# Meteorological Modeling Performance Summary

For Application to PM2.5/Haze/Ozone Modeling Projects

Kirk Baker, LADCO
Matthew Johnson, Iowa DNR Steven King, Illinois EPA

December 10, 2004

Lake Michigan Air Di rectors Consortium Midwest Regional Planning Organization
Des Plaines, Illinois

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 2 of 12 

# INTRODUCTION

The purpose of this document is to outline the model configuration and application of

the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) v3.6.3 to support photochemical and emissions modeling projects (Dudhia, 1993 and Grell et al, 1994) at Lake Michigan Air Directors Consor tium (LADCO) and th e Midwest Regional Planning Organization (Midwest RPO). The computing platform supported by LADCO/Midwest RPO is the Red Hat Linux op erating system and the Portland GroupFortran compiler. MM5 consists of the Mesoscale model MM5 and a suite of preprocessors including PREGRID, REGRIDDER, RAWINS, LITTLE R, INTERPF, INTERPX,

and TERRAIN.

The model parameterizations and physics options outlined in this document were

chosen based on the results of a series of sensitivity runs. The performance of the sensitivity tests provided a clear indication of an optimal configuration for the Upper Midwest (Johnson, 2003). The model configuration and parameterizations outlined in this document describe recent MM5 applications.

The annual 2002 36 km MM5 simulation was co mpleted by Matthew Johnson at Iowa DNR. The 36/12 km 2-way nested simula tion for the summers of 2001, 2002, and 2003 were conducted jointly by Steven King at Illinois EP A and Kirk Baker at LADCO.

TERRAIN Figure 1 The TERRAIN processor defines the horizontal grid of the MM5 application. The 24 category USGS 10 minute (~19 km) data is used for the National RPO 36-km domain, and 5 minute (~9 km)

data for 12-km domains. The National RPO grid is a Lambert conic projection
centered at coordinates –97, 40 with

first and second true latitudes at 33 and 45 degrees (See Figure 1).

The 36 km grid contains 165 x 129 grid cells and the 12 km has 193 x 199 grid cells. The 12 km grid is two-way nested within the mother grid to allow fi ne grid feedback into the coarse grid. Additional options are set to allow generation of data to support the Pleim-Xu land

surface module. Variables LSMDATA and IEXTRA are both set equal to TRUE.

Table 1

Domain

ID Grid X Cells (East-West) Y Cells (NorthSouth) Cell Size (km) Mother

Domain ID Lower Left
X,Y of Nest
1 National RPO 165 129 36 1 1, 1 2 Upper Midwest 193 199 12 1 66, 30

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 3 of 12 

# PREGRID

The PREGRID processor converts meteorologic al analyses data such as NCEP or ETA

to an intermediate data format that the REGRIDDER processor can utilize. For PREGRID, the following options were set:

• ETA/AWIP 3D and SF analyses data (ds609. 2) is used to initialize the REGRID

processor.

• Snow cover is estimated from water equivalent snow depth.

• The input analyses data is processed 3 hourly (10,800 seconds).

• The AWIP grib definition tables are used to map ETA data into MM5.

• The ETA skin temperature is used as th e source of sea surface temperature. The ETA analysis files with the extensio n “.tm12” are not used since they are the "cold start" global analysis files.

REGRIDDER

The REGRIDDER processor takes the data extracted from analyses fields and interpolates the data to user specified pressure levels and to the user specified horizontal grid.

LITTLE R

The RAWINS and LITTLE R processors perform objective analysis on the output from REGRIDDER using surface and upper air obse rvation data. Since these observations are incorporated into the ETA analysis fiel ds this step is considered redundant.

Results of sensitivity tests where ETA 3-hourly analysis was utilized to initialize with and without RAWINS objective analysis demons trated little or no difference in model performance (Baker, 2002).

Even though this step is redundant, LITTLE R is applied to enable surface nudging of soil moisture and temperature in the Pleim-Xu land surface module. NCEP ADP surface (ds 464.0) and upper air (ds 353.1 and ds 353.4) data are the appropriate data to input into LITTLE R and/or RAWINS.

