at a minimum, upon the specific host emissions and air quality models and the nature of the modeling episodes being used. Meteorological fields that might be adequate for use in one situation may be quite deficient in another if the particular chemical and physical processes

that must b e simulated are different. Thus, quantitative statistical and graphical performance criteria, though helpful, are inherently insufficient in telling modelers and decision

- makers whether meteorological fields are adequate for air quality modeling. In this study, we developed and then applied a multi

- step evaluation process whereby the adequacy of the RAMS fields for use in the SAMI acid deposition modeling was

evaluated.

In addition to comparing the SAMI results with a large range of previous meteorolog ical model evaluation studies in the U.S., we also compared the RAMS evaluation results with a recently proposed set of meteorological model evaluation bench marks based on the most recent model evaluation literature. While

these benchmarks are not aimed a t assigning a passing or failing grade to a particular meteorological model application, they do help put the results into a useful context for decision

- makers. Based on these and other analyses reported in Chapter 9, o ur overall conclusions regarding the adequacy of the RAMS modeling and the reliability of the meteorological fields supplied to the URM

- AERO model are as follows:

> The SAMI meteorological modeling activity clearly selected an appropriate regional prognostic

model for use in the assessment ;

> The RAMS modeling was carried out in a logical, sound, well

- documented manner that was consistent with good scientific principles and the procedures commonly used in the application of this sophisticated model;

> The suite of evaluation procedures em ployed to test the RAMS model were comprehensive and reflected several different model testing perspectives;

> The data base available to test the RAMS model was extremely limited, precluding a number of meaningful, stressful tests of the model to ascerta in whether it suffers from internal, compensating errors; as the result, model testing was confined principally to an operational evaluation;

> Generally, the RAMS performance for surface and aloft winds, temperatures, mixing ratios, and precipitation are

consistent with contemporary modeling experience and with new proposed evaluation benchmarks;

### 362

> In some cases, notably the under

- prediction bias for surface temperature and over

- prediction bias for surface wind speed, the RAMS model exhibits (for some ep isodes) features that could have an effect on the air quality model estimates; however, this has not been verified through sensitivity experiments with the URM

- AERO modeling system to demonstrate that these biases are indeed important. In other cases, not ably mixing ratio, the RAMS performance was much better than is typically encountered with modeling of this complexity; and

> 

### None of the performance testing results conducted have revealed flaws in RAMS performance of such a magnitude as to clearly indic ate the presence of errors that would render the model inappropriate for use as input to regional air quality models.

We conclude that the RAMS meteorological fields may be used, with appropriate cautions, as input to the regional emissions and photochem ical/aerosol models for

each of the episodes selected for the SAMI

assessment.

### 363

Figure 9

- 6. Vertical Profiles of Wind, Temperature and Mixing Ratio at Huntington, WV at 1900 EST on 25 April 1995.

### 364

9 REFERENCES

Anthes, R. A. and T. T. Warner , 1978. The Development of Mesoscale Models Suitable for Air Pollution and Other Mesometeorological Studies, Monthly Weather Review , vol. 106, pp, 1045

- 1078.

Anthes, R. A., 1977. A Cumulus Parameterization Scheme Utilizing a One

- Dimensional Cloud Model,

M onthly Weather Review, Vol. 105, pp. 270

- 286.

Anthes, R. A., E. Y. Hsie, and Y. H. Kao, 1987. "Description of the Penn State/NCAR Mesoscale Model Version 4 (MM4)", NCAR Technical Note 282, National Center for Atmospheric Research, Boulder, CO.

Arakawa, A

. and W. Schubert, 1974. Interaction of a Cumulus Cloud Ensemble with the Large Scale Environment, Part I. J. Atmos. Sci., Vol. 31, pp. 674

- 701.

ARB, 1992. "Technical Guidance Document: Photochemical Modeling", prepared by the California Air

Resources Boa

rd, Sacramento, CA.

Arunachalam, S., 2001. “A Comparison of EPA’s Models

- 3/CMAQ and the MAQSIP Modeling System for Ozone Modeling in North Carolina.”, 94 th 

### Annual Conference of the Air and Waste Management

Association

, 24

- 28 June 2001, Orlando, FL.

ASTeR , 1991. ARAMS: The Regional Atmospheric Modeling System, Version 2c User’s Guide, ASTeR, Inc., Fort Collins, CO.

Atkinson, R., 2000. “Atmospheric Chemistry of VOCs and NOx”, Atmospheric Environment

, Vol. 34, pp.

2063

- 2100.

Avissar, R. and Y. Mahrer, 1988 . Mapping Frost

- Sensitive Areas with a Three

- Dimensional Local

- Scale Numerical Model. Part I: Physical and Numerical Aspects, Journal of Applied Meteorology, Vol. 27, pp.

400

- 413.

Bankov, E., et al., 1998. “A trajectory

- clustering

- correlation methodology for examining the long

- range transport of air pollutants”, Atmospheric Environment . Vol. 32, pp. 1525

- 1534.

Banta, R. M., et al., 1998. “Daytime buildup and nighttime transport of urban ozone in the boundary layer during a stagnation episode”, Journal of Geophysical Research

, 103: 22519

- 22544.

Barchet, W. R. and R. L. Dennis, 1990. NAPAP Model Evaluation, Volume 1: Protocol, prepared for the U.S. Environmental Protection Agency, prepared by Battelle Pacific Northwest Laboratories, Richland,

WA.

Barnes, S. L., 1973: Mesoscale objective analysis using weighted time

- series observations. NOAA

Tech. Memo. ERL NSSL

- 62, National Severe Storms Laboratory, Norman, OK 73069, 60 pp, [NTIS COM

- 73

- 10781].

### 365

Beljaars, A. C. M., and A. A. Holtslag, 1991: Flux paramet erization over land surfaces for atmospheric models. J. Appl. Meteor. , 30, 327

- 341.

Beljaars, A. C., 1994: The parameterization of surface fluxes in large

- scale models under free

convection. Q. J. R. Meteorol. Soc. , 121, 255

- 270.

Benjamin, S. G. and N . L. Seaman, 1985. "A Simple Scheme for Objective Analyses in Curved Flow",

Mon. Wea. Rev. , Vol. 113, pp. 1184

- 1198.

Berman, S., et al., 1997. “Uncertainties in estimating the mixing depth

- Comparing three mixing

- depth models with profiler measurements”, Atmospheric Environment , (18) 3023

- 3039. Berman, S., et al., 1999. “Spatial and temporal variation in the mixing depth over the northeastern United States during the summer of 1995”, Journal of Applied Meteorology , 38: (12) 1661

- 1673.

Blackadar, A. K., 1979. High Resolution Models of the Planetary Boundary Layer, Advances in Environmental Science and Engineering , Vol. 1, No. 1, J. Pfafflin and E. Ziegler, Eds., Gordon and Breach,

50

- 85.

Blanchard, C. L., 1999. “Ozone Attainment and Control Strategy Eval uation”, prepared for the American Petroleum Institute, prepared by Envair, Albany, CA.

Blanchard, C. L., 2000. “Ozone Process Insights from Field Experiments: Part III: Extent of Reaction and Ozone Formation”, Atmospheric Environment

, Vol. 34, pp. 2035

- 2 044.

Blanchard, C. L., 2001. “Trends in Ambient Concentrations of Ozone and Precursor Species n Eight Geographical Regions”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

Blanchard, C. S., 2001. “Spatia l Mapping of VOC and NOx Limitation of Ozone Formation in Six Areas”,

94 th 

### Annual Conference of the Air and Waste Management Association , 24

- 28 June 2001, Orlando, FL.

Blumenthal, D., et al., 1997. “Assessment of Transport and Mixing and OTAG Model Perfor mance for Northeast U.S. Ozone Episodes: Summary of Results”, prepared for the U.S. EPA, prepared by Sonoma Technology, Inc., Santa Rosa, CA.

Bowne, N. E. and D. L. Shearer, 1991. "Lake Michigan Ozone Study: Summary of Field Measurements", First Draft, pr

epared by ENSR Consulting and Engineering, prepared for the Lake Michigan Air Directors Consortium, Des Plaines, IL.

Bowne, N. E., 1992. "The Lake Michigan Ozone Study: The Data", 85th Annual Meeting of the Air and Waste Management Association , 21

- 26 June , Kansas City, MO.

### 366

Brieman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone, 1984. Classification and Regression Trees Wadsworth, Belmont, California.

Brook, J .R., et al., 1999. “Description and Evaluation of a Model of Deposition Velocities for Rout ine Estimates of Air Pollutant Dry Deposition over North America: Part I: Model Development”, Atmospheric

Environment

, Vol. 33, pp. 5037

- 5051.

Burk, S. D. and W. T. Thompson, 1989. Vertically Nested Regional Numerical Prediction Model with

Second

- order Cl

osure Physics, Mon. Wea. Rev ., 177, pp. 2305

- 2324.

Byun, D. W., 2001. “Regional, Urban, and Neighborhood Scale Air Quality Simulation with Models

- 3 Community Multi

- Scale Air Quality

(CMAQ) Model”, 94 th 

### Annual Conference of the Air and Waste Management Ass ociation, 24

- 28 June 2001, Orlando, FL.

Casey, D. M., R. T. McNider, and A. J. Song, 1995: A quantitative analysis of the predictability of mesoscale convective activity in regions of weak synoptic forcing over the southeastern United States.

Fourteenth Conference on Weather Analysis and Forecasting

, Jan. 15

- 20, 1995, Dallas, Texas, Am.

Meteor. Soc.

Chameides, W. L., et al., 2000. “An Assessment of Tropospheric Ozone: A North American Perspective NARSTO Synthesis Team”, Available at the NARSTO Website. ( Pages 3

- 11 to 3

- 22 are particularly informative).

Chan, D., et al., 1999. “Linking Changes in Ozone to Changes in Emissions and Meteorology”, Proc. Air

Pollution 99 Conf., Palo Alto, CA. Wessex Institute of Technology, pp. 663

- 675.

Changnon, S. A., K. E . Kunkel, and B. C. Reinke, 1996: Impacts and responses to the 1995 heat

wave: A call to action. Bull. Amer. Meteor. Soc. , 77, 1497

- 1506.

Chen, C. and W. R. Cotton, 1983: A one

- dimensional simulation of the stratocumulus

- capped mixed layer. Bound.

- La yer Meteor. , 25, 289

- 321.

Civerolo, K. L., et al., 2000. “The effects of land use in meteorological modeling: implications for assessment of future air quality scenarios”, Atmospheric Environment , 34: (10) 1615

- 1621.

Colby, F. P. Jr., 1984: Convective inhibition as a predictor of convection during AVE

- SESAME II.

Mon. Wea. Rev. , 112, 2239

- 2252.

Cotton, W. R., et al., 1982. The Colorado State University Three

- Dimensional Cloud/Mesoscale Model C 1982, Part II: An Ice Phase Parameterization, J. Rech. Atmo s., Vol. 16, pp. 295

- 320.

Cowling, E. B. and C. Furiness (eds), 2000. “The state of the Southern Oxidants Study (SOS): Policy7 Relevant Findings in Ozone and PM2.5 Research 1994

- 2000”, SOS Office, Raleigh, NC. [http://www2.ncsu/CIL/southern_oxidants/] (P ages 68

- 78 are particularly informative.)

### 367

Cox, R., et al., 1998. Mesoscale Model Intercomparison, Bulletin of the American Meteorological Society

, Vol. 79, No. 2., pp. 265

- 283.

Daum, P. L., et al., 2000. “Analysis of O3 formation during a stagnation epis ode in central Tennessee in summer 1995”, Journal of Geophysical Research

, 105

- 9107

- 9119.

Davis, W. T., 2001. “VMT Growth Rates in the U.S. and Their Effects on NOx and VOC Emissions.”,

94 th 

### Annual Conference of the Air and Waste Management Association , 2 4

- 28 June 2001, Orlando, FL.

Dennis, R. L., et al., 1990. "Evaluation of Regional Acid Deposition Models", State

- of

- Science/Technology Report No. 5, National Acid Precipi tation Assessment Program, Washington, D.C.

Deuel, H. P. and S. G. Douglas, 1998. " Episode Selection for the Integrated Analysis of Ozone, Visibility, and Acid Deposition for the Southern Appalachian Mountains." Systems Applications International, Inc., San Rafael, California (SYSAPP

- 98/07r1).

Dickinson, R. E., et al., 1986. Biosphere

- Atmosphere Transfer Scheme for the NCAR Community Climate Model, Tech. Report NCAR/TN

- 275+STR, National Center for Atmospheric Research, Boulder, CO.

Dodge, M. C., 2000. “Chemical Oxidant Mechanisms for Air Quality Modeling: A Review”, Atmospheric

Environ ment , Vol. 34, pp. 2103

- 2131.

Douglas, S. G., et al., 1996. “Investigation of the Effects of Horizontal Grid Resolution on UAM

- V Simulation Results for Three Urban Areas”, prepared for Southern Company Services and Cinergy Corporation, prepared by Systems Applications, Int., San Rafael, CA.

Dudhia, J., 1989. "Numerical Study of Convection Observed During the Winter Monsoon Experiment Using a Mesoscale Two

- Dimensional Model", J. Atmos. Sci. , Vol. 46. pp. 3077

- 3107.

Dudhia, J., 1993. "Non

- hydrostatic Versi

on of the Penn State/NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front", Mon. Wea. Rev. , Vol. 121. pp. 1493

- 1513.

Earth Tech, 1997. “Assessment of UAM

- V Model Performance in the Northeast Region for OTAG Episodes” , prepared for a consortium of Utilities in the Northeast, prepared by Earth Tech, Inc., Concord,

MA.

Edgerton, E., 1996. “Comparison of Modeled Versus Observed Isoprene Concentrations at Rural and Suburban Sites Across the Eastern U.S.”, Atmospheric Rese arch and Analysis, Inc., Durham, N.C.

Ek, M. and L. Mahrt, 1991: A formulation for boundary layer cloud cover. Ann. Geophys. , 9, 716

- 724.

### 368

Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeling and Performance

Evaluation for Two Texa s Ozone Episodes”, prepared for the Texas Natural Resources Conservation Commission, prepared by ENVIRON International Corp, Novato, CA.

Emigh, R. A., et al., 1997. “Comparison of CEM

- Enhanced Emissions with OTAG Base1c Emissions and the Impact on Ozone C oncentrations”, Final Report prepared for the Electric Power Research Institute, prepared by Alpine Geophysics, LLC, Boulder, CO.

ENSR, 1993. "Model Code Verification of Air Quality and Meteorological Simulation Models for the Lake Michigan Ozone Study", prepared for the Lake Michigan Air Directors Consortium, prepared by ENSR Consulting and Engineering, Hartford, CN.

EPA, 1991. "Guidance for Regulatory Application of the Urban Airshed Model (UAM), "Office of Air Quality Planning and Standards, U.S. Envir onmental Protection Agency, Research Triangle Park, N.C.

EPA, 1997. “Calculation of Budget Components: Technical Support Document”, prepared by the Office of Air and Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC.

EPA, 1997. Byun, D. W., 2001. “Regional, Urban, and Neighborhood Scale Air Quality Simulation with

Models

- 3 Community Multi

- Scale Air Quality (CMAQ) Model”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

Eskridge, R

. E., et al., 1997. “Separating different scales of motion in time series of meteorological variables”, Bulletin of the American Meteorological Society

, 78: (7) 1473

- 1483.

Fast, J. D., 1995. Mesoscale Modeling and Four

- Dimensional Data Assimilation in Ar eas of Highly Complex Terrain, J of Appl. Meteorology , Vol. 34, pp. 2762

- 2782.

Flatau, P. J., R. L. Walko, and W. R. Cotton, 1992: Polynomial fits to saturation vapor pressure. J. Appl. Meteor. , 31, 1507

- 1513.

Flaum, J. B., et al., 1996. “Moderating the influence of meteorological conditions on ambient ozone concentrations”, Journal of the Air and Waste Management Association

. 46: (1) 35

- 46.

Gayno, G. A., 1994. Development of a Higher

- Order, Fog

- Producing Boundary Layer Model Suitable For

Use in Numeric al Weather Prediction, M. S. Thesis, The Pennsylvania State University, 104 pp.

Gayno, G. A., et al., 1994. Forecasting Visibility Using a 1.5

- Order Closure Boundary Layer Scheme in a

12 Km Non

- Hydrostatic Model, 10 th 

### AMS Conference on Numerical Weather Prediction , Portland, OR.

18

- 22 July. American Meteorological Society, Boston, MA.

Gill, D. O., 1992. User’s Guide to the Penn State/NCAR Mesoscale Modeling System, NCAR Tech. Note 381 +IA, National Center for Atmospheric Research, Boulder, CO, 233 pp.

### 369

G illani, N. V. and J. E. Pleim, 1996. “Sub

- Grid

- Scale Features of Anthropogenic Emissions of NOx and VOC in the Context of Regional Eulerian Models”, Atmospheric Environment , Vol. 30. No. 12, pp. 2043

- 2059.

Gillani, N. V., et al., 1998. “Relative Productio n of Ozone and Nitrates in Urban and Rural Power Plant Plumes I. Composite Results Based on Data from 10 Measurement Days ”, Journal of Geophysical Research, 103: 22593

- 22615.

Gillani, N. V., et al., 1998. “Loss Rate of NOy from a Power Plant Plume Based on Aircraft Measurements”, Journal of Geophysical Research , Vol. 103, pp. 22585

- 22592.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994. "Description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN

- 398

- STR, 138 p p.

Grell, G. A., et al., 1991. Semi

- prognostic Tests of Cumulus Parameterization Schemes in the Middle Latitudes, Monthly Weather Review, Vol. 119, pp. 5

- 31.

Guinnup, D. and B. Collom, 1997. “Telling The Ozone Story With Data”, Final Report, Vol. I: Exec utive Summary OTAG Air Quality Analysis Workgroup.

Guinnup, D. and B. Colloum, 1997. “Final Report for the OTAG Air Quality Analysis Workgroup”, Final Report, Vol. II, Summary and Integration of Results.

Hallmark, S. L., 2001. “Estimating Variability Bet ween Passenger Car Vehicle Types and the Implications for Fleet Mix Estimates on Emissions”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

Haney, J. L. and S. G. Douglas, 1996. “Analysis of the Effects o f Grid Resolution on UAM

- V Simulation Results for the Atlanta Nonattainment Area”, prepared for the Southeast Modeling Center, prepared by Systems Applications, Int., San Rafael, CA.

Hanna, S. and M. E. Fernau, 1998. Monte Carlo Estimates of Uncertainties in Predictions by a Photochemical Grid Model (UAM

- IV) Due to Uncertainties in Input Variables, Atmospheric Environment

, Vol. 32, No. 21. pp. 3619

- 3628.

Hanna, S. G., et al., 1996. “Evaluation of Photochemical Grid Models (UAM

- VI, UAM

- V, and the ROM

UAM

- V I Couple) Using Data from the Lake Michigan Ozone Study (LMOS)”, Atmospheric Environment

, Vol. 30, pp. 3265

- 3279.

Hanna, S. R. and J. C. Chang, 1992. “Representativeness of wind measurements on a mesoscale grid with station separations of 312 m to 10 km”, Boundary Layer Meteorology . 60, 309

- 324.

Hanna, S. R. and J. C. Chang, 1993a. Relations Between Meteorology and Ozone in the Lake Michigan

### 370

Region, Prepared for the Lake Michigan Air Directors Consortium, Prepared by Sigma Research, Inc., Concord, MA.

H anna, S. R. and J. C. Chang, 1993b. Characterization of Mixed Layer Depths During 1991 LMOS Intensive Study Periods, Prepared for the Lake Michigan Air Directors Consortium, Prepared by Sigma Research, Inc. Concord, MA.

Hanna, S. R., 1994. Mesoscale Meteo rological Model Evaluation Techniques with Emphasis on Needs of Air Quality Models, in Pielke, R. and R. Pearce, (ed). Mesoscale Modeling of the Atmosphere

, Meteoro. Monogr., No. 47, American Meteorological Society, Boston, MA.

Hanna, S. R., et al., 1995 . "Analysis and Evaluation of the UAM

- V, UAM

- IV, and ROM/UAM

- IV Photochemical Grid Models for the Region Around Lake Michigan", prepared for the Coordinating Research Council, Inc., and the National Renewable Energy Laboratory, prepared by Earth Tech,

Conc ord, MA.

Hanna, S. R., et al., 1998. “Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM

- IV) due to uncertainties in input variables.” Atmospheric Environment

, 32, 3619

- 3628.

Hanna, S. R., et al., 1998. Evaluations of Numerical Weather Prediction (NWP) Models from the Point of View of Inputs Required by Atmospheric Dispersion Models, 5 th 

### International Conference on Harmonization within Atmospheric Dispersion Modeling for Regulatory Purposes , 18

- 21 May, Rhodes,

Greec e.

Hanna, S. R., et al., 2001. “Uncertainties in predicted ozone concentrations due to input uncertainties for

the UAM

- V photochemical grid model applied to the July 1995 OTAG Domain”, Atmospheric
Environment, Vol. 34.

Henry, R. F., et al., 2000. “Effec ts of changes in data reporting practices on trend assessments”, Atmospheric Environment. 34: (16) 2659

- 2662.

Hidy, G. M., 2000. “Ozone Process Insights from Field Experiments Part I: Overview”, Atmospheric Environment, Vol. 34, pp. 2001

- 2022.

Hogrefe, C., et al., 2000. “Interpreting the information in ozone observations and model predictions relevant to regulatory policies in the Eastern United States”, Bulletin of the American Meteorological Society .81: (9)

2083

- 2106.

Hudischewskyj, B. and S. G. Doug las, 2000. "Weighting SAMI Air Quality Episodes to Calculate Annual Average Dry Deposition in the SAMI Region: Methods and Results of the Weighting Factors Analysis.” Systems Applications International, Inc., San Rafael, California. Technical memorandum.

Imhoff, R. E. and L. L. Gautney, 1998. “Regional Photochemical Model Estimation of the Scale of Ozone Transport in the Eastern U.S.”, 10 th 

### Conference on Applications of Air Pollution Meteorology with the Air

### 371

and Waste Management Association , American Mete orological Society, 11

- 16 January, Phoenix, AZ.

Imhoff, R. E., et al., 2001. “NOy Removal from the Cumberland Power Plant Plume”, Atmospheric

Environment

, Vol. 35, pp. 179

- 183.

Jacko, R. B., 2001. “Aircraft Measurements of Tropospheric Ozone Within the Lake Michigan Region”,

94 th 

### Annual Conference of the Air and Waste Management Association , 24

- 28 June 2001, Orlando, FL.

Jang, J., 2001. “Annual Application of the U.S. EPA’s Third

- generation Air Quality Modeling System Over

the Continental United States”

, 94 th 

### Annual Conference of the Air and Waste Management Association,

24

- 28 June 2001, Orlando, FL.

Kain, J. S. and J. M. Fritsch, 1990. One

- Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization, Journal of Atmos pheric Science, Vol. 47, pp. 2784

- 2802.

Kain, J. S. and J. M. Fritsch, 1993. Convective Parameterization for Mesoscale Models: The Kain

- Fritsch Scheme, The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr

., Vol. 46,

American Meteor

ological Society, pp. 165

- 170.

Kalloe, G. and K. Lagouvardos, 1997. “Atmospheric Modeling Simulation over the Eastern United States with the RAMS3b Model for the Summer of 1995”, prepared for the Electric Power Research Institute, Palo Alto, CA.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowlak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph, 1996: The NCEP/NCAR 40

- year reanalysis project. Bull. Amer. Meteor. Soc. , 77, 437

- 471.

Keating, T. J. and A. Farrell, 1999. “Transboundary Environmental Assessment: Lessons Learned from the Ozone Transport Assessment Group”, prepared for the Office of Air Qu ality Planning and Standards and the National Center for Environmental Decision Making, prepared by Carnegie Mellon University.

Kessler, E., 1969: On the distribution and continuity of water substance in the atmospheric circulation. Meteor. Monogr. , No. 32, Amer. Meteor. Soc., 1

- 84.

Kleinmman, L. I., G. M., 2000. “Ozone Process Insights from Field Experiments Part II: Observation

Based Analyses for Ozone Production”, Atmospheric Environment

, Vol. 34, pp. 2023

- 2033.

Koch, S. E., M. DesJardins, and P. J. Kocin, 1983: An interactive Barnes map analysis scheme for use

with satellite and conventional data. J. Climate Appl. Meteor. , 22, 1487

- 1503.

Koerber, M., 1998. “OTAG Modeling Results”, proceedings of the 10 th 

### Joint Conference on the Applications of Air Pollution Meteorology , 11

- 16 January, 1998, Phoenix, AZ.