INTERPF

The INTERPF processor takes the REGRIDDER/LITTLE R output that is at standard pressure levels and interpolates that data to the vertical grid defined by the user (Table 2). The vertical grid is defined in terms of sigmas, where 1 is the surface and 0 is the top of the model atmosphere. The to p of the MM5 simulati on is 100 millibars, which is approximately 15 kilometers above ground level.

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 4 of 12 

# The vertical atmosphere was resolved to 34 layers, with thinner layers in the planETAry boundary layer. The layer configuration was selected to capture the important diurnal variations in the boundary layer while also having layers in the upper troposphere to try and resolve convective activity. Output from the INTERPF processor is ready for input

into MM5.

INTERPX

The INTERPX processor is used to extract the soil temperature and soil moisture data from MM5 output files and overwrite the soil temperature and moisture fields on the MMINPUT file for the next 5 day

simulation block. This allows soil moisture and temperature to be carried over to subsequent modeling

simulations.

For example, to simulate 20 days in 4 blocks of 5 days, the first block of 5 days

would use the standard MMINPUT to run MM5, and the subsequent 3 blocks of 5 days would take the MM5 output and extract soil temperature and moisture data for the next 5 day block. This option is no longer recommended since it has

been shown to introduce a cold bias for th e temperature field, particularly in the winter months (Olerud, 2003).

MM5

The output from INTERPF, LITTLE R, and TERRAIN processors were used to run MM5. These files must be in the “./MM5/Run” directory and have the generic filenames given directly out of these processors. 3D analysis nudging for the wind field, temperatures, and moisture were applied above the boundary layer only. Analysis nudging was not performed on the rotational wind field. In addition, the observation nudging flag was turned off. This type of nudging is appropriate when there is a very dense set of observation data from a field study, which this application lacked. The default nudging weighting factors were used for all simulations: 2.5 x 10 -4 

# for wind fields and temperatures and 1.0 x 10 -5 

# for moisture fields. Table 2

k(MM5) sigma press.(mb) height(m) depth(m)

34 0.000 10000 14662 1841

33 0.050 14500 12822 1466

32 0.100 19000 11356 1228

31 0.150 23500 10127 1062

30 0.200 28000 9066 939

29 0.250 32500 8127 843

28 0.300 37000 7284 767

27 0.350 41500 6517 704

26 0.400 46000 5812 652

25 0.450 50500 5160 607

24 0.500 55000 4553 569

23 0.550 59500 3984 536

22 0.600 64000 3448 506

21 0.650 68500 2942 480

20 0.700 73000 2462 367

19 0.740 76600 2095 266

18 0.770 79300 1828 259

17 0.800 82000 1569 169

16 0.820 83800 1400 166

15 0.840 85600 1235 163

14 0.860 87400 1071 160

13 0.880 89200 911 158

12 0.900 91000 753 78

11 0.910 91900 675 77

10 0.920 92800 598 77

9 0.930 93700 521 76

8 0.940 94600 445 76

7 0.950 95500 369 75

6 0.960 96400 294 74

5 0.970 97300 220 74

4 0.980 98200 146 37

3 0.985 98650 109 37

2 0.990 99100 73 36

1 0.995 99550 36 36

0 1.000 100000 0 --SURF--

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 5 of 12



# Table 3 Configuration 36km and 12km Domains Explicit Moisture Mixed Phase (Reisner I)

Cumulus Kain-Fritsch 2 PBL Pleim-Chang (ACM)

Radiation RRTM Multi-Layer Soil Model Pleim-Xu

Shallow convection No

4-D Data Assimilation Analysis nudging on above PBL 

# Moist Physics Table No

Table 3 outlines the model configuration used for MM5 modeling up to the date of

this document. All simulation s use the mixed phase moisture scheme so that all four phases of water will be explicitly output by MM5. This is important since the photochemical model is applied for an annual basis and correctly characterizing the phase of water is important for several physiochemical processes.

Atmospheric radiation is calculated every 15 minutes in the model. Vertical moisture and temperature advection are set to us e linear interpolation. Other important

variables switched to ON include: moist vertical diffusion in clouds, temperature advection using potential temperature, diff usion using perturbation temperature, 3D coriolis force, and upper radiative boundar y condition. Sea surface temperature and snow cover are set to vary with time.

The Pleim-Xu land surface module requires that 3 additional variables be set in the
MM5 deck: ISMRD, NUDGE, and IFGROW. ISMRD is set to use soil moisture fields from the ETA analyses. NUDGE is set to nudge soil moisture data to the analyses fields. IFGROW is set to option 2, which takes vegETAtive growth into account based on vegETAtive fraction data from the TERRAIN file.

MODEL EXECUTION

MM5 was executed in 5 day blocks (7200 minu te simulation) with a 90 second time step. Model results are output every 60 minutes and the model output files are written out (i.e. split) every 24 hours to accommodate post-processing utilities. The

2002 annual simulation was initiated at 12Z December 16, 2001 and was run through 12Z January 1, 2003.

MODEL PERFORMANCE EVALUATION

Model performance was assessed quantitative ly with the METSTAT tool from Environ (Emery et al, 2001). The metrics used to quantify model performance include mean observation, mean prediction , bias error, gross error, root mean square error (including systematic and unsystematic components), and in dex of agreement. These metrics compare model predictions to Techniques Data Laboratory U.S. and Canada surface hourly observ ations (NCAR dataset ds472.0).

The MM5 model outputs predictions appr oximately 15 meters above the surface

while observations are at 10 meters. METSTAT applies micro-meteorological adjustments to the MM5 estimates to a pproximate 10-m values. MM5 also outputs

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 6 of 12 

# near-instantaneous values (90 second time step) as opposed to the values with longer averaging times taken at monitor stations. This should be considered when interpreting model performance metrics and making qualitative comparisons to satellite maps. Figure 2 Model performance metrics were applied to a sub-region of the Upper Midwest (Figure 2), meaning the metrics are hourly spatial averages of multiple

monitor locations. This will be done to gain a better understanding of MM5 performance in the Great Lakes region. All metrics are calculated within the specified model performance region for an hourly and daily time period (0Z to 23Z). Mean

wind direction is es timated by averaging the U and V wind vector components and converting those averages to compass degrees.

Additional analysis of rainfall will be done on a monthly basis. Rainfall totals in each grid cell by monthly will be compared to the corresponding monthly totals at

observation sites. Rainfall observation anal ysis data is available from the National Weather Service Climate Prediction Center on an hourly basis for the Continental United States ( http://www.cpc.ncep.noaa.gov/produ cts/precip/realtime/retro.html ). The rainfall analysis resolution is 0.25 degree longitude by 0.25 degree latitude and

extends from 140W to 60W and 20N to 60N.

PERFORMANCE METRICS

The bias error (bias) is the degree of correspon dence between the mean prediction
and the mean observation, with lower nu mbers indicative of better performance. Values less than 0 indicate under-prediction. The gross error , or mean absolute
error, is the mean of the absolute value of the residuals from a fitted statistical

model. Lower numbers indicate better model performance.

Root Mean Square Error (RMSE) is a good overall me asure of model performance. The weighting of ( prediction-observation ) by its square tend s to inflate RMSE, particularly when extreme values are presen t. With respect to a good model the root mean square error should approach zero. RM SE can be divided into a systematic and unsystematic component by least-squares re gression. Since differe nces described by systematic RMSE can be described by a linear function, they should be relatively easy to dampen by a new pa rameterization of the model. Unsystematic RMSE can be interpreted as a measure of potential accu racy or noise level (Emery et al, 2001). With respect to a good model the systemat ic difference should approach zero while the unsystematic difference approaches RMSE.

Index of Agreement is a relative measure of the degree of which predictions are

error-free. The denominator accounts for th e model's deviation from the mean of the

observations as well as to the observations deviation from their mean. It does not provide information regarding systematic and unsystematic errors. The index of agreement approaches one when model performance is best.

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 7 of 12 

# MODEL PERFORMANCE: ANNUAL 2002 36 km

The biggest issue with the pe rformance in the Great Lakes region is the existence of a cool diurnal temperature bias in the wint er and warm temperature bias over-night during the summer. The wind fields are good , although it should be noted that some diurnal errors in wind speed may not be ad equately represented in the daily metric plots. The model appears to be too wet in the late spring and summer months in the Eastern United States. Mosaic tile plot s showing daily bias and gross error for humidity, temperature, wind speed, and wind direction are shown in the Figures

below.