### 372

Korc, M. E., et al., 1993. "Comparison of Emission Inventory and Ambient Concentration ratios of NMOC, NO

x 

### , and CO in the Lake Michigan Air Quality Region", prepared for the Lake Michigan Air

Direc tors Consortium, prepared by Sonoma Technology, Inc., Santa Rosa, CA.

Kunkel, K. E., S. A. Changnon, B. C. Reinke, and R. W. Arritt, 1996: The July 1995 heat wave in the

central United States. Bull. Amer. Meteor. Soc. , 77, 1507

- 1518.

Kuo, H. L., 1965. On Formation and Intensification of Tropical Cyclones Through Latent Heat Release by Cumulus Convection, J. Atmos. Sci., Vol. 22., pp. 40

- 63.

Kuo, H. L., 1974. Further Studies of the Parameterization of the Effect of Cumulus Convection on Large

Scale Flow

, J. Atmos. Sci., Vol. 31., pp. 1232

- 1240.

LADCo, 1991. "Lake Michigan Ozone Study: Episode Selection Document", Report Version 3.0, prepared by the Lake Michigan Air Directors Consortium, Des Plaines, IL.

LADCo, 1994. "Evaluation of the UAM

- V Photochemi cal Grid Model in the Lake Michigan Region", Version 2.0, prepared by the Lake Michigan Air Directors Consortium, Des Plaines, Illinois.

LADCo, 1995a. "Lake Michigan Ozone Study Project Report: Volume III

- Technical Data Base", prepared by the Lake Michi gan Air Directors Consortium, Des Plaines, IL.

LADCo, 1995b. "Lake Michigan Ozone Study Project Report: Volume IV

- Modeling", prepared by the Lake Michigan Air Directors Consortium, Des Plaines, IL.

Lagouvardos, K., et al., 2000. “An analysis of the me teorological and air quality conditions during an extreme ozone episode over the northeastern USA”, International Journal of Environmental Pollution . 14: (1

- 6) 581

- 587.

Lesht, B. M. and D. J. Brandner, 1992. Functional Representation of Great Lakes Surfa ce Temperature, J.
Great Lakes Res. , Vol. 18. Pp. 98

- 106.

Li, S., 2001. “Quantification of Variability and Uncertainty in Natural Gas

- fueled Internal Combustion Engine NOx and Total Hydrocarbon Emissions Factors”, 94 th 

### Annual Conference of the Air and Was te Management Association, 24

- 28 June 2001, Orlando, FL.

Lin, J., 2001. “Development of New Driving Cycles for Estimating Regional Air Quality Emissions Inventories”, 94 th 

### Annual Conference of the Air and Waste Management Association , 24

- 28 June 2001,

Orl ando, FL.

Loomis, C. F., et al., 1997b. “Development of the 2007 Emissions Inventory Reflecting the EPA Section 110 SIP call Requirements for the Eastern U.S.”, prepared by Alpine Geophysics, LLC, Golden, CO.

### 373

Louis, J. F., 1979. Parametric Model of Vert ical Eddy Diffusivity Fluxes in the Atmosphere, Boundary Layer Meteorology, Vol. 17, pp. 187

- 202.

Luria, M., et al., 1992. "Lake Michigan Ozone Study (LMOS): Measurements from an Instrumented Aircraft", Atmospheric Environment , Vol. 26A, No. 18, pp. 3265

- 3277.

Luria, M., et al., 1999. “The Evolution of Photochemical Smog in a Power Plant Plume”, Atmospheric

Environment , Vol. 31, pp. 3023

- 3036.

Lurmann, F. and N. Kumar, 1997. “Evaluation of the UAM

- V Model Performance in OTAG Simulations: Summary of Perfo rmance Against Surface Observations”, Final report prepared by Sonoma Technology, Inc., Prepared for the U.S. Environmental Protection Agency and the Ozone Transport Assessment Group, Research Triangle Park, NC.

Lyons, W. A., et al., 1995. Applications of the Regional Atmospheric Modeling System (RAMS) to Provide Input to Photochemical Grid Models for the Lake Michigan Ozone Study (LMOS), Journal of Applied Meteorology , Vol. 34, No. 8, pp. 1762

- 1786.

Mahrer, Y. and R. A. Pielke, 1977. Numerical Study of t he Airflow over Irregular Terrain, Beitr. Phys.

Atmos. , Vol. 50, pp. 98

- 113.

Main, H. and P. T. Roberts, 1993. "Validation and Analysis of the Lake Michigan Ozone Study Ambient VOC Data", prepared for the Lake Michigan Air Directors Consortium, prepared b y Sonoma Technology, Inc., Santa Rosa, CA.

Majeed, M. A., 2001. “Regional Scale Modeling of PM

- 2.5 for Eastern U.S.”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

McBride, J. L. and E. E. Ebert, 2000: Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia. Wea. Forecasting

, 15 , 103

- 121.

McNally, D. E., 1997. Development of Methodology for Mapping MM5 Fields onto Arbitrary Eulerian

Phot ochemical Air Quality Simulation Models (PAQSM), Alpine Geophysics, LLC, Golden, CO.

McNally, D. E. and T. W. Tesche, 1994. "MAPS2.3 User's Guide", Alpine Geophysics, LLC, Golden,

CO.

McNally, D. E. and T. W. Tesche, 1996. "Evaluation of the MM5 Model fo r the July 1988 and July 1995 Episodes and Comparison with the OTAG Meteorological Model, RAMS", 89th Annual Meeting of the Air and Waste Management Association, 23

- 28 June 1996, Nashville, TN.

McNally, D. E. and T. W. Tesche, 1996a. “Pittsburgh Regional Ozone Attainment Study: Evaluation of the MM5 Model for Three Episodes”, prepared for the Pennsylvania Department of Environmental Protection, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

### 374

McNally, D. E. and T. W. Tesche, 1996b. Evaluation of the MM 5 Model for the 1

- 11 July 1988 OTAG Episode over the Northeastern United States, prepared for Pennsylvania Power and Light Co., prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

McNally, D. E. and T. W. Tesche, 1997. “Comparative Evaluation of the CAMx and UAM

- V Models Over the Northeastern U.S. Using the July 1995 OTAG Episode and the NARSTO

- NE Intensive Field Study Data”, prepared for the Virginia Department of Environmental Quality, prepared by Alpine Geophysics, LLC, Golden, CO.

McNally, D. E. and T

. W. Tesche, 1997. “Modeled Effects of Indiana Point Source NOx Emissions Reductions on Local and Regional 1

- hr and 8

- hr Ground Level Ozone Concentrations in 1995 and 2007 Using Two OTAG Oxidant Episodes”, prepared for the Indiana Electric Utility Air Work group, prepared by Alpine Geophysics, LLC, Golden, CO.

McNally, D. E. and T. W. Tesche, 1997. “Photochemical Modeling Analysis of the Northwest “Ozone Free” OTAG States Using the July 1995 OTAG Modeling Episode”, prepared for Minnesota Power, prepared by Alpine Geophysics, LLC, Golden, CO.

McNally, D. E. and T. W. Tesche, 1997. “State

- Specific Photochemical Modeling Analysis Using the July 1995 and July 1991 OTAG Modeling Episodes”, prepared for the Ohio Environmental Protection Agency,

Division of Air Po llution Control, prepared by Alpine Geophysics, LLC, Golden, CO.

McNally, D. E. and T. W. Tesche, 1998. “Effects of Indiana Point Source NOx Emissions Reductions on Ground Level Ozone Concentrations Using the 07EPA1a Basecase Inventory”, prepared for the Indiana Electric Association, prepared by Alpine Geophysics, LLC, Covington, KY.

McNally, D. E. and T. W. Tesche, 1998. “Scale of Transport Analysis Based on Sub

- Regional Modeling Over the OTAG Domain in the Eastern U.S.”, 91 st 

### Annual Meeting of the Air and Waste Management

Association

, San Diego, CA, 14

- 19 June 1998.

McNally, D. E. and T. W. Tesche, 1999. Impact of Stack Parameter Errors on Ground Level Ozone Metrics in the OTAG Domains”, prepared for General Motors, prepared by Alpine Geophysics, LLC, Arvada, CO.

McNally, D. E., and T. W. Tesche, 1994. "MAPS2.3 User's Guide", Alpine Geophysics, LLC, Golden,

CO.

McNally, D. E., and T. W. Tesche, 1996a. "Evaluation of the MM5 Model for the July 1988 and July 1995 Episodes and Comparison with the OTAG Me teorological Model, RAMS", 89th Annual Meeting of the Air and Waste Management Association, 23

- 28 June 1996, Nashville, TN.

McNally, D. E., and T. W. Tesche, 1996b. “Evaluation of the MM5 Model for the 1

- 11 July 1998 OTAG Episode Over the Northeastern Uni ted States”, prepared for the Pennsylvania Power and Light Company,

### 375

prepared by Alpine Geophysics, LLC, Arvada, CO.

McNally, D. E., and T. W. Tesche, 1998. “Evaluation of the MM5 Meteorological Model over the Greater Denver Front Range Region for Two Wint ertime Episodes”, prepared for the Denver Regional Air Quality Council, prepared by Alpine Geophysics, LLC, Golden, CO.

McNally, D. E., et al., 1997. “Photochemical Modeling Analysis of the Pittsburgh

- Beaver Valley Ozone

Nonattainment Area: Volume IV: C I nterim Final Report”, prepared for the Southwestern Pennsylvania Clean Air Stakeholders Group, prepared by Alpine Geophysics, LLC, Covington, KY.

McNally, D. E., et al., 1998. “Nested Regional Photochemical Modeling in Support of the Pittsburgh

Beaver Val

ley Ozone SIP”, 10 th 

### Joint Conference on the Applications of Air Pollution Meteorology with the Air and Waste Management Association , 11

- 16 January, Phoenix, AZ.

McNally, D. E., et al., 1998. “Photochemical Modeling of the Effects of VOC and NOx Emissions

Controls in the Baltimore

- Washington Ozone Nonattainment Area”, prepared for the Maryland Department of Environment, prepared by Alpine Geophysics, LLC, Covington, KY.

McNider, R. T. and R. A. Pielke, 1981: Diurnal boundary layer development over slopin g terrain. J.
Atmos. Sci. , 38, 2198

- 2212.

McNider, R. T. and R. A. Pielke, 1984: Numerical simulation of slope and mountain flows. J. Climate and Appl. Meteor. , 23, 1441

- 1453.

McNider, R. T., et al., 1998. “Meteorological conditions during the 1995 So uthern Oxidants Study Nashville/Middle Tennessee field Intensive”, Journal of Geophysical Research , 103: 22225

- 22243.

McQueen, J. T., et al., 1997. Evaluation of the RAMS Model for Estimating Turbulent Fluxes Over the Chesapeake Bay, Atmospheric Environm ent, Vol. 31. pp. 3803

- 3819.

Mellor, B. L. and T. Yamada, 1974. Hierarchy of Turbulence Closure Models for Planetary Boundary Layers, J. Atmos. Sci., Vol. 31, pp. 1791

- 1806.

Mellor, B. L. and T. Yamada, 1982. A Development of a Turbulence Closure Model f or Geophysical Fluid Problems, Review of Geophysics and Space Physics, Vol. 20, pp. 851

- 875.

Milanchus. M. L., et al., 1998. “Evaluating the effectiveness of ozone management efforts in the presence of meteorological variability”, Journal of the Air and W aste Management Association

. 48: (3) 201

- 215.

Mocko, D. M. and W. R. Cotton, 1995: Evaluation of fractional cloudiness parameterizations for use in a

mesoscale model. J. Atmos. Sci. , 52, 2884

- 2901.

Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the ground layer of the atmosphere. Akad. Nauk SSR Geofiz. Inst. Tr. , 24, 163

- 187.

### 376

Morris R. E., et al., 1997. “Assessment of the Contribution of Emissions from Northern OTAG States on Elevated Ozone Concentrations”, prepared for Sout heast Michigan Council of Governments, prepared by ENVIRON International Corp., Novato, CA.

Morris R. E., et al., 1997. “Comparison of OTAG UAM

- V/BEIS2 Modeling Results with Ambient Isoprene and Other Related Species Concentration”, prepared for American Automobile Manufacturers Association, prepared by ENVIRON International Corp., Novato, CA.

Morris, R. E. and E. Tai, 1998. “Assessment of the Impact of Kentucky Sources on Downwind

Nonattainment and Assessment of the Sources that Contribute to Nonattainme nt in Kentucky”, prepared for the Utility Information Exchange of Kentucky, prepared by ENVIRON International Corp., Novato,

CA.

Morris, R. E., 1997. “Review of NESCAUM Report on Long

- Range Transport of Ozone and its Precursors in the Eastern U. S.”, prep ared for the Midwest Ozone Group, prepared by ENVIRON International Corp., Novato, CA.

Morris, R. E., 1998. “Review and Critique of the November 7, 1997 EPA Ozone Transport SIP call and

Assessment of the Contribution of Emissions from Missouri on Nonattai nment Using the July 1995 OTAG Database”, prepared for the Missouri Electric Utility Environmental Committee, prepared by ENVIRON International Corporation, Novato, CA.

Morris, R. E., 2001. “A Methodology for Quantifying Ozone Transport and Assessing the Benefits of Alternative Control Strategies”, 94 th 

### Annual Conference of the Air and Waste Management Association,

24

- 28 June 2001, Orlando, FL.

Morris, R. E., et al., 1996. “Assessment of the Effects of Potential Control Strategies on Emissions Sources

in Ohio and Downwind Ozone Levels”, The Emissions Inventory: Key to Planning, Permits, Compliance, and Reporting , Air and Waste Management Association, Pittsburgh, PA.

Morris, R. E., et al., 1997. “Area of Influence Ozone Source Apportionment Modeling for th e July 1991 OTAG Episode and the Northeast Corridor and Lake Michigan Nonattainment Regions”, prepared for Cinergy Corporation, prepared by ENVIRON International Corp, Novato, CA.

Morris, R. E., et al., 1997. “Ozone Source Apportionment Modeling Using th e July 1991 OTAG Episode for the Northeast Corridor and Lake Michigan Regions”, prepared for Cinergy Corporation, prepared by ENVIRON International Corp., Novato, CA.

Morris, R. E., et al., 2001, “Evaluation of the CAMx and Models

- 3/CMAQ Models over the N ortheastern U.S. Region’, draft final report prepared for the Coordinating Research Council, prepared by ENVIRON International, and Alpine Geophysics, LLC, Novato, CA.

Mueller, S. F., A. Song, W. B. Norris, S. Gupta, and R. T. McNider, 1996: Modeling p ollutant

### 377

transport during high

- ozone episodes in the southern Appalachian mountains. J. Appl. Meteor. , 35,

2105

- 2120.

NESCAUM, 1997. “The Long

- Range Transport of Ozone and Its Precursors in the Eastern United States”, prepared by the Northeast States for Coordinated Air Use Management, Boston, Mass.

Nicholls, M. E., et al., 1995. Applications of the RAMS Numerical Model to Dispersion over Urban Areas, Wind Climate in Cities, eds. J. E. Cermak et al., pp. 703

- 732. Kluwer Academic Publishers,

Dordrecht.

N orris, W. B., and K. G. Doty, 1998: Southern Appalachians Mountain Initiative: Meteorological Modeling Protocol

, Version 3, 38 pp.

Norris, W. B., and K. G. Doty., 1998. “Meteorological Modeling Protocol”, prepared for the Southern Appalachian Mountains Initiative (SAMI), prepared by the Georgia Institute of Technology, University of Alabama, and the University of Virginia.

Novak, D. J., et al., 2000. “A modeling study of the impact of urban trees on ozone”, Atmospheric

Environment

, 34: (10) 1601

- 1613.

Odman, T. M., 2001. “Response and Sensitivity of PM2.5 in the Southern Appalachian Mountains”, 94 th


### Annual Conference of the Air and Waste Management Association , 24

- 28 June 2001, Orlando, FL.

OEC, 2000. “Ohio Valley

- Ozone Alley Smog Pollution and Power Plants in the Ohio River Valley: What Can Be Done”, prepared by the Ohio Environmental Council, Ohio Valley Environmental Coalition, Regional Coalition of Ohio Valley Restoration, Columbus, OH.

OTAG, 1997. “Telling the OTAG Ozone Story with Data”, Fina l Report, Vol. I, Executive Summary, prepared by the Ozone Transport Assessment Group, Air Quality Analysis Workgroup.

OTAG, 1997. "Modeling Report", Draft Version 1.1, prepared by the Ozone Transport Assessment Group, 12 February.

OTAG, 1997b. “Ozone Transport Assessment Group Executive Report, 1997", prepared by the Ozone Transport Assessment Group.

Overcamp, T. J., 2001. “Plume Rise in a Shear Layer”, 94 th 

### Annual Conference of the Air and Waste Management Association

, 24

- 28 June 2001, Orlando, FL.

Owens, P., 2001. “Weekday/Weekend Variability and Long

- term Trends in Traffic, CO, NOy, and Ozone for the Charlotte Metropolitan Area During the 1990’s ”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

### 378

Pie lke, R. A. and M. Uliasz, 1998. Use of Meteorological Models as Input to Regional and Mesoscale Air Quality Models C Limits and Strengths, Atmospheric Environment, Vol. 32, No. 8, pp. 1455

- 1466.

Pielke, R. A., 1974. A Three

- Dimensional Numerical Model of the Sea Breeze Over South Florida, Monthly Weather Review , Vol. 102, pp. 115

- 134.

Pielke, R. A., 1984. Mesoscale Meteorological Modeling , Academic Press, 612 pp.

Pielke, R. A., et al., 1992. Comprehensive Meteorological Modeling System

-- RAMS, Metero. Atm

os. Phys., Vol. 49, pp. 69

- 91.

Pielke, R. and R. Pearce, 1994. Mesoscale Modeling of the Atmosphere

, Meteoro. Monogr

., No. 47, American Meteorological Society, Boston, MA.

Porter, C. W., 1995. Short Term High Resolution Forecasting of Fog, Cloud Ceiling Heights, and Visibilities with the PSU/NCAR Mesoscale Model, M. S. Thesis, The Pennsylvania State University, 197

pp.

Porter, P. S., et al., 1996. “Statistical Characteristics of Spatially

- Decomposed Ambient Ozone Time Series Data”, (Available at http://capita.wustl.edu/otag/reports/StatChar/otagrep.htm ). New York Department of Environmental Conservation, New York, NY.

Porter, P. S., et al., 2001. “Ozone Air Quality Over North Ame rica: Part II

–An Analysis of Trend Detection and Attribution Techniques ”, Journal of the Air and Waste Management Associa tion , Vol. 51, pp. 273

- 282.

Pruppacher, H. R., and J. D. Klett, 1980: Microphysics of clouds and precipitation . D. Reidel

Publishi ng Company, 714 pp.

Rao, S. T., et al., 1996. “Dealing with the Ozone Non

- Attainment Problem in the Eastern United States”, Environmental Manager, January, pp. 17

- 31.

Rao, S. T., et al., 1997. “Space and time scales in ambient ozone data”, Bulletin of th e American Meteorological Society

, 78: (10) 2153

- 2166.

Rao, S. T., et al., 1998. “Ozone Air Quality Over North America: A Critical Review of Trend Detection Techniques and Assessments”, prepared by the NARSTO Synthesis Team, Final Report available at http://www.cgenv.com/Narsto/

Rao, S. T., et al., 2000. “Determining the airshed for ozone and fine particle pollution in the Northeastern United States”, Abstracts Papers in Atmospheric Chemistry, S 219: U628

- U62 8 Part 1 MAR 26.

Reisner, J., R. J. Rasmussen, and R. T. Bruintjes, 1996. Explicit Forecasting of Supercooled Liquid Water

### 379

in Winter Storms using a Mesoscale Model, Quart . J. Roy, Meteor. Soc.

Reisner, J., R. T. Bruintjes, and R. J. Rasmussen, 1993. Pre liminary Comparisons between MM5 NCAR/Penn. State Model Generated Icing Forecasts and Observations, Fifth Intl. Conf. On Aviation Weather Systems . Vienna, VA, Amer. Meteoro. Soc., pp 65

- 69. Resources Board, prepared by Alpine Geophysics, LLC, Ft. Wright, K Y.

Reynolds, S. D., P. M. Roth, and T. W. Tesche, 1994. "A Process for the Stressful Evaluation of Photochemical Model Performance", prepared for the Western States Petroleum Association, prepared by Envair, San Rafael, CA and Alpine Geophysics, Crested B utte, CO.

Roberts, P. T., et al., 1995. Characteristics of VOC

- Limited and NOx Limited Areas Within the Lake Michigan Air Quality Region, prepared for the Lake Michigan Air Directors Consortium, prepared by Sonoma Technology, Inc., Santa Rosa, CA.

Roth, P. M., T .W. Tesche, and S. D. Reynolds, 1998. “A Critical Review of Regulatory Air Quality Modeling for Tropospheric Ozone”, prepared for the North American Research Strategy for Tropospheric Ozone (NARSTO), prepared by Envair, and Alpine Geophysics.

Rot h, P. M., 1999. “A Qualitative Approach to Evaluating the Anticipated Reliability of a Photochemical Air Quality Simulation Model for a Selected Application”, Journal of the Air and Waste Management

Association

, Vol. 49, pp. 1050

- 1059.

Roth, P. M., et a

l., 1997. “A Critical Review of Regulatory Air Quality Modeling For Tropospheric Ozone”, Critical Review Paper for the 1998 NARSTO Ozone Assessment , 16

- 20 Nov., W. Palm Beach,

FL.

Roth, P. M., T. W. Tesche, and S. D. Reynolds, 1995. "Protocol for Evaluati ng the Performance of the SARMAP Model", prepared for the SARMAP Technical Review Committee, prepared by Envair and Alpine Geophysics, LLC.

Russell, A. and R. Dennis, 2000. NARSTO Critical Review of Photochemical Models and Modeling”, Atmospheric Environm

ent , Vol. 34, pp. 2283

- 2324.

Russell, A. G. and R. L. Dennis, 1997. Critical Review of Photochemical Models and Modeling, NARSTO Critical Review Paper, 1998 NARSTO Ozone Assessment, submitted to Atmospheric Environment.

Russell, A. G., et al., 1998. “Air Quality Modeling Protocol”, prepared for the Southern Appalachian Mountains Initiative (SAMI), prepared by the Georgia Institute of Technology, University of Alabama, and the University of Virginia.

Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of

### 380

clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder

- feeder” process in

warm

- frontal rainbands. J. Atmos. Sci. , 40, 1185

- 1206.

Ryan, W. F., et al., 1998. “Pollutant Transport During a Regional Ozone Episode in the Mid

- Atlantic States”, Journal of the Air and Waste Management Association

, Vol. 48, pp. 786

- 796.

Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting , 5,

570

- 575.

Schere, K. L. and R. A. Wayland, 1989. "EPA Regional Oxidant Model (ROM2.0): Evaluation on 1980 NEROS Data Bases", EPA/600/3

- 80/057, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC.

Schichtel. B. A. and R. A. Husar, 2001. “East ern North American Transport Climatology During High

- and

Low

- Ozone Days”, Atmospheric Environment

, Vol. 35, pp. 1029

- 1038.

Schwab, D. J., et al., 1992. Satellite Measurements of Water Surface Temperature in the Great Lakes

Great Lakes Coast Watch, J. Gr eat Lakes Res. , Vol. 18, pp. 116

- 125.

Seaman, N. L. and D. R. Stauffer, 1996. "SARMAP Meteorological Model Final Report", prepared for the San Joaquin Valleywide Air Pollution Study Agency, prepared by the Department of Meteorology, Pennsylvania State Uni versity, University Park, PA.

Seaman, N. L. and S. A. Michelson, 1998. Mesoscale Meteorological Structure of a High

- Ozone Episode During the 1995 NARSTO

- Northeast Study, Journal of Applied Meteorology , (submitted).

Seaman, N. L., 1995. A Status of Meteor ological Pre

- Processors for Air Quality Modeling, International Conf. On Particulate Matter, Air and Waste Mgt. Assn., Pittsburgh, PA.

Seaman, N. L., 1996. A Study of Meteorological Variables Needed in Air

- Quality Modeling, Annual Progress Report prepared

for the Coordinating Research Council (CRC), Interim Report, Project A

- 11, prepared by the Department of Meteorology, Penn State University, State College, PA.

Seaman, N. L., 1998. A Meteorological Modeling for Air Quality Assessments, NARSTO Critical R eview Paper, 1998 NARSTO Ozone Assessment, submitted to Atmospheric Environment.

Seaman, N. L., 2000. “Meteorological Modeling for Air Quality Assessments”, Atmospheric Environment, Vol. 34, pp. 2231

- 2260.