Performance in the other regions is simila r to the Great Lakes except where noted. The Ohio Valley has a slightly worse performance for day-time wind speed and wind direction. The central Plaines (CenrapN and CenrapS) have low peak wind speeds and a warm temperature bias in the sout hern Plaines/Texas re gion due to overpredicted night-times lows.

The Mid-Atlantic region has performance similar to the Great Lakes region. The southeast region is very wet compared to observations during the entire year. The northeast region shows degraded wind fi eld performance compared to the other Eastern regions.

Figure 3. Daily humidity bias and gross e rror (day 1 top row, last day of month bottom row)

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 8 of 12 

# Figure 4. Daily temperature bias and gross error (day 1 top row, last day of month bottom row)

Figure 5. Daily wind directio n bias and gross error (day 1 top row, last day of month bottom row)

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 9 of 12 

# Figure 6. Daily wind speed bias and gross error (day 1 top row, last day of month bottom row)

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 10 of 12 

# MODEL PERFORMANCE: RAINFALL

Convective and non-convective rainfall was to taled for each 36 km grid cell in the Eastern United States by mont h. The National Weather Serv ice rainfall analysis field was mapped to the same grid projection and domain and totaled by month. These plots are shown side by side in Appendix A.

The modeled and observed rainfall totals show good agreement spatially and in terms of magnitude in the winter, fall, an d early spring months of 2002. There arelarge over-predictions of rainfall in the late spring and summer months. These overpredictions are seen spatially and in magnit ude over the entire domain, particularly

in the Southeast United Stat es. These over-predictions are likely due to excessive convective rainfall being predicted in MM5.

MODEL PERFORMANCE: 36 km v 12 km

Model performance comparing the 36 and 12 km performance in the Great Lakes region is shown in the form of mosaic difference plots in Appendix B. In the Great Lakes region, a winter-time performance degradation is seen in the 12 km run compared to the 36 km run for the wind field and temperature. The temperatures have a cold bias at 36 km and get even colder at 12 km.

Daytime wind speed bias improves at 12 km, but nighttime bias degrades. The mixing ratio performance improves slightly in the Ohio Valley region at 12 km. Overall, the 12 km grid results in little if any benefit in terms of statistical model performance.

MODEL PERFORMANCE: Summers of 2001, 2002, and 2003

The model performance for the summers of 2001, 2002, and 2003 focusing on the Great Lakes Region is shown in prediction-o bservation time series plots in Appendix C. The predictions and observ ations are averages over many observation sites in the sub-region of interest. A summary of pe rformance in the Upper Midwest is given

below.

Summer of 2001:

• MM5 performs well for temperatures and wind field

• Not quite capturing temperature peak s during August 2001 ozone episode

• Overall moisture and rainfall slightly over-predicted, but spatial patterns look good

Summer of 2002:

• Capturing temperate peaks ok, but ov er-predicting nigh t time minimums

• Over-predictions of moisture and rainfall in the Ohio Valley; large overpredictions of rainfall in the South

• Wind fields look ok; diurnal wind speed day time peaks not always captured during the ozone episodes

Summer of 2003:

• Wind field ok; diurnal wind speed patterns not quite captured in August ozone episode

• Temperatures ok; not getting night time minimums consistently

• Rainfall ok in Upper Midwest; over-predictions in the South

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 11 of 12 

# MODEL PERFORMANCE: New 2002 v. Old 2002 Simulation

An annual MM5 simulation for 2002 was previously completed using an older version of MM5, simple ice microphysics, surface nudging, and continuous soil moisture (INTERPX). The figures in Appendix D show the differences be tween the previous

annual simulation and the cu rrent annual simulation with mosaic difference plots.

Temperatures in general show a slight degradation in the new simulation, in particular in the winter. Wind speed pe rformance improved and wind direction improved in the Central and Western United States. Humidity results are almost the

same in both simulations. Th e difference in model perfor mance is largely due to the inclusion of surface nudging in the prev ious annual 2002 simulation. That practice improves surface performance statistics but tends to create unusual meteorological artifacts like super-adiabatic lapse rates.