Seaman, N. L., and D. R. Stauffer, 1996. "SARMAP Meteorological Model Final Report", prepared for the San Joaquin Valleywide Air Pollution Study Agency, prepared by the Department of Meteorology, Pennsylvania State University, University Park, PA.

Seaman, N. L., D. R. Stauffer, and L. M. Lario, 1995. " MultiScale Four

- Dimensional Data Assimilation

### 381

System Applied to the San Joaquin Valley During SARMAP. Part I: Modeling Design and Basic Performance Characteristics", J. Appl. Meteo. , Vol. 34, pp. 1739

- 1761.

Seaman, N. L., D. R. Stauffer, and T. W. Tesche, 1992. "The SARMAP Meteorological Model: A Four

Dimensional Data Assimilation Technique Used to Simulate Mesobeta

- Scale Meteorology During a High

Ozone Episode in California", International Specialty Conference on Tropospheric Ozone Nonattainment and Desig n Value Issues , U.S. EPA/AWMA, 27

- 30 October, Boston, MA.

Seaman, N. L., et al., 1996. "Application of the MM5

- FDDA Meteorological Model to the Southern California SCAQS

- 1997 Domain: Preliminary Test Using the SCAQS August 1987 Case", Ninth Joint

Conferen ce on Applications of Air Pollution Meteorology , American Meteorological Society, 28 January

- 2 February, Atlanta, GA.

Seaman, N. L., et al., 1997. "The Use of the San Joaquin Valley Meteorological Model in Preparation of a Field Program in the South Coast Air Basin and Surrounding Regions of Southern California: Volume II

-- Numerical Modeling Studies for the Development and Application of a Guidance Technique to Support of the Design of the 1997 Southern California Ozone Study Field Program, prepared for the California Air Resources Board, prepared by the Department of Meteorology, Pennsylvania State University, University Park, PA.

Sellers, P. J., Y. Mintz, Y. C. Sud, and A. Dalcher, 1986: A simple biosphere model (SiB) for use within general circulatio n models. J. Atmos. Sci. , 43, 505

- 531.

Shafran, P. C. and N. L. Seaman, 1998. Comparison of Numerical Predictions of Boundary

- Layer Structure Over the Midwest During the Lake Michigan Ozone Study (LMOS), 10 th 

### Joint Conference on the Applications of Air P ollution Meteorology with the Air and Waste Management Association , Phoenix,

AZ. 11

- 16 January.

Sillman, S., 2000. “Ozone production efficiency and loss of NOx in power plant plumes: Photochemical model and interpretation of measurements in Tennessee”, Jo urnal of Geophysical Research

. 105: 9189

- 9202.

Sistla, G., 2001. “An Assessment of Sulfur Dioxide Emissions Reductions and the Relative Impacts of Sulfates over New York State”, prepared by the Bureau of Air Resources, New York State Department of

Enviro nmental Conservation, Albany, NY.

Sistla, G., et al., 1996. “Effects of Uncertainties in Meteorological Inputs on Urban Airshed Model Predictions and Ozone Control Strategies”, Atmospheric Environment , Vol. 30, NO. 12. pp. 2011

- 2025.

Sistla, G., et al., 2001. “A Comparison of Measured and Simulated Ozone Concentrations in Rural Areas of the Eastern United States During Summer 1995”, Journal of the Air and Waste Management

Association

, Vol. 51. pp. 374

- 386.

### 382

Stauffer, D. R. and N. L. Seaman, 1990. Use of F our

- Dimensional Data Assimilation in a Limited

- Area Mesoscale Model. Part I: Experiments with Synoptic Data. Mon. Wea. Rev.

, 118 , 1250

- 1277.

Stauffer, D. R. and N. L. Seaman, 1994. On Multi

- Scale Four

- Dimensional Data Assimilation, J. Appl.

Meteor.

, 33

, 416

- 434.

Stauffer, D. R., N. L. Seaman, and F. S. Binkowski, 1991. Use of Four

- Dimensional Data Assimilation in a

Limited

- Area Mesoscale Model. Part II: Effects of Data Assimilation Within the Planetary Boundary Layer. Mon. Wea. Rev.

, 119 , 734

- 754.

Stau ffer, D. R., T. T. Warner, and N. L. Seaman, 1985. "Newtonian "Nudging" Approach to Four

Dimensional Data Assimilation: Use of SESAME

- IV Data in a Mesoscale Model", 7th Conference on

Numerical Weather Prediction , Montreal, Canada.

Stephens, G. L., 1978: R

adiation profiles in extended water clouds. II: Parameterization Schemes. J.
Atmos. Sci. , 35, 2123

- 2132.

Steyn, D. G. and I. G. McKendry, 1988. Quantitative and Qualitative Evaluation of a Three

- Dimensional

Mesoscale Numerical Model Simulation of a Sea Breeze in Complex Terrain, Monthly Weather Review, Vol. 116, pp. 1914

- 1926.

Steyn, D. G., and I. G. McKendry, 1988. “Quantitative and Qualitative Evaluation of a Three

- Dimensional Mesoscale Numerical Model Simulation of a Sea Breeze in Complex Terrain”, M onthly Weather Review, Vol. 116, pp. 1914

- 1926.

STI, 1996. “Evaluation of the UAM

- V Model Performance in the OTAG Simulations: Summary of Performance Against Surface Observations”, Draft, prepared for the Ozone Transport Assessment Group, prepared by Sono ma Technology, Inc., Santa Rosa, CA.

Tangirala, R. S., 2001. “Precursor Emissions and Ground

- level Ozone NAAQS Exceedances in the Metropolitan Washington, DC Non

- attainment Area: Day of Week Patterns”, 94 th 

### Annual Conference of the Air and Waste Managemen t Association

, 24

- 28 June 2001, Orlando, FL.

Tesche, T. W. and D. E. McNally, 1993a. "Operational Evaluation of the CAL

- RAMS Meteorological Model for LMOS Episode 1: 26

- 28 June 1991", prepared for the Lake Michigan Air Directors Consortium, prepared by Alpine Geophysics, Crested Butte, CO.

Tesche, T. W. and D. E. McNally, 1993b. "Operational Evaluation of the CAL

- RAMS Meteorological Model for LMOS Episode 2: 17

- 19 July 1991", prepared for the Lake Michigan Air Directors Consortium, prepared by Alpine Ge ophysics, Crested Butte, CO.

Tesche, T. W. and D. E. McNally, 1993c. "Operational Evaluation of the CAL

- RAMS Meteorological Model for LMOS Episode 3: 25

- 26 August 1991", prepared for the Lake Michigan Air Directors Consortium, prepared by Alpine Geophysic s, Crested Butte, CO.

### 383

Tesche, T. W. and D. E. McNally, 1993d. "Operational Evaluation of the CAL

- RAMS Meteorological Model for LMOS Episode 4: 20

- 21 June 1991", prepared for the Lake Michigan Air Directors Consortium, prepared by Alpine Geophysics, Creste d Butte, CO.

Tesche, T. W. and D. E. McNally, 1993e. "Operational Evaluation of the SARMAP Meteorological Model (MM5) for Episode 1: 3

- 6 August 1990", prepared for the Valley Air Pollution Study Agency, prepared by Alpine Geophysics, Crested Butte, CO.

T esche, T. W. and D. E. McNally , 1993 . "Operational Evaluation of the SARMAP Meteorological Model (MM5) for Episodes 2: 27

- 29 July 1990", prepared for the Valley Air Pollution Study Agency, prepared by Alpine Geophysics, Crested Butte, CO.

Tesche, T. W. an

d D. E. McNally, 1994. "Evaluation of the UAM

- V Over Lower Lake Michigan for Two 1991 Oxidant Episodes", prepared for the Lake Michigan Air Directors Consortium, prepared by Alpine Geophysics, Crested Butte, CO.

Tesche, T. W. and D. E. McNally, 1996. “Ev aluation of the MM5 Model for Three 1995 Regional Ozone Episodes over the Northeast United States”, prepared for the Southwestern Pennsylvania Clean Air Stakeholders Group, prepared by Alpine Geophysics, LLC, Covington, KY.

Tesche, T. W. and D. E. McNally , 1996. “Pittsburgh Regional Ozone Attainment Study: Volume X

-- Evaluation of the MM5 Model for Three 1995 Regional Ozone Episodes over the Northeast United States”, prepared for the Southwestern Pennsylvania Clean Air Stakeholders Group, prepared by Alpi ne Geophysics, LLC, Covington, KY.

Tesche, T. W. and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study

–Phase II, Work Element 5 Technical Report: Comparative Evaluation of the MM5 and RAMS Models for the July 1991 OTAG Episode”, prep ared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study C Phase I:

Work Element 3: Assessment of the OTAG Data Sets

-- Task 2 Technical M emorandum: Review of the OTAG Meteorological Inputs and Outputs”, prepared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Covington, KY.

Tesche, T. W. and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study C Phase II: Work Element 6 Technical Report: Initial Evaluation of the SAQM Regional Model for the 13

- 21 July 1991 OTAG Episode”, prepared for the Ohio Environmental Protection Agency and the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Covington, KY.

Tes che, T. W. and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study C Phase II: Status Report on SAQM/UAM

- V Comparative Model Evaluation”, prepared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Covington, KY.

### 384

Tesche, T. W. and D. E. McNally, 1996b. Evaluation of the MM5 Model for Three 1995 Regional Ozone Episodes over the Northeast United States, prepared for the Southwestern Pennsylvania Clean Air Stakeholders Group, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1997. “The Use of the San Joaquin Valley Meteorological Model in Preparation of a Field Program in the South Coast Air Basin and Surrounding Regions of Southern

California. Volume I: Final MM5 Evaluation for the 3

- 6 August 1990 SARMAP Episode”, prepared for the California Air Resources Board, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1997. Use of the MM5 Model as an Aid in Designing the 1997 Southern California Ozone Study: Part I , Final Evaluation of the MM5 Model for the 3

- 6 August 1990 SARMAP Episode over the Central California Region, prepared for the California Air

Tesche, T. W. and D. E. McNally, 1998. “Cincinnati

- Hamilton Ozone Attainment Demonstration Study:

Volume 5: Eva luation of the MM5 Model for the 18

- 22 June 1994 Episode”, prepared for the Ohio Environmental Protection Agency”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1998. “Evaluation of the MM5 Model for Two 1993 Regiona l Ozone Episodes over the Gulf Coast”, prepared for the Minerals Management Service and the Offshore Operators Committee, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1998. “Examination of CAMx, SAQM, and UAM

- V Perf ormance and Response to Emissions Changes Over the Eastern U.S. for Various OTAG, LMOS, and NARSTO Episodes”, 91 st 

### Annual Meeting of the Air and Waste Management Association , San Diego, CA, 14

- 19

June 1998.

Tesche, T. W. and D. E. McNally, 1998. “Modeled Effects of Indiana Point Source NOx Emissions Reductions on Local and Regional 1

- Hr and 8

- Hr Ground Level Ozone Concentrations in 1995 and 2007 Using Two OTAG Oxidant Episodes”, prepared for the Indiana Electric Utility Air Workgroup, prepared by Alpine Ge ophysics, LLC, Covington, KY.

Tesche, T. W. and D. E. McNally, 1998. “Recommendations for Air Quality Dispersion Models and Related Aerometric Data Sets in Support of the Breton Aerometric Monitoring Program (BAMP)”, prepared for the Offshore Operators Co mmittee, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 1999. “Comparative Evaluation of the MM5 and RAMS3c Prognostic Meteorological Models over the Midwestern U.S. for Two 1991 LMOS Intensive Measurement Episodes”, prepared for the Coordinating Research Council, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 2000a. “Evaluation of the RAMS3c Prognostic Meteorological Model over the Southeastern U.S. for Three Southern Appalachian Mountain Initiative (SAMI) Episodes”, draft final report prepared for the Tennessee Valley Authority and SAMI, prepared by Alpine Geophysics, LLC,

### 385

Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 2001. “Evaluation of the MM5 Prognostic Meteorological Mod el over Central Florida for Nine Peninsular Florida Ozone Study (PFOS) 8

- hr Ozone Episodes”, report prepared for the Florida Department of Environmental Protection Agency, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally , 2001. “Evaluation of the MM5 Prognostic Meteorological Model over the Gulf Coast Region for the 6

- 11 September 1993 COAST Episode”, report prepared for private client, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. and D. E. McNally, 2000b. “Evaluation of the MM5 Mesoscale Model Over the Central

U.S. For the 11

- 24 June 1995 Ozone Episode”, prepared for the Kansas Department of Health and Environment and the U.S. Environmental Protection Agency”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W. et al., 2001a. “Evaluation of CAMx and Models

- 3/CMAQ Over the Lower Lake Michigan Region with Inputs from the RAMS3c and MM5 Models”, prepared for the Coordinating Research Council, prepared by Alpine Geophysics, LLC and ENVIRON International, Ft.Wright, KY.

Tesche, T. W. et al., 2001b. “Evaluation of the MM5 Model Over the Midwestern U.S. for Three 8

- Hr Oxidant Episodes”, prepared for the Kansas Department of Health and Environment and the Missouri Department of Natural Resource s, prepared by Alpine Geophysics, LLC and ENVIRON International, Ft.Wright, KY.

Tesche, T. W., 1991a. "Evaluation of Regional Atmospheric Models", State

- of

- Science Synthesis Paper, Comprehensive Modeling System Workshop , Sponsored by EPRI, U.S. DOE, NOAA, U.S. EPA, Environment Canada, API, and the Ontario Ministry of Environment, 7

- 8 November, Atlanta, GA.

Tesche, T. W., 1991b. "Evaluation Procedures for Using Numerical Meteorological Models as Input to Photochemical Models", 7th Joint Conference on Appli cations of Air Pollution Meteorology ; American Meteorological Society, 14

- 18 January, New Orleans, LA.

Tesche, T. W., 1994. "Evaluation Procedures for Regional Emissions, Meteorological, and Photochemical Models", 86th Annual Meeting of the Air and Waste Management Association

, 14

- 18 June, Denver, CO.

Tesche, T. W., and D. E. McNally, 1993b. "Operational Evaluation of the SARMAP Meteorological Model (MM5) for two San Joaquin Valley Ozone Episodes", prepared for the Valley Air Pollution Study Agency, pre

pared by Alpine Geophysics, Crested Butte, CO.

Tesche, T. W., and D. E. McNally, 1996. “Superregional Ozone Modeling and Analysis Study

–Phase II, Work Element 5 Technical Report: Comparative Evaluation of the MM5 and RAMS Models for the July

1991 OTAG E

pisode”, prepared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

### 386

Tesche, T. W., and D. E. McNally, 1998b. “Cincinnati

- Hamilton Ozone Attainment Demonstration Study:

Volume 5: Evaluation of the MM5 Model for the 18

- 22 June 1994 Episode”, prepared for the Ohio Environmental Protection Agency”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W., and D. E. McNally, 2001a. “Evaluation of the MM5 Prognostic Meteorological Model

over Central Florida for Nine Penin sular Florida Ozone Study (PFOS) 8

- hr Ozone Episodes”, report prepared for the Florida Department of Environmental Protection Agency, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W., and D. E. McNally, 2001b. “Application of the MM5 Prog nostic Meteorological Model over the South Central U.S. for the September 1993 COAST Ozone Episode”, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W., D. E. McNally, and C. F. Loomis, 1999. “Estimation of Emissions Reduction Targets for

t he Pennsylvania Stakeholders Study”, prepared for the Pennsylvania Department of Environmental Protection prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W., D. E. McNally, and C. F. Loomis, 1999. “Impact of Incorrect Grid M Point Source

E missions Data on Ozone Model Performance and Year 2007 CAA and SIP Call Projections”, prepared for the Indiana Electric Association, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Tesche, T. W., D. E. McNally, and Christopher Emery, 2001. “Evaluation of the MM5 Model Over the

Midwestern U.S. for Three 8

- Hr Oxidant Episodes”, report prepared for Kansas Department of Health and Environment and the Missouri Department of Natural Resources, prepared by Alpine Geophysics, LLC, Ft. Wright, KY and ENVIRON, I nternational, Novato, CA.

Tesche, T. W., et al., 1990. "Improved Treatment of Procedures for Evaluating Photochemical Models", prepared for the California Air Resources Board, prepared by Alpine Geophysics, Crested Butte, CO.

Contract No. A832

- 103.

Tesch e, T. W., et al., 1997. “Photochemical Modeling Analysis of the Pittsburgh

- Beaver Valley Ozone

Nonattainment Area: Volume VI

-- Attainment Demonstration”, prepared for the Pennsylvania Department of Environmental Protection, prepared by Alpine Geophysics, LLC, Covington, KY.

Tesche, T. W., et al., 1997. “Superregional Ozone Modeling and Analysis Study C Phase II: Comparative Evaluation of the SAQM and UAM

- V Models for the 13

- 21 July 1991 OTAG Episode”, prepared for the Midwest Ozone Group, prepared by Alp ine Geophysics, LLC, Covington, KY.

Tesche, T. W., et al., 1998. “Analysis of the Effects of VOC and NOx Emissions Reductions in the Eastern

United States on Peak 1

- hr and 8

- hr Ozone Concentrations”, prepared for the Midwest Ozone Group, prepared by Alpin e Geophysics, LLC, Covington, KY.

Tesche, T. W., et al., 1998. “Photochemical Modeling Analysis of the Subregional Effects of the EPA

### 387

Section 110 SIP Call Within and Downwind of the State of Virginia”, prepared for AlliedSignal, Inc., prepared by Alpine G

eophysics, LLC, Covington, KY.

Tesche, T. W., et al., 1998. “Application of EPA’s Flexible Attainment Demonstration Guidance to the Pittsburgh

- Beaver Valley Ozone Nonattainment Area”, 10 th 

### Conference on Applications of Air Pollution Meteorology with the A

ir and Waste Management Association , American Meteorological Society, 11

- 16 January, Phoenix, AZ.

Tesche, T. W., et al., 1998. “Assessment of the Reliability of the OTAG Modeling System”, proceedings of

the 10 th 

### Joint Conference on the Applications of Ai r Pollution Meteorology , 11

- 16 January, 1998, Phoenix,

AZ.

Tesche, T. W., et al., 1998. “Cincinnati

- Hamilton Ozone Attainment Demonstration Study: Volume 3: Interim Attainment Demonstration Results”, prepared for the Ohio Environmental Protection Agency a nd the Kentucky Natural Resources and Environmental Protection Cabinet, prepared by Alpine Geophysics, LLC, Covington, KY.

Tesche, T. W., et al., 1998. “Final Evaluation of the MM5 Model for the 3

- 6 August 1990 SARMAP Episode over Central California", 10 t h 

### Joint Conference on Applications of Air Pollution Meteorology

, Phoenix, AZ.

Tesche, T. W., et al., 1998. “Review of the EPA Ozone Transport SIP Call and Recent Post

- OTAG Modeling and Analysis Studies”, prepared for the Ohio EPA, prepared by Alpine Geoph ysics, LLC, Ft. Wright, KY.

Tesche, T. W., et al., 1998. “Tri

- State Regional Ozone Modeling Study: Results of the Sub

- Regional Modeling of the EPA Section 110 SIP call”, prepared for the Greater Cincinnati Chamber of Commerce, prepared by Alpine Geophysic s, LLC, Covington, KY.

Tesche, T. W., et al., 2000. “Evaluation of the CAMx and Models

- 3/CMAQ Models over the Lake Michigan Region with Inputs from the MM5 and RAMS3c Models”, draft final report prepared for the Coordinating Research Council, prepared by Alpine Geophysics and ENVIRON International, Ft. Wright, KY.

Tesche, T. W., D. E. McNally, C. F. Loomis, and J. G. Wilkinson, 2002. “Regional Photochemical Modeling over Central Florida for Nine 8

- hr Ozone Episodes”, Final Report prepared for the Flori da Department of Environmental Protection, prepared by Alpine Geophysics, LLC, Ft. Wright, KY.

Thompson, A. M. and R. W. Stewart, 1991. “Effect of Chemical Kinetic Uncertainties on Calculated Constituents in a Tropospheric Photochemical Model”, Journal of Geophysical Research

, Vol. 96, pp.

13089

- 13108.

Thompson, M. L., et al., 2001. “A Review of Statistical Methods for the Meteorological Adjustment of Tropospheric Ozone”, Atmospheric Environment

, Vol. 35, pp. 617

- 630.

### 388

Tonneson, G. S., et al., 1998. “Esti mating the Area of Influence of Ozone Produced by Local Precursor Emissions for a Summer Period with a Range of Photochemical Activity”, 10 th 

### Joint Conference on the Applications of Air Pollution Meteorology , American Meteorological Society, Phoenix, AZ. 1 1

- 16 January.

Tremback, C. J. and R. Kessler, 1985. Surface Temperature and Moisture Parameterization for Use in Mesoscale Numerical Models, Seventh Conf. On Numerical Weather Prediction , Montreal, Quebec, Canada, American Meteorological Society, pp. 355

- 358.

Tremback, C. J., 1990. Numerical Simulation of a Mesoscale Convective Complex: Model Development and Numerical Results, Ph.D. dissertation, Atmospheric Science Paper no. 465, Colorado State University, Fort Collins, CO.

Tremback, C. J., et al., 1997 . Development of Meteorological Inputs for the Urban Airshed Model: LMOS Episode 1: 22

- 28 June 1991, prepared for the Illinois Environmental Protection Agency, prepared by Mission Research Corporation, Fort Collins, CO, Project ACA

- 695.

Tripoli, G. J. and W. R. Cotton, 1982. The Colorado State University Three

- Dimensional Cloud/Mesoscale Model C 1982, Part I: General Theoretical Framework and Sensitivity Experiments, J. Rech. Atmos., Vol. 16, pp. 185

- 220.

Turner, D. B., 1986. A Comparison of Three Methods for Calculating the Standard Deviation of the Wind Directions, Journal of Climate and Applied Meteorology , Vol. 25. pp. 703

- 707.

Turner, J. S., 1962: The starting plume in neutral surroundings. J. Fluid Mech. , 13, 356

- 368.

U.S.

- Canada Air Quality Comm ittee, 1999. “Ground

- Level Ozone: Occurrence and Transport in Eastern North America”, Report by Subcommittee 1: Program Monitoring and Reporting, United States

- Canada Air Quality Committee. March 1999.

Ulrickson, B. L., and C. F. Mass, 1990. “Numerical In vestigation of Mesoscale Circulations Over the Los Angeles Basin, Part I: A Verification Study”, Monthly Weather Review, Vol. 118, pp. 2138

- 2161.

Uthe, E. E., et al., 1992. "Airborne Lidar Mapping of Ozone Concentrations During the Lake Michigan

Ozone S tudy", Journal of the Air and Waste Management Association , Vol. 42, pp. 1313

- 1318.

Valente, R. J., et al., 1998. “Ozone production during an urban air stagnation episode over Nashville, Tennessee”, Journal of Geophysical Research

, 103: 22,555

- 22,568.

Vu kovich, F. M., 1995. “Regional

- Scale Boundary Layer Ozone Variations in the Eastern United States and the Association with Meteorological Variations”, Atmospheric Environment , Vol. 29, pp. 2250

- 2273.

Vukovich, F. M., 1997. “Time Scales of Surface Ozone Va riations in the Regional, Non

- urban Environment”, Atmospheric Environment , Vol. 31, pp. 1513

- 1530.

### 389

Walko, R. L. and C. J. Tremback, 1991. "RAMS: The Regional Atmospheric Modeling System, Version 2c: User's Guide", prepared by ASTeR, Inc., Fort Collins, CO

.

Wesley, M. L. and B. B. Hicks, 2000. “A Review of the Current Status of Knowledge on Dry Deposition”, Atmospheric Environment

, Vol. 34, pp. 2261

- 2282.

Wheeler, N. J., et al., 1997. “Assessment of the OTAG Modeling Studies and Urban Boundary

Conditions” , prepared for the American Petroleum Institute, prepared by MCNC

- Environmental Programs, Research Triangle Park, N.C.

Wilkinson, J. G., et al., 1994. Technical Formulation Document: SARMAP/LMOS Emissions Modeling System (EMS

- 95) . AG

- 90/TS26 & AG

- 90/TS27. Prepared for the Lake Michigan Air Directors Consortium, Des Plaines, IL & The Valley Air Pollution Study Agency, Technical Support Division, Sacramento, CA. Prepared by Alpine Geophysics, Pittsburgh, PA.

Wilkinson, J. G., et al., 1998. “Emissions Model ing Protocol”, prepared for the Southern Appalachian Mountains Initiative (SAMI), prepared by the Georgia Institute of Technology, University of Alabama, and the University of Virginia.

Wilmont, C. J., 1981. “On the Validation of Models”, Phys. Geogr. , V ol. 2, No. 2, pp. 168

- 194.