MODEL PERFORMANCE: Other Notes

The index of agreement hourly metrics for humidity are interesting in that periods of poor index of agreement do not necessari ly match up to poor error. Since the metrics are averaged over a sub-domain, the hourly index of agreement tends to degrade when fronts move across the doma in. This causes very different model predictions and observations on the front and back side of the frontal passage.

This lack of agreement results in a degrad ed index of agreement metric even though the error and actual model performance is not poor. This should be acknowledged when interpreting index of agreement over a spatial region; degraded IOA may actually show a frontal passage and not truly reflect poor performance.

The time series plots below show RMSE and IOA for June 2002. Frontal passages pass through the area on the 16 th 

# , 18 th 

# , 24 th 

# , and 27 th 

# and degrade IOA.

Humidity root mean squa re error (top) and index of agreement (bottom)

RMSE Humidity

0 0.5

1 1.5

2 2.5

3 3.5

4

6/

2 6/ 3

6/ 4

6/ 5

6/

6 6/ 7

6/ 8

6/

9 6/10

6/1

1 6 /12

6 /13

6/1

4 6/

15

6 /16

6/17

6/1

8 6 /19

6 /20

6/21 6/

22

6 /23

6/24

6/25 6 /26

6 /27

6/28 6 /29

6 /30

7 / 1g/kg RMSEHum RMSESHum RMSEUHum



### IOA Humidity

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

6 / 2 6

/ 3

6/ 4

6/ 5

6 / 6

6/ 7

6/ 8

6 / 9 6 / 10

6 /11

6/12

6 / 13

6 /14

6/15

6/16

6 / 17

6 /18

6/19

6/20

6 / 21

6/22

6/23

6 / 24

6 /2 5 6/26

6/27

6 / 28

6 /29

6/30 7 / 1 IOAHum

## Kirk Baker – Lake Michigan Air Directors Consortium & Midwest RPO Page 12 of 12 

# CONCLUSION & DISCUSSION

This document contains an overview of MM5 performance. Th e results shown are intended to provide a context for future meteorological modeling applications, to identify deficiencies in the MM5 output that may result in poor photochemical model performance, and to increase confidence in the photochemical modeling applications that use these meteorological modeling estimates.

REFERENCES

Baker, K. MM5 Sensitivity for the Nation al RPO Grid, Presented at the Ad-Hoc Meteorological Modeling Meet ing, Des Plaines, IL, 2002.

Dudhia, J., A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front, Mon. Wea. Rev.,
121, 1493-1513, 1993.

Emery, C., Tai, E., Yarwood, G., Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes, Final report submitted to Texas Natural

Resources Conservati on Commission, 2001.

Grell, G. A.; J. Dudhia , and D. R. Stauffer, A description of the Fifth Generation Penn State/NCAR Mesoscale Model (MM5), NCAR Tech. Note, NCAR TN-398-STR, 138 pp.,

1994.

Johnson, M., Meteorological Modeling Protocol: IDNR 2002 Annual MM5 Application,

2003.

Olerud, D., K. Alapat y, and N. Wheeler, Meteorological modeling of 1996 for the

United States with MM5 , Final report submitted to OAQPS, U. S. EPA, 2000.

Olerud, D., VISTAS Meteorological Modeling, Presented at the Ad-Hoc Meteorological Modeling Meeting, Des Plaines, IL, 2003.

Tonnesen, G., Wang, Z., Lents. J., Gemmill , D., Morris, R., Modeling Protocol and Quality Assurance Plan for the WRAP Visibility Modeling Project, 2001.

Yarwood, G. Improvements to CAMx Wet Depostion, Presented at the Spring Modeling Meeting, Des Plaines, IL, 2004.

# APPENDIX A

Rainfall AnalysisJanuary 2002February 2002March 2002April 2002May 2002June 2002July 2002August 2002September 2002October 2002November 2002December 2002

# APPENDIX B

12 v 36 km Performance

# Observed/Predicted Windspeed

2 3 4 5 6 7 8

1/

1 1/ 2

1

/

3

1/ 4 1/

5

1

/

6 1/

7

1/

8

1/

9 1/10 1/1

1 1

/1

2 1

/1

3 1/14 1/1

5 1

/1

6 1/17 1/1

8

1/1

9 1

/2

0 1/21 1/2

2 1

/2

3 1/24 1/25 1/2

6 1/

2 7 1/28 1/2

9 1

/3

0 1/31m/s ObsWndSpd 36km Predicted 12km Predict ed 

# Bias Windspeed

-1.5

-1 -0.5 0 0.5

1 1.5

1/

1

1

/ 2

1

/

3

1/ 4 1/

5

1/

6

1

/

7

1/ 8 1/ 9 1

/1

0 1

/1

1 1/12 1/1 3 1

/1

4 1

/1

5 1/16 1/17 1/1

8 1

/1

9 1/20 1/21 1/2

2 1

/2

3 1

/2

4 1/25 1/26 1/2

7 1

/2

8 1/

2 9 1/30 1/3 1m/s 36km Bias 12km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

1/

1

1

/ 2

1

/ 3

1/ 4 1/

5

1/

6

1/

7

1/

8

1/ 9 1/10 1/1 1 1

/1

2 1/

1 3 1/

1 4 1/15 1/1

6

1/1

7 1

/1

8 1/19 1/20 1/21 1/2 2 1

/2

3 1/

2 4 1/

2 5 1/26 1/2

7

1/2

8 1

/2

9 1/30 1/31deg ObsWndDir 36km Predicted 12km Predict ed 

# Bias Wind Direction

-30

0 30

1/

1

1

/ 2

1

/ 3

1/ 4 1/

5

1/

6

1/

7

1/

8

1/ 9 1/10 1/1 1 1

/1

2 1/

1 3 1/

1 4 1/15 1/1

6

1/1

7 1

/1

8 1/19 1/20 1/21 1/2 2 1

/2

3 1/

2 4 1/

2 5 1/26 1/2

7

1/2

8 1

/2

9 1/30 1/31deg 36km Bias WD 12km Bias WD 

# Great Lakes Jan 2002

# Bias Temperature

-5 -4 -3 -2 -1 0 1 2

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31K 36 km Bias T 12 km Bias T



# Observed/Predicted Temperature

255 260 265 270 275 280

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31K ObsTemp 36 km Pred_T 12 km Pred_T 

# Predicted/Observed Humidity

0 1 2 3 4 5 6

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31g/kg ObsHum 36km Pred q 12 k Pred q 