Wishinski, P. R., 2001. “An Evaluation of an Operational Mesoscale Meteorological Model in Predicting Surface Wind Fields and Temperatures Over Complex Terrain and Coastal Regions of the Northeastern U.S.”, 94 th 

### Annual Conferenc e of the Air and Waste Management Association , 24

- 28 June 2001, Orlando,

FL.

Wolf, G. T., et al., 2001. “Ozone Air Quality Over North America: Part I

–A Review of Reported

Trends ”, Journal of the Air and Waste Management Association , Vol. 51, pp. 273

- 282

.

Yamartino, R. J., 1984. “A Comparison of Several ‘Single

- Pass’ Estimators of the Standard Deviation of Wind Direction”, Journal of Climate and Applied Meteorology , Vol. 23. Pp. 1362

- 1366.

Yamazaki, T., J. Kondo, T. Watanabe, and T. Sato, 1992: A heat

- balance model with a canopy of one or two layers and its application to field experiments. J. Appl. Meteor. , 31, 86

- 103.

Zhang J., et al., 1998. “Meteorological processes and ozone exceedances in the Northeastern United States during the 12

- 16 July 1995 episode”. Journal of Applied Meteorology , 37: (8) 776

- 789.

Zhang, D. L. and R. A. Anthes, 1982. High

- Resolution Model of the Planetary Boundary Layer Sensitivity Tests and Comparisons with SESAME

- 79 Data, Journal of Applied Meteorology , Vol. 21. pp. 1594

- 1609.

### 390

Zhang, J. and S. T. Rao, 1999. “The role of vertical mixing in the temporal evolution of ground

- level ozone concentrations”, Journal of Applied Meteorology , (12) 1674

- 1691.

Zheng, A. “Quantification of Variability and Uncertainty in Emissions Inv entories: A Prototype Software Tool with Application to Utility NOx Emissions”, 94 th 

### Annual Conference of the Air and Waste Management Association, 24

- 28 June 2001, Orlando, FL.

Zurbenko, I. G., et al., “Mapping Ozone in the Eastern United State”, Environ mental Manager , February

24

- 30.

### 391

10. APPENDIX 1:

OUTPUT FROM EPISODE SELECTION SOFTWARE

The following is the episode software output for the 68 day Case 15 set of episode days. In order to get the correct output for all 3 pollutants, the software ha s to be run twice. The 68 days should be input to get the errors and outputs for ozone and visibility. Then the full 80 days (68 days plus 11 non

- rain days plus 940212) must be input to get the correct errors for the acid deposition episodes (the softwar e will only provide acid deposition outputs if each 8 day deposition period is entered). The following output has been cut and pasted so that the correct values are displayed for all 3 pollutants.

Use all days = false fin[1][1]= . \

run \ gsmacid.run3.1.reru

n fin[1][2]= . \

run \ snpacid.run3.2.rerun fin[2][1]= . \

run \ gsmvis.run1.8.rerun fin[2][2]= . \

run \ snpvis.run1.2.rerun fin[3][1]= . \

run \ gsm126.run2.rer fin[3][2]= . \

run \ snp126.run2.4.rerun Day list file = . \

run \ out5 \ case25t1.txt

Tolerance = 0.800000 actual days per data day for each set: 8 ,1 ,1 , Observation based metric [1][1] = 30.225004, # seasons = 6 Observation based metric [1][2] = 23.948332, # seasons = 6 Observation based metric [2][1] = 892574.375000, # seasons = 5 Observation based metric [2][2] = 829 570.812500, # seasons = 6 Observation based metric [3][1] = 68817.117188, # seasons = 5 Observation based metric [3][2] = 66109.351562, # seasons = 5 Number of initial days input: 68 This represents 68 actual modeling days and 10 episodes. Of these days , 1.5% are in episodes of length 2 or less.

910723

910724

910725

910726

910727

910728

910729

910730

910731

920624

920625

920626

920627

920628

920629

### 392

930323

930324

930325

930326

930327

930328

930329

930330

930331

930511

930512

930513

930514

930515

930516

930517

930803

930804

930805

930806

930807

930808

930809

930810

930811

940208

940209

940210

940211

940213

950426

950427

950428

950429

950430

950501

950502

950503

950524

950525

950526

950527

950528

950529

### 393

950711

950712

950713

950714

950715

950716

950717

9507 18

950719

ERRORS FOR EACH TREE WITH THIS SET OF DAYS:

fin[1][1]: bin= 1, class=1, WA/WP= 1.093, n(j)/m(j)=61.000, dates= 950523 bin= 2, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 4, class= 2, WA/WP= 1.186, n(j)/m(j)= 8.000, dates= 930323 940208 bin= 5, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 6, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 7, class=1, WA/WP= 1.081, n(j)/m(j)=25.000, dates= 950425 bin= 8, class=2, WA/WP= 0 .000, n(j)/m(j)= 0.000, dates= bin= 9, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=11, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, class=3, WA/WP= 1.268, n(j)/m(j)= 4.000, dates= 9 30511 930803 bin=13, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=14, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=2, WA/WP= 1.122, n(j)/m(j)=17.000, dates= 920623 bin=16, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=17, clas s=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=18, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=19, class=4, WA/WP= 1.000, n(j)/m(j)= 5.000, dates= 910723 910723, class=4, val= 5.49 920623, class=2, val= 0.89 930323, class=2, val= 1.13 930511, class=3, val= 1.64 930803, class=3, val= 1.89 940208, class=2, val= 1.17 950425, class=1, val= 0.69 950523, class=1, val= 0.17 B[1]= 1.136, B[2]= 2.023, B[3]= 1.846, B[4]= 1.600, rescaled Ar=31.588583, S=6.00000 0, N= 221, sum P(j)= 221 usedays=8, distance error=0.045114, biased error=

- 0.370603

### 394

fin[1][2]: bin= 1, class=1, WA/WP= 1.000, n(j)/m(j)=38.000, dates= 920623 950711 bin= 2, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=2, WA/WP= 1.541, n( j)/m(j)=11.000, dates= 950425 bin= 4, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 5, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 6, class=3, WA/WP= 1.403, n(j)/m(j)=14.000, dates= 930323 bin= 7, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, da tes= bin= 8, class=2, WA/WP= 1.619, n(j)/m(j)= 4.500, dates= 940208 950523 bin= 9, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=11, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, class =4, WA/WP= 1.130, n(j)/m(j)= 2.500, dates= 910723 930511 910723, class=4, val= 5.12 920623, class=1, val= 0.21 930323, class=3, val= 1.97 930511, class=4, val= 2.84 940208, class=2, val= 1.24 950425, class=2, val= 1.02

9 50523, class=2, val= 1.41 950711, class=1, val= 0.15 B[1]= 1.474, B[2]= 1.186, B[3]= 1.286, B[4]= 1.286, rescaled Ar=23.702814, S=6.000000, N= 208, sum P(j)= 208 usedays=8, distance error=

- 0.010252, biased error=

- 0.224372

fin[2][1]: bin= 1, c lass=1, WA/WP= 2.743, n(j)/m(j)=13.000, dates= 930327 bin= 2, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 4, class=2, WA/WP= 1.156, n(j)/m(j)=11.000, dates= 950426 bin= 5, class=3, WA/WP= 0.0 00, n(j)/m(j)= 0.000, dates= bin= 6, class=3, WA/WP= 1.894, n(j)/m(j)=16.000, dates= 950429 bin= 7, class=2, WA/WP= 1.872, n(j)/m(j)=50.000, dates= 930324 bin= 8, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 9, class=1, WA/WP= 1.904, n(j)/m(j)=27.0 00, dates= 940209 bin=10, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=11, class=5, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, class=5, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=13, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=14, class= 5, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=3, WA/WP= 1.445, n(j)/m(j)=16.000, dates= 930515 bin=16, class=4, WA/WP= 1.404, n(j)/m(j)= 9.000, dates= 920624 930811 950527 950712 bin=17, class=3, WA/WP= 1.521, n(j)/m(j)=10.000, dates= 930804 9308 07

### 395

bin=18, class=5, WA/WP= 1.000, n(j)/m(j)= 1.000, dates= 910731 bin=19, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=20, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=21, class=5, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=22, class=5, WA/WP = 1.031, n(j)/m(j)= 3.500, dates= 910727 950715 bin=23, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=24, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=25, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=26, class=5, WA/WP= 0.000, n(j)/m(j) = 0.000, dates= 910727, class=5, val= 26605.00 910731, class=5, val= 31824.00 920624, class=4, val= 20918.00 930324, class=2, val= 8223.00 930327, class=1, val= 4505.00 930515, class=3, val= 15464.00 930804, class=3, val= 10029.00 930807, class=3, val= 9338.00 930811, class=4, val= 19024.00 940209, class=1, val= 3919.00 950426, class=2, val= 6538.00 950429, class=3, val= 9766.00 950527, class=4, val= 17516.00 950712, class=4, val= 17785.00 950715, class=5, val= 25882.00

B [1]= 1.263, B[2]= 1.223, B[3]= 1.311, B[4]= 1.383, B[5]= 1.667, rescaled Ar=885440.500000, S=5.000000, N= 414, sum P(j)= 414 usedays=15, distance error=

- 0.007992, biased error=

- 0.252793

fin[2][2]: bin= 1, class=2, WA/WP= 1.736, n(j)/m(j)=21.333, dates= 950426 950429 950503 bin= 2, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 4, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 5, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 6, cla ss=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 7, class=3, WA/WP= 1.174, n(j)/m(j)=10.000, dates= 950527 bin= 8, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 9, class=2, WA/WP= 1.000, n(j)/m(j)= 5.000, dates= 930324 bin=10, class=3, WA/WP= 0.000 , n(j)/m(j)= 0.000, dates= bin=11, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, class=1, WA/WP= 2.805, n(j)/m(j)=18.000, dates= 930331 940209 bin=13, class=4, WA/WP= 1.609, n(j)/m(j)= 7.000, dates= 930512 930807 bin=14, class=3, WA/WP= 1.246, n( j)/m(j)=13.000, dates= 930515

### 396

bin=15, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=16, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=17, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=18, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates=

bi n=19, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=20, class=5, WA/WP= 1.568, n(j)/m(j)= 7.000, dates= 950712 950715 bin=21, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=22, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=23, class=4, WA/

WP= 1.392, n(j)/m(j)= 5.000, dates= 930811 bin=24, class=4, WA/WP= 1.613, n(j)/m(j)= 6.000, dates= 910731 bin=25, class=4, WA/WP= 1.000, n(j)/m(j)= 3.500, dates= 920624 920627 bin=26, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=27, class=3, WA/WP= 1.414, n(j)/m(j)= 7.500, dates= 930804 950719 bin=28, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=29, class=4, WA/WP= 1.157, n(j)/m(j)= 8.000, dates= 910724 950524 910724, class=4, val= 16610.00 910731, class=4, val= 23289.00 920624, class=4, val= 16664.00 920627, class=4, val= 18077.00 930324, class=2, val= 5580.00 930331, class=1, val= 3892.00 930512, class=4, val= 23834.00 930515, class=3, val= 9576.00 930804, class=3, val= 13994.00 930807, class=4, val= 16282.00 930811, c lass=4, val= 18647.00 940209, class=1, val= 3715.00 950426, class=2, val= 5548.00 950429, class=2, val= 9031.00 950503, class=2, val= 6745.00 950524, class=4, val= 16746.00 950527, class=3, val= 11944.00 950712, class=5, val= 26001.00

95 0715, class=5, val= 38917.00 950719, class=3, val= 12226.00 B[1]= 1.900, B[2]= 1.467, B[3]= 1.490, B[4]= 1.215, B[5]= 1.000, rescaled Ar=833232.812500, S=6.000000, N= 467, sum P(j)= 467 usedays=20, distance error=0.004414, biased error=

- 0.251605

fin [3][1]: bin= 1, class=1, WA/WP= 1.138, n(j)/m(j)=94.800, dates= 930513 930806 950501 950502 950529 bin= 2, class=2, WA/WP= 1.882, n(j)/m(j)= 7.000, dates= 930808 950503 950527 bin= 3, class=1, WA/WP= 1.349, n(j)/m(j)=30.000, dates= 910728 930807

### 397

bin= 4, cl ass=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 5, class=2, WA/WP= 1.623, n(j)/m(j)=15.750, dates= 930515 930805 930811 950426 bin= 6, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 7, class=2, WA/WP= 1.207, n(j)/m(j)= 5.333, dates= 910727 910731 930809 bin= 8, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 9, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=2, WA/WP= 1.162, n(j)/m(j)= 6.000, dates= 930511 930516 bin=11, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, cla ss=3, WA/WP= 1.494, n(j)/m(j)= 2.500, dates= 950429 950525 bin=13, class=2, WA/WP= 1.442, n(j)/m(j)= 3.000, dates= 930517 bin=14, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=16, class=4, WA/WP = 1.081, n(j)/m(j)= 9.000, dates= 920624 bin=17, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=18, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=19, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=20, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000 , dates= bin=21, class=1, WA/WP= 1.263, n(j)/m(j)=23.000, dates= 920626 bin=22, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=23, class=3, WA/WP= 1.056, n(j)/m(j)= 8.000, dates= 910726 bin=24, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=25, c lass=3, WA/WP= 1.473, n(j)/m(j)= 5.000, dates= 920628 950524 950712 950713 bin=26, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=27, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=28, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=29, class =3, WA/WP= 1.438, n(j)/m(j)= 1.500, dates= 950526 950715 bin=30, class=3, WA/WP= 1.000, n(j)/m(j)= 3.000, dates= 950711 bin=31, class=4, WA/WP= 1.281, n(j)/m(j)=10.500, dates= 950714 950719 bin=32, class=3, WA/WP= 1.327, n(j)/m(j)= 4.000, dates= 950717

910 726, class=3, val= 871.80 910727, class=2, val= 499.30 910728, class=1, val= 243.40 910731, class=2, val= 620.70 920624, class=4, val= 1396.50 920626, class=1, val= 83.30 920628, class=3, val= 763.80 930511, class=2, val= 63 8.30 930513, class=1, val= 89.00 930515, class=2, val= 488.60 930516, class=2, val= 551.90 930517, class=2, val= 703.60 930805, class=2, val= 385.80 930806, class=1, val= 104.20 930807, class=1, val= 290.80

### 398

930808, class=2, va l= 558.20 930809, class=2, val= 549.60 930811, class=2, val= 568.50 950426, class=2, val= 540.60 950429, class=3, val= 807.60 950501, class=1, val= 168.60 950502, class=1, val= 78.50 950503, class=2, val= 548.40 950524, cla ss=3, val= 820.40 950525, class=3, val= 771.70 950526, class=3, val= 767.10 950527, class=2, val= 658.40 950529, class=1, val= 87.90 950711, class=3, val= 1017.00 950712, class=3, val= 874.60 950713, class=3, val= 1086.40

9507 14, class=4, val= 1351.10 950715, class=3, val= 1012.20 950717, class=3, val= 824.20 950719, class=4, val= 1161.20 B[1]= 1.000, B[2]= 1.217, B[3]= 1.747, B[4]= 1.178, B[5]=0, rescaled Ar=68429.140625, S=5.000000, N=1052, sum P(j)=1052 usedays =35, distance error=

- 0.005638, biased error=

- 0.200580

fin[3][2]: bin= 1, class=1, WA/WP= 1.089, n(j)/m(j)=52.875, dates= 910730 930513 930806 950501 950502

950503 950528 950529 bin= 2, class=1, WA/WP= 1.452, n(j)/m(j)=37.000, dates= 950428 bin= 3, class= 2, WA/WP= 1.356, n(j)/m(j)=23.000, dates= 950429 bin= 4, class=1, WA/WP= 1.134, n(j)/m(j)=53.000, dates= 920625 930805 bin= 5, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 6, class=4, WA/WP= 1.477, n(j)/m(j)= 2.000, dates= 950427 bin= 7, class=2, W A/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 8, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 9, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=3, WA/WP= 1.497, n(j)/m(j)= 9.333, dates= 930512 950524 950711 bin=11, class=2, WA/WP= 0.000 , n(j)/m(j)= 0.000, dates= bin=12, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=13, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=14, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=2, WA/WP= 1.577, n(j)/m(j)=19.000, dates= 95071 9 bin=16, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=17, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates=

### 399

bin=18, class=2, WA/WP= 1.688, n(j)/m(j)= 5.818, dates= 910726 910728 910731 920624 920626

920628 930809 930810 930811 950525 950718 bin=19, c lass=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=20, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=21, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=22, class=3, WA/WP= 1.323, n(j)/m(j)=12.000, dates= 920629 bin=23, class=2, WA/WP= 1.379, n(j )/m(j)=12.000, dates= 950717 bin=24, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=25, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=26, class=4, WA/WP= 1.013, n(j)/m(j)= 5.000, dates= 950713 bin=27, class=3, WA/WP= 1.554, n(j)/m(j)= 3.000, dat es= 950712 bin=28, class=4, WA/WP= 1.181, n(j)/m(j)= 7.000, dates= 950715 bin=29, class=3, WA/WP= 1.174, n(j)/m(j)= 1.000, dates= 930511 910723 bin=30, class=4, WA/WP= 1.108, n(j)/m(j)=13.000, dates= 950714 bin=31, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=32, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= 910723, class=3, val= 1143.10 910726, class=2, val= 658.90 910728, class=2, val= 737.80 910730, class=1, val= 62.80 910731, class=2, val= 638.50 920624, class=2, val= 39 1.50 920625, class=1, val= 224.50 920626, class=2, val= 547.00 920628, class=2, val= 449.00 920629, class=3, val= 1022.10 930511, class=3, val= 1019.60 930512, class=3, val= 958.40 930513, class=1, val= 72.40 930805, class=1, va l= 70.40 930806, class=1, val= 89.80 930809, class=2, val= 745.40 930810, class=2, val= 567.90 930811, class=2, val= 549.30 950427, class=4, val= 1167.80 950428, class=1, val= 250.60 950429, class=2, val= 428.70 950501, cla ss=1, val= 65.80 950502, class=1, val= 85.60 950503, class=1, val= 140.60 950524, class=3, val= 1121.60 950525, class=2, val= 620.00 950528, class=1, val= 72.00 950529, class=1, val= 58.10

### 400

950711, class=3, val= 935.80

9507 12, class=3, val= 901.70 950713, class=4, val= 1162.60 950714, class=4, val= 1456.60 950715, class=4, val= 1334.70 950717, class=2, val= 527.30 950718, class=2, val= 430.60 950719, class=2, val= 545.20 B[1]= 1.000, B[2]= 1.191, B[3 ]= 1.425, B[4]= 1.205, B[5]=0, rescaled Ar=66196.039062, S=5.000000, N=1066, sum P(j)=1066 usedays=36, distance error=0.001311, biased error=

- 0.166249

### 401

__________________________________________ Total Error (average unsigned) = 12.257393 Total Error (sq

rt sum squared) = 67.399037 Total Error (optimized cost) = 0.000000 Total Error (average biased) = 32.433480

__________________________________________

The following is the episode software output for Case 15 dry deposition only. The full 80 days wer e run through the software to get the correct outputs (similar to wet deposition).

Use all days = false fin[1][1]= snp121.forwt.dat fin[1][2]= gsm22.forwt.dat Day list file = case15f.out

Tolerance = 0.800000 actual days per data day for each set: 8 ,

Ob servation based metric [1][1] = 536.409973, # seasons = 6 Observation based metric [1][2] = 316.571991, # seasons = 5 Number of initial days input: 80 This represents 80 actual modeling days and 9 episodes. Of these days, 0.0% are in episodes of lengt h 2 or less.

910723

910724

910725

910726

910727

910728

910729

910730

910731

920623

920624

920625

920626

920627

920628

920629

920630

930323

930324

930325

930326

930327

### 402

930328

930329

930330

930331

930511

930512

930513

930514

930515

930516

930517

930518

9305 19

930803

930804

930805

930806

930807

930808

930809

930810

930811

930812

940208

940209

940210

940211

940212

940213

940214

940215

940216

950425

950426

950427

950428

950429

950430

950501

950502

950503

950523

950524

950525

### 403

950526

950527

950528

950529

950530

9 50711

950712

950713

950714

950715

950716

950717

950718

950719

ERRORS FOR EACH TREE WITH THIS SET OF DAYS:

fin[1][1]: bin= 1, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 2, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=1, WA/WP= 0 .000, n(j)/m(j)= 0.000, dates= bin= 4, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 5, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 6, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 7, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates=

b in= 8, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 9, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=11, class=1, WA/WP= 1.087, n(j)/m(j)=18.000, dates= 940208 bin=12, class=3, WA/WP= 0. 000, n(j)/m(j)= 0.000, dates= bin=13, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=14, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=16, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates=

bi n=17, class=1, WA/WP= 1.273, n(j)/m(j)=19.000, dates= 910723 930323 930803 950425

950523 950711 bin=18, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= 910723, class=1, val= 8.97 930323, class=1, val= 9.58 930803, class=1, val= 13.55

94020 8, class=1, val= 13.42 950425, class=1, val= 6.31 950523, class=1, val= 7.49 950711, class=1, val= 13.68

### 404

B[1]= 1.171, B[2]=0, B[3]=0, B[4]=0, rescaled Ar=455.885895, S=6.000000, N= 244, sum P(j)= 178 usedays=7, distance error=

- 0.1501 17, biased error=

- 0.470564

### 405

fin[1][2]: bin= 1, class=4, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 2, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 3, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 4, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000 , dates= bin= 5, class=2, WA/WP= 1.000, n(j)/m(j)= 2.000, dates= 930511 bin= 6, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin= 7, class=1, WA/WP= 1.068, n(j)/m(j)=12.000, dates= 930323 940208 bin= 8, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates=

bi n= 9, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=10, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=11, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=12, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=13, class=4, WA/WP= 0.000, n(j )/m(j)= 0.000, dates= bin=14, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=15, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=16, class=1, WA/WP= 1.019, n(j)/m(j)=19.750, dates= 910723 920623 930803 950523 bin=17, class=2, WA/WP= 0.000, n(j)/m( j)= 0.000, dates= bin=18, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=19, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=20, class=3, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=21, class=2, WA/WP= 1.135, n(j)/m(j)= 6.000, dates= 950711 bin=22, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=23, class=1, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= bin=24, class=2, WA/WP= 0.000, n(j)/m(j)= 0.000, dates= 910723, class=1, val= 3.62 920623, class=1, val= 7.16 930323, class=1, val= 6 .73 930511, class=2, val= 9.65 930803, class=1, val= 6.02 940208, class=1, val= 3.41 950523, class=1, val= 6.17 950711, class=2, val= 8.14 B[1]= 1.314, B[2]= 5.667, B[3]=0, B[4]=0, rescaled Ar=287.421631, S=5.000000, N= 226, sum P(j)= 189 usedays=8, distance error=

- 0.092081, biased error=

- 0.578595

__________________________________________ Total Error (average unsigned) = 12.109898 Total Error (sqrt sum squared) = 17.610785 Total Error (optimized cost) = 0.000000

Total Erro r (average biased) = 52.457958

__________________________________________

### 406

11. APPENDIX 2:

EPISODE SELECTION PROCESS IN DETAIL



### 11.1 Running the Episode Selection Software

The episode selection software was run at least a hundred times to get the fina l list of SAMI episode days. There was a great deal of experimentation in learning how the software worked, as well as learning its strengths and weaknesses. The software generally did a good job of selecting a set of episode days that

could be used as a starting point. But it was clear that a great deal of “user intervention” would be required to get a final set of acceptable episode days.

The software was designed to allow the user to specify whether the priority should be given to minimization

of e rror or maximization of episode length. It is more efficient to run a small set of long episodes than a large set of short episodes. Long episodes minimize the total number of ramp

- up days needed and it is generally easier to create meteorological inputs for long continuous time periods. Therefore the episode length was the primary consideration. It was envisioned that somewhere between 5 and 10 week long episodes would be selected.

It was noted earlier that the selection of episode days is limited by the availability of ambient data. The acid deposition data consists of 8 day periods and the ozone and visibility data are single day periods. The ozone data is available every day during the April

- October growing season and the visibility data is only

av ailable on Wednesdays and Saturdays.

Initial runs of the episode software produced a number of 2

- 4 day ozone and visibility episodes and a number of 8 day acid deposition episodes. The goal of the episode selection was to minimize the total number of epi

sodes and days, and to minimize the unscaled error at the same time. It was quickly realized that the most efficient way to select a limited number of episodes would be to select ozone and visibility episodes that were embedded within a series of 8 day ac id deposition episodes. Essentially, a full 8 day period 8 had to be selected to represent each acid deposition episode. If most (or all) of the ozone and visibility days fell within the same 8 day periods then shorter episodes could be eliminated.

Since we wanted the software to select mostly 8 day episode periods, acid deposition was made first in the user selected “pollutant order”. The software was run many times with different combinations of pollutant order and park order. It was found to be impo rtant for acid deposition to be first. Visibility was selected second and ozone third. The order of the parks was not found to be important. Great Smokies was selected first. The complete order is as follows:

8 After the episodes were selected. several days at the beginning and end of acid deposition episodes were eliminated if they were non

- rain days since the episode classification was based on wet deposition. The eliminated days were also screened to make sure they were not ozone or visibility days. A total of 11 days out of 80 were
removed as non

- rain days.

### 407

1) Acid deposition

- GSM 2) Acid deposition

- SNP 3) Visibility

- GSM 4) Visibility

- SNP 5) Ozone

- GSM 6) Ozone

- SNP

The episode software can be run in 2 modes. In the first mode the user specifies the pollutant and park order, the tolerance 9 , the default number of days to select, and a distance and cost factor 10

. The software returns a full list of episode days that is “optimized” across the 3 pollutants and 2 parks (based on the user specified inputs). The software calculates the errors associated with the episode days and provides the scaling factors necessary t

o recreate the annual and /or seasonal averages. In the second mode, the user specifies a complete set of episode days and the software simply calculates the errors and the scaling factors. The first mode can be used to select an initial “optimized” set of days and then the second can be used to hand edit the episode days and recalculate the errors.

11.2 SAMI Episode Selection

Round 1

The episode selection process began around the same time that GA Tech was selected as the main SAMI modeling contractor . When GA Tech started their initial testing of the URM

- 1ATM model they needed to prepare input data for at least one episode to use as a test case. In some of the initial testing of the episode software, the “July 1995 “ episode (approximately July 10 th



### - 15 th 

### ) was frequently selected as both a high ozone and high (poor) visibility episode. Since this was likely to be good episode for SAMI to use and since it had also been an OTAG episode, SAMI decided to choose July 10 th



### - 15 th 

### 11 as its first episode.

Th e episode selection was run several times to let the software select the episode days. It was found that several 2 or 3 day episodes were almost always selected. The short episodes often contained days representing bins with high weights for one or more pollutant. The high weight days often represent a significant fraction of the annual or seasonal metric, but selecting those days would make it difficult to maintain the goal of modeling long episodes. Therefore it was important to attempt to replace thes e days with alternative days from the same bin which may fall within an 8 day acid deposition episode.

9 Tolerance is the allowed distance from the bin mean for a day to be able to be selected. SAMI used of tolerance of 0.80 (80%)

. 10 A larger distance factor minimizes the error and a larger chain factor minimizes the number of short episodes. SAMI set both factors to 1 which equally weights for error and episode length.

11 The July ’95 episode was later extended to July 11 th

- 19 th (plus ramp

- up days) in order to make it an acid deposition episode and to include July 17, 18, and 19 as ozone days and July 19 th as a visibility day.

### 408

As expected, the unscaled error was well correlated with the number of days selected. As part of the final round of episode selection, different case s (set of days) were developed which began with 48, 56, and 70 episode days. These cases were hand edited in different ways to become possible episode selection

candidates.

The initial round of episode selection contained cases ranging from 8

- 10 episodes and 46

- 71 episodes days. The following is a summary of the first six cases that were considered by SAMI. The tables contain the number of days and episodes in each case; the biased (unscaled) and scaled errors for each pollutant and park; and the number

of properly classified days for each pollutant and park, sorted by class.

### 409

Table 4

- 8

Ozone Ozone Annual Vis. Annual Vis. Acid Dep. Acid Dep.

GSM SNP GSM SNP GSM SNP

Case 1 46 days biased error %

- 43.3

- 45.1

- 43.1

- 42.5

- 47.7

- 56.3 8 episodes scaled error %

- 4.7

- 14.2 5.2

- 0.2

- 8.5

- 23.1 class 1 days 5 7 0 1 1 1 Started with 48 days class 2 days 5 1 2 2 2 0 class 3 days 4 5 4 3 1 2 class 4 days 4 3 2 1 2 4 class 5 days N/A N/A 1 2 N/A N/A Total Days 18 16 9 9 6 7

Case 2 52 days biased error %

- 38.2

- 33.6

- 36.5

- 55.1

- 61.5

- 24.4 8 episodes scaled error %

- 8.2

- 0.5 8.5 2.4

- 20.9 7.6

class 1 6 5 0 1 1 2 Started with 56 days class 2 7 3 1 3 2 1

class 3 6 5 5 3 0 1

class 4 1 4 6 1 1 1

class 5 N/A N/A 1 3 N/A N/A

Tot al Days 20 17 13 11 4 5

Case 3 59 days biased error %

- 34.3

- 25.1

- 28.9

- 49.3

- 50.4

- 12.5 9 episodes scaled error %

- 6.5 0.9 10.3 9.6

- 9.2 3.9

Derived from case 2 class 1 6 5 0 2 1 2

class 2 8 6 1 3 2 2

class 3 6 5 6 3 1 1

class 4 2 5 6 2 1 1

class 5 N/A N/A 1 3 N/A N/A Total Days 22 21 14 13 5 6

Case 4 67 days biased error %

- 19.1

- 24.3

- 28.9

- 31.9

- 47.3

- 27.9 10 episodes scaled error % 0.9 1.4 0.1

- 6.6 13.5 2.1

class 1 9 14 3 2 1 2 Started with 70 days class 2 11 8 2 1 2 2

cla ss 3 11 6 4 4 3 1

class 4 3 4 6 8 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 34 32 16 17 7 6

Case 5 66 days biased error %

- 23.7

- 16

- 33.7

- 33

- 47.3

- 29.5 9 episodes scaled error %

- 1.9 2.1

- 0.8

- 8.2 13.5 0.5

class 1 9 13 3 2 1 3

Derived fr om case 4 class 2 10 11 2 1 2 2

class 3 9 6 4 5 3 1

class 4 3 3 4 6 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 31 33 14 16 7 7

Case 6 71 days biased error %

- 18.3

- 17.2

- 28.9

- 32.2

- 47.3

- 29.5 10 episodes scaled error % 0.4 0.9 0.1

- 7 13.5 0 .5

class 1 9 14 3 2 1 3

Derived from case 5 class 2 11 11 2 1 2 2

class 3 12 7 4 5 3 1

class 4 4 4 6 8 1 1

class 5 N/A N/A 1 2 N/A N/A

### 410 Total Days 36 36 16 18 7 7



### Case 1 started with 48 days selected by the software. A short 2 day episode was removed to arrive at the final 46 day total. It can be seen from cases 1

- 3 that the biased errors for ozone were generally in the

- 30

- 45% range; the biased err ors for visibility ranged from

- 30

- 55% and the biased errors for acid deposition

were in th e

– 40

- 60% range. It was not known if these errors were “acceptable”. But by increasing the number of episode days, it was shown that the errors could be reduced considerably. Cases 4

- 6 increased the starting number of episode days to 70. With 9 or 10 e pisodes and 67

- 71 days, the ozone errors were

reduced to around

– 20%; the visibility errors were reduced to around

– 30%; and the acid deposition errors

were reduced to

– 30% at SNP and

– 50% at GSM.

Round 2

The next 3 cases represented the second set of p ossible episodes. Case 7 was derived from Case 1 and Cases 8 and 9 were both derived from Case 5. Case 5 contained fewer days and episodes than Cases 4 and 6. Therefore it became the default set of episode days. The objective of Cases 8 and 9

was to re duce the number of episodes and days even further without increasing the errors.

Table 4

- 9

Ozone Ozone Annual Vis. Annual Vis. Acid Dep. Acid Dep.

GSM SNP GSM SNP GSM SNP

Case 7 42 days biased error %

- 60.9

- 59.9

- 45.8

- 47.1

- 56.2

- 66.3 6 episod es scaled error %

- 35.6

- 27.6 9.1

- 5.4 42.1 99.9

class 1 5 8 0 1 0 0

Derived from case 1 class 2 6 1 1 3 2 0

class 3 0 3 4 3 1 2

class 4 0 0 1 1 2 1

class 5 N/A N/A 1 1 N/A N/A Total Days 11 12 7 9 5 3

Case 8 63 days b iased error %

- 24.6

- 19.9

- 32.9

- 33.6

- 55.3

- 29.7 8 episodes scaled error %

- 2.8

- 0.2 0.9

- 6.9

- 16.9 0.3

class 1 7 10 4 3 1 3

Derived from case 5 class 2 9 11 1 2 3 2

class 3 9 6 4 4 2 1

class 4 3 3 4 6 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 28 30 14 17 7 7

Case 9 62 Days biased error %

- 24.7

- 15.5

- 26.2

- 30.9

- 45.2

- 16.8 8 Episodes scaled error %

- 2.8 1.1

- 1.4

- 3

- 8.7

- 1

class 1 8 13 2 1 2 3

Derived from case 5 class 2 11 12 2 4 2 2

class 3 10 6 5 4 2 1

class 4 3 4 4 6 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 32 35 14 17 7 7

### 411

### 412

It can be seen in Case 7 that decreasing the number of episodes to 6 with only 42 episode days, drastically increases the biased errors, especially for ozone an d visibility. Cases 8 and 9 show that the number of episodes from Case 5 can be decreased from 9 to 8 without a large increase in error. In fact, in some cases the errors in Cases 8 and 9 are even lower than Case 5. Since the errors in Case 9 were slight ly lower than Case 8 (and there was one less day), Case 9 was proposed as the final set of modeling days.

Round 3

At this point in the process Case 9 had been established as a “benchmark” set of episode days. The

associated biased and scaled errors were reasonable compared to other selected sets of episode days. Soon after this leg of the episode selection process was completed, several groups within SAMI wanted to explore the possibility of reducing the number of episodes and episode days. It was unkno wn whether SAMI’s schedule and budget would support modeling 8 episodes and 62 episode days.

Cases 9b, 9c, and 9d were derived from Case 9 in an effort to illustrate the effects of reducing the number of episodes. As was expected, in all 3 cases, the bia sed errors increased. By limiting the number of episode days to less than 50, it was not possible to sufficiently reduce the errors to a level that satisfied the modeling subcommittee. It was therefore argued that more episodes and episode days were need ed in order to effectively represent the seasonal and annual metrics for all three 3 pollutants. The episode selection process continued.

### 413

Table 4

- 10

Ozone Ozone Annual Vis. Annual Vis. Acid Dep. Acid Dep.

GSM SNP GSM SNP GSM SNP

Case 9b 48 Day s biased error %

- 28.2

- 31.5

- 35.5

- 47.8

- 64.9

- 30.4 6 Episodes scaled error % 3.5

- 4.9 0.1

- 9.6

- 12.4 0.8

class 1 7 9 2 1 1 2

Derived from case 9 class 2 9 8 1 1 1 1

class 3 8 5 4 4 2 1

Removed 950425 class 4 2 3 3 4 1 1

and 920623 c lass 5 N/A N/A 1 2 N/A N/A Total Days 26 25 11 12 5 5

Case 9c 45 Days biased error %

- 34.1

- 21.8

- 34.7

- 46.6

- 56.2

- 28.8 8 Episodes scaled error %

- 7.2

- 2.5 1.3

- 4.6

- 18.7 2.4

class 1 5 9 2 1 1 2

Derived from case 9 class 2 5 7 1 1 2 1

class 3 9 6 2 2 1 1

Removed 950425 class 4 3 3 3 4 1 1

and 930803 class 5 N/A N/A 1 2 N/A N/A Total Days 22 25 9 10 5 5

Case 9d 47 Days biased error %

- 37.8

- 29.2

- 27.4

- 33.2

- 65.6

- 18.3 6 Episodes scaled error %

- 8

- 4.7

- 3. 1 2

- 13.7

- 2.5

class 1 6 13 2 1 1 2

Derived from case 9 class 2 7 12 2 4 1 2

class 3 9 6 3 2 1 1

Removed 930803 class 4 2 4 2 2 1 1

and 920623 class 5 N/A N/A 1 2 N/A N/A Total Days 24 35 10 11 4 6



### Round 4

At this point, Case 9 wa s still the best combination of episode days selected to date. Both the number of days and magnitude of errors were reasonable. The biggest weakness of Case 9 was its limited representation of class 1 visibility days (cleanest 20% of days). This was imp ortant for several reasons. First, the committee wanted to be able to adequately represent the full range of conditions from clean to dirty for all pollutants. They wanted to be sure that the future change in air quality could be quantified on clean days as well as dirty days (since the goal was to capture the annual average visibility). Second, the proposed regional haze rules required a demonstration that visibility does not degrade on the cleanest 20% of visibility days at each Class 1 area.

### 414

In Ca se 9 there was only one class 1 visibility day at SNP and two class 1 day at GSM. Therefore, in an effort to increase the number of class 1 visibility days, it was decided to add a ninth episode which contained relatively clean days. A February ’94 episo de (940208) was selected which contained two (properly classified) class 1 days in each park. The episode could also be extended to be 8 days long to pick up an additional class 2 acid deposition episode for each park. Case 10 shows the errors associated with adding the period from 940209

- 940212. This covers the visibility days only (940209 and 940212). The errors barely change compared to Case 9 because the new clean episode days represent days with small weights. But the number of class 1 visibility days increases to 4 at GSM and 3 at SNP. Case 11 adds a couple more days to make the new episode both a visibility and acid deposition episode. This brings the total number of episodes to 9 and the number of episode days to 68.

As an alternative, Case 1 2 adds another summer episode (930724) instead of the winter ’94 episode. The summer episode has a larger effect on error, but doesn’t add any other clean days. Cases 13 and 14 add both the winter and summer episodes. While the errors are improved slight ly compared to Case 11, the modeling committee decided that the number of episodes needed to be limited 9. Case 11 was deemed to be the most efficient combination of episodes and errors. The SAMI operations committee subsequently approved the decision to model 9 episodes (and 68 episode days).

### 415 Table 4

- 11

Ozone Ozone Annual Vis. Annual Vis. Acid Dep. Acid Dep.

GSM SNP GSM SNP GSM SNP

Case 11 68 Days biased error %

- 24.7

- 15.5

- 24.4

- 29.5

- 45

- 17.6 9 Episodes scaled error %

- 2.8 1.1

- 1.6

- 0.2

- 8.2

- 2

class 1 8 13 4 3 2 3

Derived from case 9 class 2 11 12 2 4 3 3 (Add 940208

- 940213) class 3 10 6 5 4 2 1

VIs. and Acid class 4 3 4 4 6 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 32 35 16 19 8 8

Case 12 67 Days bias ed error %

- 19.5

- 16.7

- 21.4

- 28.6

- 45.2

- 16.8 9 Episodes scaled error %

- 0.6 0

- 0.4 0.8

- 8.7

- 1

class 1 8 14 2 1 2 3

Derived from case 9 class 2 12 12 2 4 2 2 (Add 930724

- 930728 ) class 3 13 7 5 4 2 1

ozone and vis. class 4 4 5 6 8 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 37 38 16 19 7 7

Case 13 71 Days biased error %

- 19.5

- 16.7

- 19.6

- 30.1

- 45.2

- 16.8 10 Episodes scaled error %

- 0.6 0

- 0.6

- 1.8

- 8.7

- 1

class 1 8 14 4 3 2 3

Derived from case 9 class 2 1 2 12 2 4 2 2 (Add 930724

- 930728 class 3 13 7 5 4 2 1

and 940209

- 940212) class 4 4 5 6 8 1 1

ozone and vis. class 5 N/A N/A 1 2 N/A N/A Total Days 37 38 18 21 7 7

Case 14 73 Days biased error %

- 19.5

- 16.7

- 19.6

- 30.1

- 45

- 17.6 10 Episodes scaled error %

- 0.6 0

- 0.6

- 1.8

- 8.2

- 2

class 1 8 14 4 3 2 3

Derived from case 9 class 2 12 12 2 4 3 3 (Add 930724

- 930728 class 3 13 7 5 4 2 1

and 940208

- 940213) class 4 4 5 6 8 1 1 ozone, vis., and acid class 5 N/A N/A 1 2 N/A N/A

To tal Days 37 38 18 21 8 8

Round 5



### Naturally, the episodes were selected before the meteorological modeling could take place. The episode selection process assumed that the meteorological and air quality models would be able to successfully reproduce the historical episodes. The meteorology associated with one of the episodes in Case 11 proved to be particularly difficult. The 940405 episode was a class 4 acid deposition for GSM and represented a

### 416

significant fraction of the annual deposition (it had a h igh weight). There was a large amount of observed precipitation at GSM during the episode. RAMS was not able to adequately reproduce the observed precipitation. It was therefore decided to drop this episode in favor of another class 4 acid deposition

ep isode at GSM.

Several other GSM class 4 episodes were identified. Cases 15 and 16 show the errors when the 940405 episode is replaced by the 910723 and 91806 episodes respectively. The overall error in Case 15 is generally lower than either Case 16 or t he original Case 11. Additionally, 910723 is a class 4 acid deposition episode at SNP and contains several additional visibility and ozone days at each park. he subcommittee decided to use the 910723 episode as the replacement. Case 15 became the final set of episode days with 9 episodes and 69 days 12

.

Table 4

- 12

Ozone Ozone Annual Vis. Annual Vis. Acid Dep. Acid Dep.

GSM SNP GSM SNP GSM SNP

Case 15 69 Days biased error %

- 20

- 16.6

- 23.6

- 26.4

- 37.1

- 22.4 9 Episodes scaled error %

- 0.5 0.1

- 1. 2

- 1.9 4.5

- 1

class 1 8 11 3 3 2 3

Derived from case 11 class 2 13 14 2 4 3 3

deleted 940405 and class 3 11 7 4 4 2 1

added 910723 class 4 3 4 4 8 1 2

class 5 N/A N/A 3 2 N/A N/A Total Days 35 36 16 21 8 9

Case 16 68 Days b iased error %

- 20.5

- 17.6

- 23.9

- 30.7

- 39.6

- 26.9 9 Episodes scaled error %

- 0.9

- 1.1

- 1

- 2.7 0.4

- 6.8

class 1 9 12 3 3 2 2

Derived from case 11 class 2 11 11 2 4 3 3

deleted 940405 and class 3 12 6 5 4 2 1

added 910806 class 4 3 4 4 8 1 1

class 5 N/A N/A 1 2 N/A N/A Total Days 35 33 15 21 8 7



### The Final Round

Following the approval of the Case 15 episode days, one more addition was made to the episode selection process. In the original development of the CART database, only wet d eposition measurements were used to characterize acid deposition. After the episode selection was completed (and the base case modeling was well on its way), the modeling subcommittee decided to look at estimating the annual total for dry deposition. SAI

was hired to run CART again for GSM and SNP using the same meteorological information as the original classification. The details of the analysis can be found in (Hudischewskyj, 2000).

12 The subsequent air quality model performance for visibility for day 940212 was judged to be deficient. This day was later thrown out of the episode selection statistics for visibility for both parks, but the total number of modeled days is still 69.

### 417

Since the 69 episode days were already selected, SAMI needed to de termine how the already selected episodes would be classified for dry deposition. Since the wet deposition episodes were heavily influenced by the amount of precipitation at the Class I areas, it was no surprise that dry deposition during most of the

sele cted episodes was low. At GSM there were 6 class 1 dry deposition episodes and 2 class 2 episodes.

At SNP all 7 selected

episodes fell into class 1 bin . Due to the under

- representation of the medium and high dry deposition periods, the unscaled dry depo sition errors are relatively high. For the same reason the uncertainty associated with the dry deposition weightings is likely to be higher than for the other pollutants.

An additional source of uncertainty results from the removal of the 11 “non

- rain” days from the original 80 day episode total. These days were removed at a time in the selection process when only wet deposition was being considered. It was thought to be reasonable to remove these days (at the beginning and end of episode period) bec ause wet deposition cannot occur on days without precipitation. But dry deposition obviously occurs on non

- rain days. In fact, dry deposition is likely to be highest on those days. It was too late in the process to add the non

- rain days back into the ep isodes. The following table summarizes the dry deposition results.

Table 4

- 13 Dry Dep. Dry Dep.

GSM SNP

Case 15 69 Days biased error %

- 57.8

- 47.0 9 Episodes scaled error %

- 9.2

- 15.0

class 1 6 7

class 2 2 0

class 3 0 0

class 4 0 0

class 5 N/A N/A Total Days 8 7

### 418

12. APPENDIX 3:
DETAILED COMPARISONS WITH OTHER PROGNOSTIC MODEL EVALUATION
STUDIES

Table 10

- 1 and Figure 10

- 1 summarize statistically and graphically the performance evaluation results of nearly thirty (30) MM 5 and RAMS model applications over the past five years. Results for 12 km model applications are listed since this the scale most commonly reported in recent studies. Most of the studies, however, have also examined prognostic model skill at 36 km and 4 km scales; this information has also been compiled by AG.

Table 10

- 1 presents episode

- mean statistics for surface temperature, mixing ratio, and winds. While these statistics may be helpful in making general comparisons between studies and episodes, the calculation of an episode mean statistic often conceals important day

- to

- day and/or hour

- to

- hour variations that may be quite important in judging the adequacy of a meteorological or air quality model simulation.

### 419

Table 12

- 1. Summary of Prognostic Met eorological Model Performance Evaluations by Alpine Geophysics: 12 Km Grid Resolution Results. 

### (a) Surface Temperatures (deg C). Modeling Domain Model Study Ref Episode Mean Bias(deg C) Gross Error (deg C)

St. Louis MM5 Kansas/Missouri 12 11

- 24 June ‘9 5

- 0.3 1.6

St. Louis MM5 Kansas/Missouri 12 8

- 15 July ‘95 0.2 1.7

St. Louis MM5 Kansas/Missouri 12 14

- 21 Aug ‘98 2.0 2.3 Kansas City MM5 Kansas/Missouri 12 11

- 24 June ‘95

- 0.7 1.5 Kansas City MM5 Kansas/Missouri 12 8

- 15 July ‘95

- 0.8 1.7 Kansas City M M5 Kansas/Missouri 12 14

- 21 Aug ‘98 0.9 1.6

Texas MM5 COAST 11 4

- 11 Sept ‘93 0.2 1.8

Central Florida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99 0.1 1.5

Central Florida MM5 PFOS

- Episode 2 10 2

- 10 May ‘97 0.2 1.6

Central Florida MM5 PFOS

- Episode 3 10 25

- 30 Aug ‘97 0.2 1.7

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘99

- 0.4 1.3

Central Florida MM5 PFOS

- Episode 5 10 17

- 23 Sept ‘97 0.1 1.6

Central Florida MM5 PFOS

- Episode 9 10 20

- 28 Apr ‘98 0.3 1.3

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 0.8 2 .4

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90 0.2 2.9

Southeastern U.S. RAMS SAMI 7 3

- 12 Aug ‘93

- 0.4 1.6

Southeastern U.S. RAMS SAMI 7 22

- 29 Jun ‘92

- 1.1 1.8

Southeastern U.S. RAMS SAMI 7 24 Apr

- 3 May ‘91

- 0.8 1.8 Upper Midwest RAMS CRC

- LMO`S 6 26

- 28 Jun ‘91 0.1 1.4 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91

- 0.0 1.9 Upper Midwest MM5 CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.5 1.6 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91

- 0.3 1.7

Eastern U.S. RAMS OTAG 3 13

- 21 Jul ‘91 1.6 2.1

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘ 91

- 0.1 2.0

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88

- 0.6 3.3

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95

- 0.2 2.0

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94

- 0.7 2.4

Southeastern U.S. MM5 BAMP 9 6

- 11 Sep ‘93

- 0.4 2.1

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93

- 0.3 2.4

Mean Value

- 0.0 1.9

### 420

Table 12

- 1. Summary of Prognostic Meteorological Model Performance Evaluations by Alpine Geophysics: 12 Km Grid Resolution Results. (a) Surface Mixing Ratio (gm/Kg).