# Bias Humidity

-0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2

1/

1

1/ 2 1/ 3 1/ 4 1/

5

1/

6 1/

7 1/

8

1/

9 1/

10

1/

11

1/12 1/13 1/14 1/15 1/

16

1/

17

1/

18

1/19 1/20 1/21 1/22 1/

23

1/

24

1/2 5

1/

26

1/

27

1/

28

1/

29

1/30 1/31g/kg 36 km Bias q 12 km Bias q 

# Great Lakes Jan 2002

# Observed/Predicted Windspeed

2 4 6 8

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26 6 /27

6

/ 2 8 6/29 6

/30m/s ObsWndSpd 36km Predict ed 12km Predict ed



# Bias Windspeed

-2 -1 0 1 2

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26 6 /27

6

/ 2 8 6/29 6

/30m/s 36 km Bias 12 km Bias



# Bias Wind Direction

-30

0 30

6/

1

6/ 2 6

/ 3

6/

4

6/ 5 6

/ 6

6/ 7 6

/ 8

6/

9 6

/10

6/1 1 6/12 6/1 3 6/14 6

/ 1 5 6/1 6 6/17 6

/ 1 8 6/19 6

/ 2 0 6/2 1

6 /22

6

/ 2 3 6/24 6

/ 2 5 6/26 6 /27

6

/ 2 8 6/29 6

/30deg 36km Bias 12km Bias 

# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

6/

1

6/ 2 6

/ 3

6

/ 4

6

/ 5

6

/ 6

6/

7

6

/ 8

6/

9 6

/ 1 0 6/11 6

/12

6/13 6

/ 1 4 6/15 6

/ 1 6 6/17 6/1 8 6/19 6

/ 2 0

6 /21

6/2 2 6/23 6/2 4

6 /25

6/2 6

6 /27

6/2 8 6

/ 2 9 6/3 0deg ObsWndDir 36 km PredWnd 12 km PredWndDir 

# Great Lakes June 2002

# Observed/Predicted Temperature

270 275 280 285 290 295 300 305 310

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30K ObsTemp 36 km 12 km 

# Bias Temperature

-3 -2 -1 0 1 2 3

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30K 36 km Bias 12km Bias 

# Predicted/Observed Humidity

0 2 4 6 8 10 12 14 16 18

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30g/kg ObsHum 36 km Predicted 12k m Predict ed 

# Bias Humidity

-2 -1 0 1 2 3 4

6/

1

6/ 2 6/

3

6/ 4 6/

5

6/ 6 6/

7

6/

8

6/

9 6/10 6/

11

6/12 6/13 6/

14

6/15 6/

16

6/17 6/

18

6/

19

6/20 6/

21

6/22 6/

23

6/

24

6/

25

6/26 6/

27

6/28 6/

29

6/30g/kg 36 km bias 12 km Bias 

# Great Lakes June 2002

# Observed/Predicted Windspeed

0 2 4 6

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9 7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31m/s ObsWndSpd 36 km winds 12 km winds



# Bias Windspeed

-2 -1 0 1

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9 7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31m/s 36 km Bias 12 km Bias



# Observed/Predicted Wind Direction

0 60 120 180 240 300 360

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9 7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31deg ObsWndDir 36 km PredWnd 12 km PredWndDir



# Bias Wind Direction

-30

0 30

7/

1

7/ 2 7

/ 3

7

/ 4

7

/ 5

7

/ 6

7/

7

7

/ 8

7/

9 7

/ 1 0 7/11 7

/12

7/13 7

/ 1 4 7/15 7

/ 1 6 7/17 7/1 8 7/19 7

/ 2 0

7 /21

7/2 2 7/23 7/2 4

7 /25

7/2 6

7 /27

7/2 8 7

/ 2 9 7/3 0 7

/31deg 36km Bias 12km Bias 

# Ohio Valley July 2002

# Predicted/Observed Humidity

10 11 12 13 14 15 16 17 18

7/ 1 7/ 2

7/

3

7/

4

7/ 5 7/ 6 7/

7

7/

8

7/ 9

7/10 7/11 7/

12

7/

13

7/

14

7/

15

7/16 7/17 7/18 7/

19

7/

20

7/

21

7/

22

7/23 7/24 7/

25

7/2 6 7/27 7/28 7/29 7/

30

7/

31g/kg ObsHum 36km Pred q 12 k Pred q 

# Bias Humidity

-3 -2 -1 0 1 2 3 4

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31g/kg 36 km Bias q 12 km Bias q



# Bias Temperature

-3 -2 -1 0 1 2 3

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31K 36 km Bias T 12 km Bias T



# Observed/Predicted Temperature

280 285 290 295 300 305 310

7/

1

7/ 2 7/ 3 7/ 4 7/

5

7/

6 7/

7 7/

8

7/

9 7/

10

7/

11

7/12 7/13 7/14 7/15 7/

16

7/

17

7/

18

7/19 7/20 7/21 7/22 7/

23

7/

24

7/2 5

7/

26

7/

27

7/

28

7/

29

7/30 7/31K ObsTemp 36 km Pred_T 12 km Pred_T 

# Ohio Valley July 2002

# APPENDIX C

Performance for Summers 2001, 2002, and 2003JUNE 2001June 2001JULY 2001July 2001AUGUST 2001August 2001JUNE 2002June 2002JULY 2002July 2002AUGUST 2002August 2002JUNE 2003June 2003JULY 2003July 2003AUGUST 2003August 2003Upper Midwest High Ozone Days• 2001:– June 13-14, 18-19, 25-30– July 16-20; July 31 – Aug 2– Aug 5-8• 2002:– June 19-25– July 7-8, 14-17– Aug 1-4, 9-13• 2003:– June 22-26– July 1-3– Aug 20-21

# APPENDIX D

Performance Comparison: Old v. New Annual 2002 Simulation