Modeling Domain Model Study Ref Episode M ean Bias (gm/Kg) Gross Error

(gm/Kg)

St. Louis MM5 Kansas/Missouri 12 11

- 24 June ‘95

- 0.9 1.3

St. Louis MM5 Kansas/Missouri 12 8

- 15 July ‘95

- 0.6 1.6

St. Louis MM5 Kansas/Missouri 12 14

- 21 Aug ‘98 2.4 2.6 Kansas City MM5 Kansas/Missouri 12 11

- 24 June ‘ 95

- 0.5 1.2 Kansas City MM5 Kansas/Missouri 12 8

- 15 July ‘95 0.2 1.5 Kansas City MM5 Kansas/Missouri 12 14

- 21 Aug ‘98

- 2.3 2.5

Texas MM5 COAST 11 4

- 11 Sept ‘93 0.1 1.4

Central Florida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99

- 0.1 1.2

Central Florida MM5 PF OS

- Episode 2 10 2

- 10 May ‘97 0.1 1.2

Central Florida MM5 PFOS

- Episode 3 10 25

- 30 Aug ‘97

- 2.0 2.3

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘99 0.8 1.5

Central Florida MM5 PFOS

- Episode 9 10 17

- 23 Sept ‘97

- 0.4 1.6

Central Florida MM5 PFOS

- 10 20

- 28 Apr ‘98

- 0.2 0.9

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 0.2 2.2

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90

- 0.2 1.9

Southeastern U.S. RAMS SAMI 7 3

- 12 Aug ‘93

- 0.6 1.1

Southeastern U.S. RAMS SAMI 7 22

- 29 Jun ‘92

- 0.3 1.0

Southeastern U.S. RAMS SAMI 7 24 Apr

- 3 May ‘91

- 0.1 0.7 Upper Midwest RAMS CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.1 1.2 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91 0.4 1.4 Upper Midwest MM5 CRC

- LMOS 6 26

- 28 Jun ‘91

- 0.1 1.2 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91

- 0.6 1.5

East ern U.S. RAMS OTAG 3 13

- 21 Jul ‘91

- 0.0 1.2

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘91

- 0.3 1.4

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88

- 1.4 2.0

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95

- 1.5 2.2

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94

- 1.6 2.2

Southeaste rn U.S. MM5 BAMP 9 6

- 11 Sep ‘93

- 0.6 1.0

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93

- 1.5 1.9

Mean Value

- 0.4 1.5

### 421 Table 12

- 1. Summary of Prognostic Meteorological Model Performance Evaluations by Alpine Geophysics: 12 Km Grid Resolution Results. (a)

Surface Wind Speeds

Modeling Domain

Model Study Ref Episode Average Error (%) RMSE

(ms - 1 ) Index of

Agreement Mean

Wind Dir.
Diff. (deg)

St. Louis MM5 Kansas/Missouri 12 11

- 24 June ‘95 31.6 1.88 0.48 20

St. Louis MM5 Kansas/Missour i 12 8

- 15 July ‘95 10.3 1.86 0.41 1

St. Louis MM5 Kansas/Missouri 12 14

- 21 Aug ‘98 47.5 1.83 0.45 4 Kansas City MM5 Kansas/Missouri 12 11

- 24 June ‘95 38.9 2.03 0.45 2 Kansas City MM5 Kansas/Missouri 12 8

- 15 July ‘95 28.5 1.97 0.47 12 Kansas City MM5 Ka nsas/Missouri 12 14

- 21 Aug ‘98 45.1 1.90 0.47 0

Texas MM5 COAST 11 4

- 11 Sept ‘93 61.4 2.20 0.69 15.0

Central Florida MM5 PFOS

- Episode 1 10 16

- 24 Apr ‘99 20.9 1.94 0.78 10.0

Central Florida MM5 PFOS

- Episode 2 10 2

- 10 May ‘97 21.0 1.95 0.78 32.0

Central Florida MM5 PFOS

- Episode 3 10 25

- 30 Aug ‘97 30.6 1.86 0.73 32.0

Central Florida MM5 PFOS

- Episode 4 10 4

- 10 April ‘97 18.1 1.80 0.80 8.0

Central Florida MM5 PFOS

- Episode 5 10 17

- 23 Sept ‘97 27.9 1.84 0.72 9.0

Central Florida MM5 PFOS

- Episode 9 10 20

- 28 A pr ‘98 24.0 1.79 0.78 26.4

Eastern U.S. MM5 Pittsburgh SIP 1 31 July

- 2 Aug ‘95 12.6 1.78 0.75 8.0

Western U.S. MM5 SARMAP 4 3

- 6 Aug ‘90 22.6 2.13 0.80 3.0

Southeastern U.S. RAMS SAMI 7 3

- 12 Aug ‘93 139.6 2.18 0.75 25.6

Southeastern U.S. RAMS SAMI 7 22

- 29 Jun ‘92 65.6 1.89 0.75 20.6

Southeastern U.S. RAMS SAMI 7 24 Apr

- 3 May ‘91 60.2 2.35 0.81 4.4 Upper Midwest RAMS CRC

- LMOS 6 26

- 28 Jun ‘91 11.9 1.82 0.69 16.7 Upper Midwest RAMS CRC

- LMOS 6 17

- 19 Jul ‘91 3.5 1.73 0.64 7.4 Upper Midwest MM5 CRC

- LMOS 6 26

- 28 Jun ‘91 5.8 1.70 0.79 14.0 Upper Midwest MM5 CRC

- LMOS 6 17

- 19 Jun ‘91 15.6 1.65 0.77 7.4

Eastern U.S. RAMS OTAG 3 13

- 21 Jul ‘91 4.6 1.61 0.74 27.1

Eastern U.S. MM5 OTAG 3 13

- 21 Jul ‘91 23.0 1.92 0.73 17.0

Eastern U.S. MM5 OTAG 2 1

- 11 Jul ‘88 65.6 3.21 0.64 7.9

Eastern U.S. MM5 OTAG 1 12

- 15 Jul ‘95 21.2 1.91 0.68 15.2

Eastern U.S. MM5 Cincinnati SIP 5 18

- 22 Jun ‘94 82.4 2.69 0.80 0.1

Southeastern U.S. MM5 BAMP 9 6

- 11 Sep ‘93 89.4 2.36 0.60 21.5

Southeastern U.S. MM5 BAMP 9 15

- 19 Aug ‘93 193.6 2 .66 0.65 120.0

### 422 Mean Value 42.2 2.02 0.68 16.8

### 410 Figure 12

- 1 . Accuracy of Predicted Peak 2m Temperatures From Recent Prognostic Meteorological Models.

### 411

Figure 12

- 2. Mean Bias in Predicted Hourly Temperatures at 2 m From Recent Prognostic Meteorolo gical Models

.

### 412

Figure 12

- 3. Bias in Hourly Wind Speeds at 10 m From Recent Prognostic Meteorological Models.

### 413

Figure 12

- 4. Difference in Mean Wind Direction at 10

- m From Recent Prognostic Meteorological Models

.

### 414

13. APPENDIX 4 :



# Southern A ppalachian Mountains Initiative



# M E T E O R O L O G I C A L 

# M O D E L I N G



# P R O T O C O L

Version 3 May 6, 1998

Prepared By William B. Norris and Kevin G. Doty University of Alabama in Huntsville

### 415 Meteorological Modeling Protocol

1. Introduction

The Georg

ia Institute of Technology (GIT) plans to simulate a series of episodes selected for the Southern Appalachian Mountains Initiative (SAMI). The episodes have been chosen so that important metrics of ambient ozone concentration, acid deposition, and visibil ity impairment can be statistically constructed from the model results and used to assess future air quality in the Southern Appalachian Mountains. GIT will use

the Urban

- to

- Regional Multiscale model ( URM

- 1ATM ) to carry out the air

- quality simulations. A mong the inputs required by URM

- 1ATM are fields of meteorological variables such as wind components, temperature, and moisture. The University of Alabama in Huntsville (UAH) will use a mesoscale meteorological model to provide the required meteorological input for URM

- 1ATM

.

This document describes the technical choices in the design of the meteorological model simulations and serves the following purposes:



## • It commits UAH to a set of data sources and procedures for conducting the meteorological simulation s.



## • It clarifies the interface between the UAH and GIT models.



## • It provides SAMI with a basis for reviewing, evaluating, and approving the meteorological modeling

activities.

Nevertheless, the document should be viewed more as guidance than law. Each epi sode will undoubtedly possess unique features that may require adjustments to the prescribed modeling approach. The modeling process should be left flexible enough to allow for adaptation to situations that may be perceived only as the

model simulations a

re conducted.

The primary model for producing the meteorological fields is the Regional Atmospheric Modeling System (RAMS), Version 3a, developed by scientists at Colorado State University. To a lesser extent UAH will use the Pennsylvania State Universit y

- National Center for Atmospheric Research (PSU

- NCAR) mesoscale model, version 5, (MM5). The technical attributes of these models are described in Appendix A (RAMS) and Appendix B (MM5). RAMS has been used in previous air

- quality evaluations to provide meteorological fields to photochemical models (Lyons et al., 1991; Ozone Transport Assessment Group (OTAG), 1996). Likewise, MM5 has been used for these types of applications (Seaman et al ., 1995).

The main body of the document is organized into nine to pics. Following this introduction Sections 2

- 4 describe the overall design and approach to the meteorological simulations. Sections 5

- 7 describe processing and evaluating the data produced by the model runs. Section 8 pertains to computational issues

su ch as the hardware to be used and the estimated amount of time needed to perform the simulations. Section 9 pertains to quality assurance.

### 416

Section 1: Introduces the protocol.

Section 2: Defines the model domain and grid structures.

Section 3: Describe s the kinds of inputs RAMS will use for initialization and assimilation.

Section 4: Discusses the choices to be made among available subgrid

- scale parameterizations.

Section 5: Lists the variables for which output will be available from RAMS either direc tly or as

derived values.

Section 6: Lists and discusses activities that will be carried out for a subset of the simulations to

evaluate model performance.

Section 7 : Describes the major difficulties that will be encountered in exporting the meteorologic al

fields to URM

- 1ATM and how these difficulties will be approached.

Section 8 : Describes the computational resources to be used and provides estimates of the run times

for standardized simulations so that actual run times can be predicted.

Section 9: Ou

tlines quality

- assurance activities that will be carried out to promote the accuracy and reproducibility of the meteorological modeling.

2. Meteorological Model Domain and Grid Configuration

Important initial decisions in designing the simulations are se lecting the size and location of the meteorological model domain and its system of nested grids. The aim of these choices is to ensure that the data supplied to URM

- 1ATM is appropriate as possible for air

- quality modeling in the SAMI region subject to the constraints of available resources.

2.1 Meteorological Model Domain

Horizontally the domain must be large enough to supply meteorological fields for the entire URM

- 1ATM domain. Vertically the domain must be high enough to model the meteorological proce sses that could significantly affect air

- quality levels in the Southern Appalachians.

2.1.1 Horizontal Extent of Meteorological Domain

The meteorological model domain must be at least as large as the air

- quality model domain. For comparison with the met

eorological domain, a horizontal view of the air

- quality domain is shown in Figure

1

- 1 in a Mercator projection with the true latitude at 26 o 

### N and central longitude of 99 o 

### W. The domain

extends east to west from about 66 o 

### W to 99 o 

### W, and south to north f rom about 26 o 

### N to about 47 o 

### N. This domain was selected to encompass all the major source regions which potentially have a significant

### 417

impact on the Southern Appalachians.

The bounds of the meteorological model domain are shown in Figure 2. The size an d location ensures that at every point in URM

- 1ATM the applicable meteorological values can be obtained by interpolation (with subsequent adjustment to conserve mass between the two domains [see Section 7]).

2.1.2 Vertical Extent of Domain

The top of the meteorological domain is approximately 17 km . With this vertical extent RAM permits the development of deep convection and can capture the vertical motions associated with stratospheric intrusions into the troposphere which may be important in relation to ozone transport.

2.2 Grid Configuration

The characteristics of the physical problem and the demands on computer resources have been considered in determining the horizontal and vertical resolution of the grid structures to be used. Independent of the resolution, RAMS uses the Arakawa C grid (Messinger and Arakawa, 1976; Arakawa and Lamb, 1977) as the basis of its numerical integrations. In this arrangement scalar variables are defined at grid

- cell centers and wind components are defined on grid

- cell faces (Figure 3)

2.2.1 Horizontal Grid Configuration

Before describing the horizontal grid system to be used in RAMS, the grid system of URM

- 1ATM will be

described. The URM

- 1ATM grid structure has five levels of resolution. The outermost grid has a

res olution of 192 km with successive grids having resolutions of 96, 48, 24, and 12 km. The grids having the coarser resolutions are used to resolve emissions in major source regions away from the Southern Appalachians. The finer grids are used to resolve bo th emissions and pollutant levels in or near the complex topography of the Southern Appalachians. These features are depicted in Figure 1

- 1

.

The RAMS horizontal grid configuration differs from that of URM

- 1ATM . Both models carry out their respective calc ulations in a Cartesian coordinate system but use different map projections. URM

- 1ATM maps its domain from the surface of a spherical earth into a plane using a Mercator projection while RAMS uses an oblique stereographic projection.

The coarse, interm

ediate, and fine grids for RAMS have resolutions of 96, 24, and 12 km, respectively, as shown in Figure 2. The details about the locations of the three grids are listed in Tables 1

- 3. The purpose
of the 96

- km grid is to capture the synoptic

- scale meteoro logy that influences mesoscale processes in and near the Southern Appalachians. The RAMS intermediate grid (horizontal resolution of 24 km) covers the Southeast, and the fine grid (horizontal resolution 12 km) covers portions of the Southern Appalachians.

The choice of 12 km for the RAMS fine

- grid resolution was largely based on the use of the URM

- 1ATM

12

- km fine

- grid resolution for URM

- 1ATM . Aside from the need to limit excessive model run times, the rationale was that the meteorological details resultin g from using a finer grid, such as 4 km, would be smoothed out when the meteorological fields were interpolated into the coarser URM

- 1ATM domain. The

location of the 12

- km grid required that it include the Great Smoky Mountains National Park and the

### 418

Shena ndoah National Park.

2.2.2 Feasibility of an Ultra

- Fine Grid Resolution for Complex Terrain

An ultra

- fine resolution of 4 km will significantly improve the resolution of complex terrain. Finer resolution

of the terrain will in turn lead to better reso lution of slope

- induced meteorological fields. The question is whether the higher resolution will significantly affect the outcome of the air

- quality modeling. Therefore, UAH will make two preliminary model runs on a selected episode. The first will use the nest of the 96, 24 and 12 km just described, while the second will add the ultra

- fine resolution of 4 km.

The ultra

- fine resolution consists of two grids

-- one near the Great Smoky Mountain National Park and the

other near the Shenandoah National Park as shown in Figure 4. The grid covering Shenandoah has been extended to the west to include the nearby front range of the Allegheny Mountains which is expected to affect the meteorology that occurs in Shenandoah. The topography of the region is shown in Figure 5. The

details about the characteristics of the ultra

- fine grids are listed in Table 4.

The output fields from the two preliminary model runs will be passed to GIT for input to URM

- 1ATM . If

the ultra

- fine resolution leads to air

- quality model re sults which are significantly different then the UAH/GIT team will seek guidance from SAMI in resolving the conflict between unacceptably long model run times and the possible degradation of modeling quality from using a maximum resolution of 12 km. "Sign ificantly" is intentionally not defined here in order to permit discussion of this point among SAMI modeling

subcommittee members when the test results become available.

2.2.3 Vertical Grid Configuration

RAMS and URM

- 1ATM differ in their vertical coordin ate systems. RAMS uses a sigma

- h system. At the surface the model’s lowest level assumes nearly the shape of the underlying terrain. The upper levels become progressively flatter as they approach the domain top. The thickness of the RAMS vertical layer s increases from 40 m near the surface to a maximum of 1000 m. The spacing of the vertical levels is listed in

Table 5.

3. Model Inputs

The required input data for RAMS can be divided into two main groups. One is the atmospheric data for the initial, boundary condition, and nudging fields. The other is the various fields which are required to specify the surface characteristics.

3.1 Atmospheric Data

Atmospheric data are needed to initialize model fields, to provide time

- dependent boundary conditions as the simulations proceed, and to furnish values interior to the model domain toward which the model solution can be nudged (see 4.3).

The National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research

### 419

(NCAR) reanalysis data (Kalnay et al., 1996) was chosen as the main data source for the atmospheric data. The procedures used to transform the reanalysis data into a form useful in RAMS is described in Appendix C. The required three

- dimensional variables needed for an ini tial state are the horizontal wind components, water vapor mixing ratio, potential temperature, and a scaled pressure (the Exner function).

3.2 Surface Characteristics

Topography for the 96

- km grid will be obtained from a 10

- minute resolution dataset com piled by the U.S. Navy (Global Terrain Height Data) and for the other grids from a 30

- second resolution dataset (NCAR Contiguous U.S. Terrain Height Data).

Among the important variables in regional modeling, soil moisture is one of the most intractable because of its spatial and temporal variability and the absence of routine measurements. Values can be deduced from infrared satellite images, but because of expense and time, that approach is not being considered for this project (although see 6.3). Ini tial soil moisture fields will be obtained from the reanalysis dataset even though they are highly influenced by the model component of the reanalysis system as opposed to

observations.

Soil temperature will initialized as a function of the atmospheric te mperature. This is accomplished in RAMS by specifying a profile of departures from the lowest air temperature. Sea

- surface temperatures (specifically for the Atlantic Ocean and the Gulf of Mexico) will be obtained again from the reanalysis

dataset.

Soil type data are derived from the STATSGO dataset developed by the National Cooperative Soil Survey and distributed by the National Resources Conservation Service. Raw data resolution is on the order of 1 km horizontally and with 11 layers in the vertical o ver most of the continental United States. The current soil model in RAMS is not designed to allow variable soil types in the vertical so the predominant or modal

value was chosen from the STATSGO data for each soil model column.

Vegetation indices and 1 8 land

- use types are supplied with RAMS by the model distributor. These values

will be used in each simulation.

4. Model Configuration

RAMS is a versatile modeling system capable of simulating flows from the scale of a global hemisphere to

the scale o f a building (Pielke et al ., 1992). Nearly 200 options are available to RAMS users to allow them to tailor the model configuration to best fit the problem to be studied. This section discusses the most important of these choices for SAMI modeling.

4.1 N esting vs. Stand

- Alone Domains

Preliminary work in running the different resolution grids independently an d in a sequential fashion in a one

way nested approach has not been successful. Therefore, future simulations will run the nested grids

simultaneous ly in a two

- way approach which is the default and intended mode of the RAMS model.

### 420

4.2 Moist Processes

For each type of impact of interest to SAMI

-- ozone concentrations, acid deposition, and visibility impairment

-- clouds and precipitation are important and therefore the various choices which govern the moisture physics are important as well.

4.2.1 Cumulus Parameterization

The generation of cumulus clouds presents a special problem for mesoscale modeling. A number of cumulus parameterizations have been devised to approximate subgrid

- scale convection by using the grid

scale values as the environment for the convection. A modified version of the Kuo parameterization (Kuo, 1974), is available in RAMS and will be activated for the 96

- and 24

- km grids but p robably not the 12

- km grid. This version of the Kuo scheme has a simple downdraft with the precipitation efficiency being made a

function of vertical wind shear.

4.2.2 Grid

- scale Moist Processes

One of SAMI’s purposes for modeling the Southern Appalachi ans is to determine the relationship between emissions and acid deposition in the region. In order for the air

- quality model to simulate wet deposition properly, the meteorological model will need to provide variables such as three

- dimensional fields of c loud and rain water mixing ratio and two

- dimensional fields such as cloud base and cloud top and the surface precipitation rate. RAMS provides several levels of moist physics of which the most complex allows for five prognostic condensate types: rain, sno w, pristine ice, ice aggregates, graupel, and hail. Cloud water values are obtained diagnostically by subtracting the total condensate plus water vapor from the total water. UAH is proposing to use the minimum level of the moist physics which will allow for only cloud and rain water but no ice microphysics.

4.3 Four

- Dimensional Data Assimilation (FDDA)

FDDA is a technique for incorporating external data into a model so that it influences the model solution while maintaining time continuity and dynamic c oupling among the modeled fields. The external data may consist of observations, if the model is being used to reconstruct past events, or of forecasts. Also, the data may be available for all or only a few of the model domain’s grid points, either on th e boundaries or in the

interior.

RAMS uses the method of Newtonian relaxation, or nudging, to incorporate external data. With this method the model state is relaxed toward the state defined by the external data by adding artificial tendency

terms to one or more of the models prognostic equations. In RAMS the artificial tendency term is given by



# ( ) a a T

e m 

### − (1)

where a e 

### is the external value, a m



### is the corresponding model value, and T is a time scale controlling the strength of the nudg

ing term. The time scale T can be allowed to vary from a typical minimum value near the

### 421

domain walls in order to maximize nudging there and then increased to the maximum value, T
max 

### , in the

center of the domain. The time scale T can also be held constant everywhere and this will be the initial approach taken in this study. Nudging will be performed on the 96

- km grid but not on the finer meshes. For the SAMI simulations the external data will be the analyses obtained from the reanalysis dataset. The

FDD A will be performed with T
max 

### = 1 h. In tests conducted by the Tennessee Valley Authority less bias was found in the simulated wind speeds and directions when T
max 

### = 1 h. However, the standard error in these variables increased compared to results when T
ma x 

### = 3 h. The optimal choice may be episode dependent.

4.4 Other Configuration Considerations

Other choices must be made before RAMS can be run. Some of these are summarized in this section.

4.4.1 Model Physics

The equations used by RAMS are the nonhy drostatic, quasi

- Boussinesq set described by Tripoli and Cotton (1982). Although the hydrostatic version of RAMS could be used on the coarser grids, the nonhydrostatic model will be used throughout to maintain consistency.

4.4.2 Finite

- Difference Formula tion

The finite

- difference formulation is second order in space and a hybrid combination in time. In the hybrid temporal scheme in the nonhydrostatic mode the u,v, and w wind components and pressure use centered in time differencing (i.e., the leapfrog s cheme) whereas all other scalars use forward in time differencing. The advection terms are written in flux form so that mass, momentum, and energy are conserved.

4.4.3 Diffusion

The horizontal diffusion is handled as a first

- order eddy viscosity based o n a local exchange coefficient that is a function of velocity deformation and static stability derived from Smagorinski (1963) with modifications. The vertical diffusion calculations include a Mellor and Yamada (1982) scheme to allow for the prognostic

de termination of the turbulent kinetic energy.

4.4.4 Radiation

RAMS includes two schemes for radiation calculations. The Mahrer

- Pielke (1977) scheme accounts for sloping terrain, forward Rayleigh scattering, and the absorption of shortwave radiation by ox ygen, ozone, carbon dioxide, and water vapor, but it does not allow for the presence of any kind of condensate. The Chen and Cotton (1983) scheme does account for condensate and is the scheme UAH will use for SAMI modeling initially. UAH is aware that a potential problem has been identified in the Chen and Cotton (1983) scheme having to do with solar heating rates in stratiform clouds. If the problem appears in the SAMI modeling, UAH may switch to the Fu et al. (1995) scheme which is not supplied with RA MS but which has been added to RAMS by researchers in the Global Hydrology and Climate Center in Huntsville, Alabama. A drawback of the Fu et al. (1995) scheme is its high computational demands.

### 422

4.4.5 Soil Model

The boundary values at the bottom of the domain are supplied by a soil model. The model was first developed by McCumber and Pielke (1981) and later modified by Tremback and Kessler (1985). A layer of finite depth at the soil

- atmosphere is assumed, and prognostic equations are formulated for flu xes of heat and water vapor through the layer. The model assumes eleven layers from the surface down to 0.50 m

below the surface.

4.4.6 Model Physics Changes

The original RAMS

- 3a model uses the Louis (1979) parameterization in determining the surface fluxes which eliminates the need for any iterative calculations to determine the Obukhov length. Beljaars and Holtslag (1991) provide evidence that the difference between the Louis (1979) approach and an exact calculation can be large in stable situations . Therefore, an efficient iterative approach which explicitly solves for the Obukhov length as a function of the surface Richardson number has been implemented using the general recommendations of Beljaars and Holtslag (1991). The transition of the surfa ce fluxes to the free convection regime is implemented in a fashion similar to Beljaars (1994).

The original cloud fraction approach in RAMS

- 3a was to designate it either as zero or one but no values in between. The Ek and Mahrt (1991) cloud fraction sch eme has been introduced as described by Mocko and Cotton (1995). It has been implemented in a way such that cloud fractions in the unstable boundary layer are a function of the horizontal mesh size, the surface moisture flux, and the standard deviation of the vertical velocity.

5. Model Output

Hourly average values of all needed variables will be saved for each episode. The biogenic emission model

and URM

- 1ATM require certain meteorological fields that RAMS does not compute directly but which can

be der ived from the basic state variables. Table 7 gives a list of the variables which will be delivered to GIT, classifies it as “basic” or “derived,” specifies its dimension, and gives the units in which it is expressed.

6. Evaluation of Meteorological Field s

Since externally available data is blended into RAMS solutions via FDDA, finding independent data sets to evaluate RAMS performance is a potential problem. However, certain routinely observed and independent surface variables exist, and two types of re motely sensed data sets are available that are relevant and useful. These data sets will provide a reasonable measure of RAMS performance. A relative measure comes from comparing RAMS output with the corresponding output of another meteorological model, such as MM5. Also, comparisons can be made with observations toward which RAMS has been nudged to ascertain how well the model is assimilating the external values. Finally, a subjective method for evaluating RAMS is to examine trajectories generated by a Lagrangian particle model. These matters are discussed in this section. The quantitative statistical measures that will be used to compare predicted and observed values are described in Appendix D.

### 423

6.1 Routine Observations

Three possible categories of routine observations could be used to verify model simulations. The first category is the hourly surface airways dataset which provides surface or near

- surface measurements of temperature, pressure, wind, and precipitation at several hundred locations ac ross the United States. Software has been developed which interpolates a given variable to an observed station location and computes error statistics such as the standard deviation and bias. This dataset will likely be the predominant

one used for the ve rification of near

- surface variables. The second category is the cooperative network of observations which has a much higher spatial density but which suffers from considerable asynoptic temporal characteristics which make it difficult to use. The third category is the network of rawinsondes which are available every 12 h. Again software has been developed to compare model soundings interpolated to the rawinsonde locations.

6.2 Comparison with Wind/Temperature Profiler Measurements

From June 19

- July 28, 1995, the Southern Oxidant Study (SOS) conducted an intensive field study of the transport of ozone and its precursors in Nashville and the surrounding region of Middle Tennessee. Measurements were taken on both surface and airborne platforms using bo th direct and remote

- sensing methods. Among the instruments used in the study were five wind and temperature profilers. These were located at various points in the Nashville area (Figure 6). Using radar, these devices were able to observe wind speeds an

d directions from near the surface to about 6 km above the surface. Using sodar, they were able to observe temperatures from near the surface to about 0.7 km above the surface. The resulting profiles are generally available every hour for the entire stud y period.

To take advantage of the rich set of observations with which model output can be compared, RAMS will be run initially over the SAMI domain for the period July 11

- 19, 1995. For each hour of this period the RAMS velocity components will be compar ed with the corresponding values from the profilers at various levels. Figure 7 shows how wind speeds calculated by RAMS compared with wind speeds measured by the three profilers closest to Nashville for four vertical layers during July 9

- 18, 1995. The w inds were taken from a RAMS run made by the Wisconsin Department of Natural Resources for OTAG. Figures of this type can assist in revealing biases in RAMS predictions and will be used in this manner to evaluate modeling carried out for SAMI. Similarly, comparisons will be made of RAMS and observed temperature profiles in the first few hundred meters of the atmosphere.

6.3 Comparison of Cloud Fields with Satellite Images

Modeled cloud fields can be compared with satellite images to determine how well th e moist physics of the model are working. Visible and infrared images are available hourly from NOAA geostationary satellites. Techniques are available for mapping these cloud fields into modeling domains so that grid

- cell comparisons

can be made of loca

tion, height, and optical thickness of clouds. This technique will be used to evaluate the performance of RAMS for one episode for which precipitation or extensive cloud cover is known to have occurred and for which satellite images are readily available. A summary of the techniques for processing satellite images for comparison with model results is given in Appendix E.

### 424

6.4 Comparison with MM5 Output

One of the ways in which MM5 is potentially superior to RAMS is the number of available cumulus
paramet erizations (seven in MM5 compared to one in RAMS). MM5 will be run for a wet, convective episode using the Kain

- Fritsch parameterization (Kain and Fritsch, 1993) to provide a comparison against the Kuo scheme of RAMS. In a comparison of several cumulus p arameterizations Wang and Seaman (1997) found that no particular one exhibited superior behavior in all simulations but the Kain

- Fritsch parameterization had several desirable characteristics including relatively good performance in warm

- season

events.

A comparison of MM5 and RAMS output for the same episode cannot be a pure comparison of cumulus parameterization schemes. Aside from the differences in the two schemes, MM5 and RAMS are based on somewhat different implementations of atmospheric physics and are initialized according to different procedures. However, using observed precipitation amounts at stations across the modeling domain judgments can be made about the performance of each model.

The modeling protocol for RAMS as described in Sections 2, 3, and 4 of this document will also apply to MM5 simulations except that data to initialize and provide time

- dependent boundary values for the model will come from MM5’s own data analysis package.

6.5 Lagrangian Particle Model Trajectory Visualizations

A subjective but useful means of examining the flow fields produced by RAMS is by flow visualization. One way to do this is by driving a Lagrangian particle model (LPM) with the output of RAMS. UAH has a LPM (McNider, 1981; McNider et al., 1988) that can be used for this purpose.

The LPM releases fictitious particles sequentially anywhere within the domain. Once released, the particles are subjected to the grid

- scale flow fields of the me teorological model and may also be subjected to turbulent transport

from subgrid

- scale flows. The results are displayed graphically, either as single frames or as animations. Use of the LPM is straightforward and consumes little extra computer time. Therefore, the LPM will be used with every episode using major source regions and selected locations in complex terrain as release points.

7. Post

- Processing of Meteorological Fields

RAMS was not designed for the purpose of providing input to URM

- 1ATM nor was URM

- 1ATM designed to receive the output of RAMS. Therefore, a number of post

- processing steps will have to be performed before the output from RAMS can be used in URM

- 1ATM . These steps include transformations between different map projections and adjustments to insure conservation of mass.

7.1 Map Projections

The horizontal wind components in RAMS are Cartesian components and are not components relative to a

### 425

spherical earth. The RAMS wind components will be rotated to "true" north

- south and west

- east components by UAH for use by GIT.

7.2 Interpolation of RAMS Fi elds to the URM

- 1ATM Grid

GIT will perform the necessary horizontal and vertical interpolations on the RAMS output data to the

URM

- 1ATM model grids as well as rotation of the "true" horizontal wind components to the Cartesian components on the URM

- 1ATM gr id. Further details are contained in the SAMI Air Quality Modeling Protocol (Russell et al ., 1997).

7.3 Conservation of Mass

The final step in converting RAMS fields to appropriate URM

- 1ATM input is to correct for a loss of

conservation of mass. The c ombination of spatial interpolation and different continuity equations between

RAMS and URM

- 1ATM can give rise to mass conservation errors in the URM

- 1ATM model. A common technique for rectifying the problem is to adjust the vertical velocity in the "new" model (here, URM

- 1ATM )

so that mass conservation is enforced. The d etails of how GIT will do this can be found in the SAMI Air Quality Modeling Protocol (Russell et al ., 1997).

8. Computational Considerations

Table 7 gives estimates for the total run t ime for 78 days on three machine configurations: a 75 MHZ SGI,

one 500 MHZ DEC

- ALPHA, and two 500 MHZ DEC

- ALPHAs. The total run time of 78 days is based on 62 chosen days plus 16 "ramp

- up" days needed on the basis of 2 "ramp

- up" days for 8 episodes. The time estimates given are purely computer time and do not reflect the time costs needed for run initialization, debugging, etc.

9. Quality Assurance

The model simulations carried out by UAH will make use of either RAMS or MM5, models recognized by
atmosp heric scientists as incorporating currently acceptable technology for representing atmospheric processes. The role of quality assurance in the modeling efforts of UAH will be to ensure that the critical steps in the modeling process can be accounted for, and if necessary, duplicated. To this end the program of quality assurance described below will be implemented.

9.1 Appropriateness of Input Data Sets

Meteorological and air

- quality models must be appropriately initialized and be supplied with appropria te boundary values if the results of a model simulation are to be meaningful. For the same reason the input files used to establish a model configuration for a simulation must be appropriate to the problem under investigation. Often differences of opinio n exist on the suitability of the data to be used for these purposes. Traditionally the issue has been settled by opening the modeling process up to peer review and allowing experts to pass judgment on the matter by independently critiquing the papers and presentations of their colleagues. This approach, which has a long

- standing history of utility and success in the modeling

### 426

community, will be employed by UAH. Input data sets for each episode will be available for inspection for anyone on the SAMI Model ing sub

- committee or for any other expert the sub

- committee chooses.

### 427

9.2 File Documentation

Five main groups of files are associated with each model run: the atmospheric data for the initial and nudging fields, the surface characteristics files for fi elds such as terrain, input file(s) specifying model options, the output of the model itself, and the model source code. Three copies of each file group will be maintained: one on the machine where it was produced, one on the long

- term disk storage on the local Cray, and one on a workstation backup tape. In addition, the input file(s) specifying model options will be appended to each model output file. The final measure to insure file documentation will be a careful log file

which will show the date of t he simulation and the names of the input data sets. The logs will make use of a consistent system of identification codes for associating the files with the simulation.

### 428 ACKNOWLEDGEMENTS

The first four authors would like to acknowledge and thank Stephen F. Mueller and his staff at Tennessee Valley Authority who assembled and edited this report. Plots for most of the UAH diagrams were created by the National Center for Atmospheric Res earch (NCAR) Graphics package. NCAR is supported by the

National Scienc e Foundation. The 500

- mb geopotential height anomaly plots were obtained from the National Oceanic and Atmospheric Administration

- Cooperative Institute for Research in Environmental Sciences (NOAA

- CIRES) Climate Diagnostics Center (CDC) web page. Much o f the data required to run and verify the meteorological simulations was obtained from NCAR's Scientific Computing Division's (SCD) Data Support Section. Other important datasets were obtained from the Earth System Science Center (ESSC) of the Pennsylvani a State University and the International Satellite Land Surface Climatology Project (ISLSCP).

### 429 References

Anthes, R. A. and T. T. Warner, 1978. Development of hydrodynamic model suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev

. 106 , 1045

- 1078.

Arakawa, A. and V. R. Lamb, 1977. Computational design of the basic dynamical process of the UCLA general circulation model. In Methods of Computational Physics

, Vol. 17 , 174

- 265. 337 pp.

Arritt, R. W., R. T. McNider, and R. A. Pie lke, 1987. Numerical model evaluation of the extension of the critical dividing streamline hypothesis to mesoscale two

- dimensional terrain. Atmos. Environ., 30 , 1905

- 1913.

Atwater, M. A. and P. S. Brown, Jr., 1974. Numerical calculation of the latitudinal variation of solar radiation for an atmosphere of varying opacity. J. Appl. Meteor., 13 , 289

- 297.

Barnes, S. L., 1973. Mesoscale objective analysis using weighted time

- series observations. NOAA Tech.

Memo. ERL NSSL

- 62, National Severe Storms Laboratory , Norman, OK 73069, 60 pp, [NTIS COM

73

- 10781].

Beljaars, A. C., 1994. The parameterization of surface fluxes in large

- scale models under free convection. Q. J. R. Meteorol. Soc., 121 , 255

- 270.

Beljaars, A. C. M. and A. A. M. Holtslag, 1991. Flux parame terization over land surfaces for atmospheric

models. J. Appl. Meteor

., 30 , 327

- 341.

Casey, D. M., R. T. McNider, and A. J. Song, 1995. A quantitative analysis of the predictability of mesoscale convective activity in regions of weak synoptic forcing ove r the southeastern United States. Fourteenth Conference on Weather Analysis and Forecasting, Jan. 15

- 20, 1995, Dallas, Texas, Am.

Meteor. Soc.

Chang, J. S., R. A. Brost, I. S. A. Isaksen, S. Madronich, P. Middleton, W. R. Stockwell, and C. J. Walcek, 198 7. A three

- dimensional eulerian acid deposition model: physical concepts and formulation. J. Geophys. Res

., 92 , D12, 14681

- 14700.

Chen, C. and W. R. Cotton, 1983. A one

- dimensional simulation of the stratocumulus

- capped mixed layer.

Bound.

- Layer Meteor

. , 25 , 289

- 321.

Diak, G. R., C. Gautier, and S. Masse, 1982. An operational system for mapping insolation from GOES

satellite data. Solar Energy, 28 :371

- 376.

Diak, G. R. and C. Gautier, 1983. Improvements to a simple physical model for estimating insolat ion from

GOES data. J. Appl. Meteor

. , 22 : 505

- 508.

Ek, M. and L. Mahrt, 1991. A formulation for boundary layer cloud cover. Ann. Geophys., 9 , 716

- 724.

### 430

Fast, J. D., 1995. Mesoscale modeling and four

- dimensional data assimilation in areas of highly comple x terrain. J. Appl. Meteor., 34 , 2762

- 2782.

Fast, J. D., S. Zhong, and C. D. Whiteman, 1996. Boundary layer evolution within a canyonland basin. Part II: Numerical simulations of nocturnal flows and heat budgets. J

. Appl. Meteor., 35 , 2162

- 2178.

Fu, Q. , S. K. Krueger, and K. N. Liou, 1995. Interactions of radiation and convection in simulated tropical

cloud clusters. J. Atmos. Sci

., 52 , 1310

- 1328.

Gautier, C., G. Diak, and S. Masse, 1980. A simple physical model to estimate incident solar radiation at

the surface from GOES satellite data. J. Appl. Meteor

. , 19 : 1005

- 1012.

Gautier, C., 1982. Mesoscale insolation variability derived from satellite data. J. Appl. Meteor

. , 21 : 51

- 58.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994. A Description of the Fifth

- Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note NCAR/TN

- 398+STR, National Center for Atmospheric Research, Boulder, Colorado.

Kain, J. S. and J. M. Fritsch, 1993. Convective parameterization for mesoscale models: The Kain

- Fri tsch

scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Mono ., No. 46, Amer. Meteor. Soc., 165

- 170.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. C

helliah, W. Ebisuzaki, W. Higgins, J. Janowlak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and D. Joseph, 1996. The NCEP/NCAR 40

- year reanalysis project. Bull. Amer. Meteor. Soc

., 77 , 437

- 471.

Koch, S. E., M. DesJardins, and P. J. Kocin, 1983. An interactive Barnes map analysis scheme for use

with satellite and conventional data. J. Climate Appl. Meteor

., 22 , 1487

- 1503.

Kondratyev, K. Y., 1969. Radiation in the Atmosphere . (New York: Academic Press).

Kuo, H. L., 1974. Further

studies of the parameterization of the influence of cumulus convection on large

scale flow. J. Atmos. Sci

., 31 , 1231

- 1240.

Louis, J. F., 1979. A parametric model of vertical eddy fluxes in the atmosphere. Bound.

- Layer Meteor

., 17 , 187

- 202.

Lynch, P., 1 989. Partitioning the wind in a limited domain. Mon. Wea. Rev

., 117 , 1492

- 1500.

Lyons, W. A., C. J. Tremback, and T. W. Tesche, 1991. Model Performance Evaluation and Sensitivity Testing. Prognostic meteorological modeling protocol for the Lake Michigan Ozone Study, Version 2. Lake Michigan Air Directors Consortium, 2350 East Devon Ave., Des Plaines, Illinois 60018.

### 431

MacDonald, J. E., 1960. Direct absorption of solar radiation by atmospheric water vapor. J. of Meteor

., 17 , 319

- 328.

Mahrer Y. and R. A. Pielke, 1977. A numerical study of the air flow over irregular terrain. Contrib. Atmos. Phys., 50 , 89

- 113.

McCumber, M. C. and R. A. Pielke, 1981. Simulation of the effects of surface fluxes of heat and moisture

in a mesoscale numerical model. Part I: Soi l layer. J. Geophys. Res., 86 , 9929

- 9938.

McNider, R. T., 1981. Investigation of the impact of topographic circulations on the transport and dispersion of air pollutants. Ph.D. dissertation, Dept. of Environmental Sciences, University of Virginia, 210

p p.

McNider, R. T. and R. A. Pielke, 1981. Diurnal boundary layer development over sloping terrain. J.
Atmos. Sci

. , 38 , 2198

- 2212.

McNider, R. T. and R. A. Pielke, 1984. Numerical simulation of slope and mountain flows. J. Climate and Appl. Meteor

. , 23

, 1441

- 1453.

McNider, R. T., M. Moran and R. A. Pielke, 1988. Influence of Diurnal and Inertial Boundary Layer Oscillations on Long

- Range Dispersion. Atmos. Environ

., 22 , 2445

- 2462.

McNider, R. T., A. J. Song, D. M. Casey, P. J. Wetzel, W. L. Crosson, and R. M Rabin, 1994. Toward a dynamic

- thermodynamic assimilation of satellite surface temperature in numerical atmospheric models.

Mon. Wea. Rev

., 122 , 2784

- 2803.

McNider, R. T., J. A. Song, and S. Q. Kidder, 1995. Assimilation of GOES

- derived solar insola tion into a mesoscale model for studies of cloud shading effects. Int. J. Remote Sensing, 16 , 2207

- 2231.

McNider, R. T, W. B. Norris, D. M. Casey, J. E. Pleim, S. J. Roselle, and W. M. Lapenta, 1997. Assimilation of satellite data in regional air quality models. Twenty

- Second NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its Application in Clermont

- Ferrand, France, June 2

- 6,
1997.

Mellor, G. L. and T. Yamada, 1982. Development of the turbulent closure model for geophysical flui d problems. Rev. Geophys. Space Phys

., 20 , 851

- 875.

Messinger, F. and A. Arakawa, 1976. Numerical Methods Used in Atmospheric Models. GARP Publication Series, No. 14, WMO/ICSU Joint Organizing Committee, 64 pp.

Mocko, D. M. and W. R. Coton, 1995: Evalu ation of fractional cloudiness parameterizations fro use in a

mesoscale model. J. Atmos. Sci

. , 52 ,2884

- 2901.

Mueller, S. F., A. Song, W. B. Norris, S. Gupta, and R. T. McNider, 1996. Modeling pollutant transport

### 432

during high

- ozone episodes in the southern Appalachian mountains. J. Appl. Meteor

., 35 , 2105

- 2120.

O'Brien, J. J., 1970. Alternative solutions to the classical vertical velocity problem . J. Appl. Meteor.

, 9

, 197

- 203.

Ozone Transport Assessment Group, 1996. Modeling Protocol, Version 3.0.

Pielk e, R. A., 1974. A three

- dimensional numerical model of the sea breeze over south Florida. Mon.

Wea. Rev

., 102 , 115

- 134.

Pielke R. A., W. R. Cotton, R. L. Walko, C. J. Tremback, W. A. Lyons, L. D. Grasso, M. E. Nicholls, M. D. Moran, D. A. Wesley, T. J. L ee, and J. H. Copeland, 1992. A comprehensive meteorological modeling
system

-- RAMS. Meteor. Atmos. Phys

., 49 , 69

- 91.

Poulos, G. S. and J. E. Bossert, 1995. An observational and prognostic numerical investigation of complex terrain dispersion. J. Appl. M eteor

., 34 , 650

- 669.

Ruggaber, A., R. Dlugi, and T. Nakajima, 1994. Modelling radiation quantities and photolysis frequencies in the troposphere. J. Atmos. Chem., 18 , 171

- 210.

Russell, A. G., R. McNider, J. Moody, and J. Wilkinson, 1997. Air Quality Mod eling Protocol. Southern Appalachian Mountains Initiative, Asheville, North Carolina.

Seaman, N. L., 1992. Meteorological modeling applied to regional air

- quality studies using four

- dimensional

data assimilation. In Environmental Modeling , ed. P. Melli and P. Zanetti (Southampton: Computational Mechanics Publications), 65

- 88.

Seaman, N. L., D. R. Stauffer, and A. M. Lario

- Gibbs, 1995. A multiscale four

- dimensional data assimilation system applied in the San Joaquin Valley during SARMAP. Part I: Modelin g design and basic performance characteristics. J. Appl. Meteor

., 34 , 1739

- 1761.

Smagorinski, J., 1963. General circulation experiments with the primitive equations, Part I, The basic experiment. Mon. Wea. Rev

., 91 , 99

- 164.

Tremback, C. J. and R. Kessl er, 1985. A surface temperature and moisture parameterization for use in mesoscale numerical models. Preprints, Seventh Conf. on Numerical Weather Prediction , Montreal, Que., Canada, Amer. Meteor. Soc., 17

- 20.

Tripoli, G. J. and W. R. Cotton, 1982. The C olorado State University three

- dimensional cloud/mesoscale

model

-- 1982, Part I, General theoretical framework and sensitivity experiments. J. Rech. Atmos

., 16 , 185

- 219.

Wang, W. and N. L. Seaman, 1997. A comparison study of convective parameterization sch emes in a

mesoscale model. Mon. Wea. Rev

., 125 , 252

- 278.

### 433

Zaveri, R. A., R. D. Saylor, L. K. Peters, R. T. McNider, A. Song, 1995. A model investigation of

summertime diurnal ozone behavior in rural mountainous locations. Atmos. Environ., 29 , 1043

- 1065.

### 434 Appendix A. RAMS

A.1 Description of RAMS

RAMS is a three

- dimensional, primitive

- equation model that uses terrain

- following coordinates. It represents the merger of a hydrostatic model originally developed at the University of Virginia (Pielke, 1974; Ma hrer and Pielke, 1977; M cNider and Pielke, 1981, 1984) and a nonhydrostatic model developed by Tripoli and Cotton (1982) at Colorado State University. The surface energy budget is maintained using radiative fluxes, latent and sensible heat fluxes, and sub surface heat conduction. Shortwave parameterization is by Mahrer and Pielke (1977); longwave parameterization, by Chen and Cotton (1983). Subsurface heat conduction is provided by a soil temperature model (Tremback and Kessler, 1985). Turbulence is para

meterized using a 1.5

- closure scheme developed by Mellor and Yamada (1982). RAMS provides for the inclusion of terrain elevations, land use, and land

- water fractions. RAMS also provides for four

- dimensional data assimilation (FDDA). This feature allows large

- scale evolution to be incorporated through the domain boundaries and observations within the domain to be blended with the

model

- calculated tendencies to control error growth.

There is no limit either on RAMS domain size or on its cell mesh size. T he model can be used to simulate large

- scale atmospheric systems on the order of a planetary hemisphere, mesoscale flows over subcontinental regions, microscale phenomena such as tornadoes, and submicroscale turbulent flow over buildings. In view of the c omplexity of mountain flows, RAMS’ ability to resolve small

- scale phenomena is advantageous. RAMS employs two

- way grid nesting that allows large

- scale and small

- scale phenomena to be modeled simultaneously.

A.2 Previous RAMS Applications

A considerable amount of the use of RAMS and its predecessor models by UAH investigators has centered around a study of flows affected by topography. McNider developed the first three

- dimensional simulations

of small

- scale slope and valley flows (McNider, 1981; McNider and Pielke, 1984) and investigated large

scale sloping terrain (McNider, 1981). Using mesoscale models, Arritt et al . (1987) also carried out an analysis of the role of thermally driven topographic flow in altering Froude

- number dependent plume

- impact

mod els, such as CTDMPLUS. Recently the role of the Great Smoky Mountains in influencing the regional circulation and in initiating convection has been investigated using models and satellite data (Casey et al

., 1995). The UAH efforts most directly related t o this work have been modeling studies which investigated the role of terrain in ozone exposure at high elevations and the impact on modulating the diurnal ozone profile (Zaveri et al ., 1995). For six weeks under the SOS program, UAH also carried out real

- time RAMS simulations over the Southeast during the Nashville field intensive campaign. The model was initialized and nudged at the boundaries using the Eta model forecasts. The ozone field was initialized using noon observations from state monitoring s ites and transported in the simulations as a passive scalar.

Pielke et al . (1992) have conducted a survey of the range of RAMS applications. These extend from large

- eddy simulations to synoptic

- scale weather systems and from mid

- latitude tornadoes to sub tropical thunderstorms. In particular in very recent studies, Poulos and Bossert (1995), Fast (1995), Fast et al

. (1996), and Mueller et al . (1996) describe studies in which RAMS is used to simulate flows over complex

### 435

terrain including FDDA.

RAMS has bee n applied to locations in the SAMI region. In an unpublished study, UAH used the hydrostatic predecessor of RAMS (UVA/CSU) to model the Southern Appalachians. The simulation was able to reproduce the grid scale updrafts over the elevated terrain and the subsidence over the surrounding regions. Mueller et al . (1996) employed RAMS, along with a Lagrangian particle dispersion model, to identify a meteorological modeling methodology that can be used in regional photochemical modeling and to identify large re gional ozone precursor sources that may impact the southern Appalachians during periods having high ozone levels. With respect to the first goal, a number of approaches were tested. A hydrostatic model with homogeneous initialization, RAMS run nonhydrost atically with homogeneous initialization, and RAMS run nonhydrostatically with nonhomogeneous initialization and FDDA were evaluated against observations. The last method best reproduced observed wind patterns.

### 436 Appendix B. MM5

MM5 is the fifth generatio n of the Penn State/NCAR Mesoscale Model originally developed by Anthes in the early 1970’s and later documented by Anthes and Warner (1978). The current version has been documented by Grell et al . (1994).

MM5 is a grid

- point model with finite difference s centered in space and time. Second

- order finite differences are used for the advection terms, and an Asselin time filter is applied to all prognostic variables. The model can be run either in a hydrostatic or nonhydrostatic mode. The hydrostatic optio n uses split

semi

- explicit time integration for efficient treatment of the fast gravity modes. The nonhydrostatic option

uses semi

- implicit time integration for the sound

- wave modes. The horizontal grid uses the Arakawa

- Lamb B staggering in which scalarsare defined at the center of a grid square and eastward and northward velocity components are defined at the corners. MM5 uses normalized pressure differences (sigma coordinates) for its vertical coordinate system. Vertically, all variables are carried at half

- sigma levels except for vertical velocity, which is carried at full

- sigma levels.

MM5 is capable of both one

- way and two

- way nesting up to nine domains with nests at the same level being allowed to overlap. Options are provided for initializing n ests. MM5 is also capable of FDDA to allow observations to be input to the model over an extended period of time using forcing terms that nudge the solution toward the observations while maintaining realistic continuity of flow and geostrophic and

thermal

- wind balances. The model is able to modify flows according the shape of the terrain underlying the

model domain. It also allows for thirteen land

- use categories that account for seasonal changes in climatological values of albedo, roughness length, long wave emissivity, heat capacity, and moisture availability. Calculations can be carried out on model grids that are either in the Lambert conformal, polar stereographic, or Mercator projections.

### 437 Appendix C. Initialization Procedures

The National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data (Kalnay et al., 1996) (hereafter referred to as "data" or "reanalysis data") was chosen as the main data source for the initial and boundary condition file s for the 96

- km meteorological grid for two reasons: 1) it provides a consistent, easily available dataset for any episode chosen for this study, and 2) it provides a source for variables such as soil moisture which are not synoptically available over lar ge areas. The reanalysis product used for the three

- dimensional fields was the data on a Gaussian horizontal grid and a sigma grid vertically with respect to pressure (i.e., sigma

- p). The horizontal resolution is on the
order of 1.9 o 

### while the vertical r esolution has 28 levels.

The important points of the process required to convert the reanalysis data into a form compatible for the RAMS model will now be discussed briefly. This involves three basic steps: a horizontal interpolation, a vertical interp olation, and a wind adjustment. The horizontal step uses a Barnes (1973) analysis scheme implemented in a manner similar to Koch et al. (1983) to obtain the values of the required variables on the reanalysis sigma

- p surfaces at the horizontal locations of the RAMS horizontal grid. A weighted

- average of a Barnes (1973) analysis of the surface airways observations of the wind at 10

- m and the reanalysis winds

at 10

- m provides the lowest level of data for the vertical interpolation of the wind data.

The vert ical interpolation involves three heights: the reanalysis terrain height, the model terrain height, and an estimate of the mixed layer depth. A stability dependent function with respect to the reanalysis data, F, is defined in (C.1),



## ( ) F = - z

- E 2 2 2

2 

### r ¶ q ¶ ¶ q ¶ z z (C.1)

where r 

### is density, 

## q is the potential temperature, and z and E are the height above sea level and terrain
height for the reanalysis data, respectively. An estimate of the top of the mixed layer, Z

m 

### , is then defined by the highest level where F > 10.0 kg K 3 

### m - 8 

### with a potential temperature lapse rate < 1.0e

- 03 K m

- 1 

### . The height differences dz 1 

### , dz 2 

### , and dz

g 

### are then defined by (C.2)

- (C.4), where E* is the model terrain height.

1 m 

### dz = Z E

− (C.2)

2 

### dz = E E

*



### − (C.3) 

# [ ]

g 1 

### dz = max dz

, 2



### dz (C.4)

For variables other than the wind components and for the situation where the model terrain is less than or equal to the data terrain, i.e., E* < E, the vertical profile is divided into t hree regions using the heights Z
c 

### and

Z p 

### as defined in (C.5) and (C.6).

c g 

### E + dz Z = (C.5)

p g 

### E + dz Z = * 

### (C.6)

For heights with respect to the model terrain, z*, which are at or above Z
c 

### , the model values are obtained by simple linea

r interpolation with respect to height from the data values. For the region Z p 

### < z* < Z

c 

### , the model values are obtained from a linear regression using data values between the heights Z p 

### and Z

c 

### . For the region below Z

p 

### , the data gradient values are utiliz ed according the descriptions given in (C.7)

- (C.10),

where the index "k" refers to data vertical levels and the index "l" refers to model vertical levels. The

### 438

difference between the data and model terrain heights is added to the data heights as in (C.7) s o that they can be referenced with regard to the model height values. The bottom and top weights for the linear interpolation of the data gradient values to the model level "l" are described in (C.8). The estimated gradient

value at model level "l" is th en given by (C.9), where "A" can be any scalar data variable. The actual model value of "A" at level "l" is then calculated by extrapolating downward from the value above as in (C.10).

a * 

### Z z + E E =

− 

### (C.7) 

## ( ) ( ) ( )
( ) ( ) ( ) ( ) for z k z z k + 1

z k + 1 z

z k + 1 z k

1

- a a

a

a a 

### ≤ ≤

= −

−

= 

   

    *

* 

### ,

, l

w l

w w b

t b 

### (C.8)

l b

k t

k 

### A

z w A

z w A

z ¶
¶ ¶

¶ ¶

¶   

= 

  + 

 

+ 1 

### (C.9)



## ( ) ( ) ( ) ( )



# [ ] * * * 

### A l A l A

z k z l

l a 

### = +

− 

  +

− 1 1 ¶
¶ Z (C.10)

For the wind components for the situation where the model terrain is less than or equal to the data terrain, the vertical profile is divided into only two regions. For model heights which are at or above Z
c 

### , the model values are obtained by simple linear interpolation. For model heights below Z
c 

### , the model values are obtained by expanding or contracting the equivalent reanalysis data layer using regression of the data wind values with respect to th

e natural logarithm of height. Some of the details of this procedure will now be discussed. The heights of the wind 10 m above the data and model terrain are denoted as in (C.11) and (C.12). Using the slope and intercept defined in (C13) equation (C.14) is the linear relationship or mapping between the reanalysis data heights z and the corresponding heights Z
a 

### on the model vertical grid. Three different linear regressions are then performed on the data wind components in the layer between Z
c 

### and

10 * 

### Z using the adjusted heights Z
a 

### . The three regressions are shown in (C.15)

- (C.17), where "B" is a u or v wind component. The slope b 

### remains the same for the three regressions but the intercept values change.
In (C.1 5) the regression line for the estimated wind component is forced to match the data value at the level

Z
c 

### . In (C.16) the estimated wind component is forced to match the data value with the largest magnitude in the layer from Z

c 

### to 10 * 

### Z at level Z

max 

### . Finally, in (C.17) the estimated wind component is forced to match

the data value at 10



### Z . The actual model value at a model grid level is then a weighted average of two of the

three estimates. The determination of whic h two estimates are used depends on whether z* is between Z
c 

### and Z
max 

### or whether z* is between Z
max 

### and 10

* 

### Z . This approach has three advantages: it allows for continuity of the wind components relative to the layer above where direct interpolation of the wind data are performed, it allows any wind maximum in the layer to be captured, and it allows for matching the data

winds at the 10

- m level.

### 439 10 

### 10 * * 

### Z E = + meters (C.11)

10 

### 10 Z E = + meters 

### (C.12)



# ( )

z c

c z z c 

### S Z Z

Z Z b S Z = −

− =

− 10

10 

### 1 *



### , ( C.13)

a 

### Z = +

z z 

### S z b (C.14) 

# ( )

t *

t 

### B ln z b = + b 

### (C.15) 

# ( )

b * b 

### B ln z b = + b 

### (C.16) 

# ( )

m *

m 

### B ln z b = + b 

### (C.17)

For the situation where the model terrain is greater than the data terrain, i.e., E* > E, the vertical profile is

divided into only two regions for all variables. For model heights which are at or above Z
c 

### , the model values are again obtained by simple linear interpolation. For variables other than the wind components the region

below Z

c 

### , the data gradient values are utili zed according the descriptions given in (C.7)

- (C.10) as described above. The wind components in the region below Z
c 

### are handled in the same way as described above as

well.

Barnes (1973) analyses of temperature and specific humidity at 2

- m from the surfac e observations are used to adjust the temperature and moisture fields on the RAMS grid after the vertical interpolation just described. The depth over which this adjustment occurs is the largest of either the mixing depth Z

m 

### or a specified minimum depth, which is usually on the order of 500

- 1000 m. The profiles of temperature and specific humidity are changed over this depth in a linear fashion to match the observed analyses at 2 m. The stability of each column is then checked and corrected to remove any layers above the bottom layer where potential temperature decreases with height.

The bulk of the computational time is spent in the third and final step which is the wind adjustment. The combination of horizontal and vertical interpolation and the diff erences in the terrain between the reanalysis data and the RAMS terrain introduce small changes in the divergent component of the wind which if left uncorrected generally lead to large, unrealistic vertical velocities at the top of the model domain. First the divergence and the relative vorticity are calculated on the RAMS grid. The divergence in each column is then adjusted as a function of height after O'Brien (1970) in order to obtain a vertical velocity near zero at the top of the model domain which i s consistent with the top boundary condition used in RAMS. The subsequent approach to the wind calculations is then essentially after Lynch (1989). The total wind is divided into divergent, rotational, and harmonic components. The harmonic wind is defin ed such that it has neither vorticity or divergence. The velocity streamfunction and potential fields are obtained by solving

Poisson's

- type equations with the known relative vorticity and adjusted divergence by simultaneous over

relaxation with a Dirichl et boundary condition of zero. Gradients of the streamfunction and potential fields then give the rotational and divergent wind components, respectively. The sum of the derived rotational and divergent wind components is then subtracted from the total wi nd on the domain border to obtain the first

guess of the border values of the harmonic wind. These perimeter values of the harmonic wind

### 440

are then adjusted such that their line integral around the perimeter is zero which insures the nondivergence of

the harmonic component. The velocity potential for the harmonic component is then calculated in the same way using simultaneous over

- relaxation with a Neumann boundary condition provided by the perimeter values of the harmonic wind. Gradients of the velocity potential then give the harmonic wind over the entire domain. The final wind field is then obtained by summing the divergent, rotational, and harmonic components over the entire three

- dimensional domain.

### 441 Appendix D. Statistical Measures for Model Evalua tion

Bias : The average of the differences between modeled and observed values.



### Bias = 1

n M i 

### − O i 

# ( )

i = 1 n 

# ∑

A positive (negative) bias indicates the model is overpredicting (underpredicting) on the

average.

Standard Error : The standard deviation of the differences between modeled

and observed values.



### Standard Error = 1

n M

i 

### − O i 

# ( )

− Bias 

# [ ] 2 

# ∑

The standard error is a measure of the dispersion of the modeled values from the observed

values.

Correlation : The normalized covariance of modeled and observed values.

Correlation = Cov ( M

, O )

s M 

### s O



### where



### Cov ( M

, O ) = 1

n M

i 

### − M 

# ( )

i = 1 n 

# ∑ O i 

### − O 

# ( )

M 

### and 

## O ( 

## s M 

### and s O 

### ) are the means (standard deviations) of the modeled and observed

values.

### 442 Appendix E. Satellite Image Processing Techniques

Three satellite data

- processing techniques that will be needed for carrying out work of meteorological

model evaluation are described.

E.1 Method 1: Surface Insolation from Satellite Images

The GOES

- 7 (GOES

- 8) satel lite returns the magnitude of upwelling radiation as brightness counts in the range 0

- 63 (0

- 1023). Relative to land and water, cloud tops are highly reflective and usually result in larger brightness counts. In general, the deeper the cloud, the greater its ability to reflect radiation in the visible range. Counts of intermediate magnitude indicate a mixture of radiation reflected from both cloud top and the earth’s surface. The lowest counts arise when little or no cloud is present and the reflection i s primarily, if not entirely, from the earth’s surface. Our insolation model has been developed to compute surface albedo and insolation values from visible images observed by GOES satellites (McNider et al ., 1995;

McNider et al ., 1997). The heart of the model is a radiative

- transfer technique patterned after that of

Gautier et al . (1980), Gautier (1982), Diak et al . (1982), and Diak and Gautier (1983), which is used to convert hourly GOES brightness data into surface insolation.

The technique requires h ourly surface albedo obtained from clear

- sky brightness counts. If a single, cloud

free image were available for each hour of daylight, the brightness counts could be obtained directly from them. However, because cloudy skies are so common, a single, clo ud

- free image is usually not available. Our experience has shown that for a given daylight hour images over a period of 20

- 30 days are needed to obtain a stable minimum brightness count for that hour, especially in the Southeast during summer when

cumulus clouds are ubiquitous. Brightness counts are converted into reflectances using a formula unique to each satellite. This approach has the inherent ability to account for both spatial and temporal differences in albedo due to soil type, vegetation, and ti me of day and year.

Once the clear

- sky albedo and the brightness count are known for a given satellite

- image pixel, surface insolation at the pixel can be calculated from the radiative

- transfer model of Gautier et al . (1980). The model assumes a single c loud layer. Above the cloud layer, radiation is Rayleigh scattered and absorbed by water vapor. In the cloud layer, radiation is scattered and absorbed. Below the cloud layer, radiation is absorbed by water vapor. For the scattering coefficients, we us e the parameterization originally presented by Kondratyev (1969) and modified by Atwater and Brown (1974). For the water

- vapor absorption coefficients, we use an empirical formulation of MacDonald (1960). For in

- cloud absorption we use a step

function th at depends on brightness count (McNider et al ., 1995). This results in a quadratic equation in cloud albedo. Once known, cloud albedo can be used to calculate downwelling solar radiation and insolation at the surface. The procedure yields surface insola tion at each pixel in an image. Such images can be gridded and values for all pixels within a grid cell averaged to produce hourly input fields for assimilation into photochemical models, or, as for the case we are proposing, for comparing with the hourly insolation fields predicted by a meteorological model.

### 443

E.2 Method 2: Cloud

- Top Altitudes from Satellite Images

Photochemical modeling systems differ in their sources of photodissociation constants. In some cases these

are obtained from radiative tran sfer models that include the scattering, absorbing, or reflecting properties of atmospheric aerosols and clouds (e.g., Ruggaber et al . 1994). In other cases radiative transfer models assume clear skies, and correction factors are later applied to the resu lts to obtain the desired cloudy

- sky values (e.g., Chang et al ., 1987). In either approach c loud information is essential. Cloud information is sometimes obtained directly from National Weather Service observations. In other cases it is obtained from

met eorological models that handle moisture dynamically and thermodynamically either through parameterizations or microphysics.

We have already mentioned the shortcomings of these data sources

-- the sparseness, lack of frequency, and subjectivity of the Nation

al Weather Service observations, and the errors in cloud placement, extent, and timing of the meteorological

- model predictions

-- and have pointed out how satellite observations can overcome many of these weaknesses. Calculating the effect of clouds on phot olysis frequencies requires a knowledge of cloud altitude and thickness. For example, RADM approximates the cloud optical depth t 

### with the parameterization

t = 3 L con 

### Δ z cld 

### 2 r H 2 O 

### r



### where L con 

### is the mean condense d water content, Δ z cld 

### is the mean depth of the cloud layer, r is the mean cloud drop radius, and r H 2 O 

### is the density of water (Chang et al ., 1987). In RADM, constant values are
assumed for each factor except Δ z cld 

### , which is obtained from the meteorological model.

Cloud

- top elevations can be estimated using infrared satellite images. Just as with visible images, GOES

- 7 (GOES

- 8) returns infrared measurements in discrete counts in the range 0

- 63 (0

- 1023) . These values can be transformed into temperatures using radiative transfer equations. If the area corresponding to a pixel is covered with clouds, the temperature associated with the pixel is that of the top of the clouds. If a temperature sounding is available near the location of the pixel at the time of the satellite measurement, the pressure level, and hence the elevation, at cloud top can be determined. At this point in the development of the technique, for a given pixel we are using the sounding from the closest rawinsonde station and are linearly interpolating in time between successive soundings.

A potential shortcoming of the method is using the sparse and relatively infrequent NWS soundings. Another potential shortcoming arises from the rel atively low resolution of infrared pixels. The 8 x 8 km (4 x

4

- km) area of a GOES

- 7 (GOES

- 8) infrared pixel may be only partially covered by clouds so that the temperature value returned by the satellite for that pixel is a weighted mean of the surface an d cloud

- top temperatures, T avg 

### . In such cases the method, without further modification, would produce a cloud

- top elevation lower than that of the actual cloud top. Because the horizontal resolution of GOES

- 7 and GOES

8 visible images (1 x 1 km) exceeds that of GOES

- 7 (GOES

- 8) infrared images by a factor of 64 (16), the visible image corresponding to an infrared image can be used to quantify the fraction a of an infrared pixel covered by cloud. If the surface temperature T sfc

### 444

for the pixel is known or can be estimated (perhaps directly from a surface measurement or from a neighboring clear pixel), then the temperature of the cloud top T cld 

### can be estimated by solving the following equation:

T avg 

### = ( 1

− a ) T

sfc 

### + a T

cld



### We have found that the square

- root weighting provides more consistent results than a linear weighting. A third possible shortcoming arises from the fact that the infrared sensor sees only the top of the uppermost cloud layer. We compu

te the elevation of the cloud base as the lifting condensation level, which is the elevation of the cloud base for the lowest cloud layer. If cloud thickness is computed as the difference between cloud top and cloud base, too large a thickness will be cal culated when multiple cloud layers are present in the troposphere. The problem can be particularly troublesome when lower cloud decks are shielded from the satellite sensor by cirrus.

E.3 Method 3. Surface (Skin)

- Temperature Changes from Satellite Images

Biogenic and soil NO

x 

### emissions rates depend on surface temperature. The ability of atmospheric models to predict surface temperatures would be improved if concurrent surface

- temperature observations could be successfully assimilated into the calculatio ns. We have developed a method, employing infrared satellite images, that is capable of assimilating surface temperature rates of change into boundary

- layer models. The method allows us to calculate surface temperatures that are completely consistent wit h values calculated for

all other variables in the model. We have tested the method in one

- and three

- dimensional models for cases not involving photochemical modeling (McNider et al ., 1994; McNider et al ., 1997).

In principle, the performance of atmosph eric models should be improved if observations are blended at appropriate times into the models’ calculations via FDDA. FDDA has not worked well for surface temperatures because they are not always compatible with the calculated vertical profiles of eithe r the air or soil. By altering the slopes of temperature profiles near the surface, the approach may cause predicted dispersion rates and other variables depending on the temperature gradient to be significantly affected (Seaman, 1992). Our method for as similating temperature observations overcomes the difficulty.

Surface temperature is produced from the interplay of a number of processes. Among these are those that govern net radiation, sensible and latent heat flux at the earth

- air interface, and heat

flux through the top few centimeters of soil. Some of these variables vary widely over space and time. For example, even if latent

heat flux is correctly initialized throughout the domain of a photochemical model, it is difficult for the model

to accura tely predict future values since it depends on the “friction humidity” of similarity theory, which is intrinsically difficult to measure or calculate. Our method allows us to skirt the need for knowing the value of friction humidity. By assimilating obse rved temperature rates of change into the model, we automatically account for the combined effect of all processes affecting surface temperature, including those pertaining to heat flux. Other types of satellite data can also be assimilated into the model s.

### 445 Table 1. Specifications of the RAMS Coarse Grid System
Horizontal Resolution: 96 km

Number of East/West Cells 35
Number of North/South Cells 31
Number Vertical Cells: 35

Center Latitude: 37.800 o N Center Longitude: 85.100 o W

Northwest

Corner
Latitude: 48.742 o N Longitude: 107.289 o W

Southwest Corner
Latitude: 23.858 o N Longitude: 100.902 o W

Southeast Corner
Latitude: 23.858 o N Longitude: 69.298 o W

Northeast Corner
Latitude: 48.742 o N Longitude: 62.911 o W

### 446 Tabl e 2. Specifications of the RAMS Intermediate Grid System

Horizontal Resolution: 24 km

Number of East/West Cells 62

Number of North/South Cells 58

Number Vertical Cells: 35

Center Latitude: 36.484 o N Center Longitude: 82.951 o W

Northwest Co rner Latitude: 42.477 o N Longitude: 91.679 o W

Southwest Corner

Latitude: 30.227 o N Longitude: 90.699 o W

Southeast Corner

Latitude: 29.973 o N Longitude: 75.547 o W

Northeast Corner

Latitude: 42.133 o N Longitude: 73.895 o W

### 447 Table 3. Specifications of the RAMS Fine Grid System
Horizontal Resolution: 12 km

Number of East/West Cells 84
Number of North/South Cells 68
Number Vertical Cells: 35

Center Latitude: 36.578 o N Center Longitude: 82.276 o W

Northwest Corner

Latitu de: 40.194 o N Longitude: 87.997 o W

Southwest Corner
Latitude: 32.725 o N Longitude: 87.734 o W

Southeast Corner
Latitude: 32.725 o N Longitude: 77.093 o W

Northeast Corner
Latitude: 39.904 o N Longitude: 76.302 o W

### 448 Table 4. Specificat ions of the RAMS Ultra

- Fine Grid System

Horizontal Resolution 4 km Great Smoky Mt. NP Shenandoah NP

Number of East/West Cells 71 68

Number of North/South Cells 44 32

Number Vertical Cells: 35 35

Center Latitude 35.733 ° N 38.495 ° N Center Longitud e 83.014 ° W 79.503 ° W

Northwest Corner

Latitude 36.478 ° N 39.024 ° N Longitude 84.603 ° W 81.079 ° W

Southwest Corner

Latitude 34.931 ° N 37.909 ° N Longitude 84.572 ° W 81.055 ° W

Northeast Corner

Latitude 36.479 ° N 39.025 ° N Longitude 81.46 9 ° W 77.974 ° W

Southeast Corner

Latitude 34.932 ° N 37.909 ° N Longitude 81.499 ° W 77.998 ° W

### 449

Table 5. Specification of Vertical Grid Spacing

Cell Level No. u,v and scalar vertical grid w vertical grid

35 16866.71 17366.71

34 15866.71 16366.71

3 3 14866.71 15366.71

32 13866.71 14366.71

31 12866.71 13366.71

30 11866.71 12366.71

29 10866.71 11366.71

28 9866.71 10366.71

27 8866.71 9366.71

26 7866.43 8366.71

25 6859.91 7366.71

24 5923.04 6371.05

23 5115.68 5505.26

22 4413.64 4752.40

21 380 3.16 4097.74

20 3272.32 3528.47

19 2810.71 3033.45

18 2409.31 2603.00

17 2060.27 2228.70

16 1756.76 1903.22

15 1492.83 1620.19

14 1263.33 1374.08

13 1063.77 1160.07

12 890.23 973.97

11 739.33 812.15

10 608.12 671.43

9 494.01 549.07

8 394.79 44 2.67

7 308.52 350.15

6 233.49 269.70

5 168.26 199.73

4 111.53 138.90

3 62.20 86.00

2 19.30 40.00

1

- 18.00 0.00

### 450

Table 6. RAMS Output to GIT

Variable Type Dimension Units u wind component Basic 3 m s - 1
v wind component Basic 3 m s - 1

vertical wind component Basic 3 m s - 1
density Basic 3 N m - 2

pressure Basic 3 mb
temperature Basic 3 K water vapor mixing ratio Basic 3 g kg
- 1
vertical turbulent exchange coeff. Basic 3 m 2 s - 2
u wind component at 10 m Basic 2 m s - 1
v wind component at 10 m Ba sic 2 m s - 1
temperature at 2 m Basic 2 K water vapor mixing ratio at 2 m Basic 2 g kg
- 1
grid

- scale cloud base Derived 2 m grid

- scale cloud top Derived 2 m convective cloud base Derived 2 m convective cloud top Derived 2 m convective cloud area Deriv ed 2 % liquid precipitable water Derived 2 mm convective fraction of total precipitation rate Derived 2 % mixing height Derived 2 m surface shortwave radiative flux Basic 2 W m - 2
total precipitation rate Basic 2 mm s - 1

### 451

The second row labeled "Rati o" is the ratio of CPU time divided by the simulation time for a two

- way nested run with the 96, 24, and 12 km grids. The total simulation time is derived from the minimum number of days to be modeled by current estimates which are 62 actual days over 8 e pisodes plus 2 "ramp

- up" days per episode which gives a total simulation time of 78 days. The columns then give the actual estimated time to run all days with the UAH SGI machine, one DEC

- ALPHA, and two DEC

- ALPHA machines. See

the text for further detail s.

Table 7. Estimated RAMS Execution Times

Parameter 75 MHZ

SGI 500 MHZ

DEC

- ALPHA Two 500 MHZ

DEC

- ALPHA

Ratio 4.16 1.04 1.04 Time for 78 days 324 days 81 days 41 days