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 Directors 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 Consortium (LADCO) and the Midwest Regional 

Planning Organization (Midwest RPO). The computing platform supported by 

LADCO/Midwest RPO is the Red Hat Linux operating system and the Portland Group 

Fortran 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 completed by Matthew Johnson at Iowa 

DNR. The 36/12 km 2-way nested simulation for the summers of 2001, 2002, and 

2003 were conducted jointly by Steven King at Illinois EPA 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 fine 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 meteorological 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 the source of sea surface temperature. 

The ETA analysis files with the extension “.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 observation data. Since these observations 

are incorporated into the ETA analysis fields this step is considered redundant. 

Results of sensitivity tests where ETA 3-hourly analysis was utilized to initialize with 

and without RAWINS objective analysis demonstrated 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 top of the MM5 simulation 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 the 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 

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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 simulations 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 use linear interpolation. Other important 

variables switched to ON include: moist vertical diffusion in clouds, temperature 

advection using potential temperature, diffusion using perturbation temperature, 3D 

coriolis force, and upper radiative boundary 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 minute 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 quantitatively 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 index of agreement. 

These metrics compare model predictions to Techniques Data Laboratory U.S. and 

Canada surface hourly observations (NCAR dataset ds472.0). 

The MM5 model outputs predictions approximately 15 meters above the surface 

while observations are at 10 meters. METSTAT applies micro-meteorological 

adjustments to the MM5 estimates to approximate 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 estimated 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 analysis 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/products/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 correspondence between the mean prediction 

and the mean observation, with lower numbers 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 measure of model performance. 

The weighting of (prediction-observation) by its square tends to inflate RMSE, 

particularly when extreme values are present. With respect to a good model the root 

mean square error should approach zero. RMSE can be divided into a systematic and 

unsystematic component by least-squares regression. Since differences described by 

systematic RMSE can be described by a linear function, they should be relatively 

easy to dampen by a new parameterization of the model. Unsystematic RMSE can be 

interpreted as a measure of potential accuracy or noise level (Emery et al, 2001). 

With respect to a good model the systematic 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 the 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 performance in the Great Lakes region is the existence of 

a cool diurnal temperature bias in the winter 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 adequately 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 plots 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 similar 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 southern Plaines/Texas region 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 field performance compared to the other 

Eastern regions. 

Figure 3. Daily humidity bias and gross error (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 direction 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 totaled for each 36 km grid cell in the 

Eastern United States by month. The National Weather Service 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, and early spring months of 2002. There are 

large over-predictions of rainfall in the late spring and summer months. These overpredictions are seen spatially and in magnitude over the entire domain, particularly 

in the Southeast United States. 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-observation time series plots in Appendix 

C. The predictions and observations are averages over many observation sites in the 

sub-region of interest. A summary of performance in the Upper Midwest is given 

below. 

Summer of 2001: 

• MM5 performs well for temperatures and wind field 

• Not quite capturing temperature peaks 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 over-predicting night 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 between the previous 

annual simulation and the current annual simulation with mosaic difference plots. 

Temperatures in general show a slight degradation in the new simulation, in 

particular in the winter. Wind speed performance improved and wind direction 

improved in the Central and Western United States. Humidity results are almost the 

same in both simulations. The difference in model performance is largely due to the 

inclusion of surface nudging in the previous 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 necessarily 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 domain. 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 degraded 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 16th, 18th, 24th, and 27th and degrade IOA. 

Humidity root mean square 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

 6/11 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

 6/30

 7/ 1

g/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 6/25 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. The 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 National RPO Grid, Presented at the Ad-Hoc 

Meteorological Modeling Meeting, 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 Conservation 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. Alapaty, 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 Analysis 
January 2002
February 2002
March 2002
April 2002
May 2002
June 2002
July 2002
August 2002
September 2002
October 2002
November 2002
December 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

1/11

 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

 1/25

1/26

 1/27

 1/28

1/29

 1/30

 1/31

m/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

 1/10

 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

 1/25 1/26

1/27

 1/28

 1/29

 1/30

 1/31

m/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

 1/11

 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

 1/25

 1/26

1/27

1/28

 1/29

 1/30

 1/31

deg

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

 1/11

 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

 1/25

 1/26

1/27

1/28

 1/29

 1/30

 1/31

deg

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

1/101/11

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

1/161/171/18

 1/19 1/20

 1/21 1/22

1/23

1/24

 1/25

1/261/271/281/29

 1/30

 1/31

K

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

1/101/11

 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

 1/25

1/261/271/281/29

 1/30

 1/31

K

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

1/101/11

 1/12 1/13 1/14

 1/15

1/16

1/17

1/18

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

1/231/24

 1/25

1/261/271/281/29

 1/30

 1/31

g/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

1/101/11

 1/12 1/13 1/14

 1/15

1/16

1/17

1/18

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

1/231/24

 1/25

1/261/271/281/29

 1/30

 1/31

g/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 6/11

 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 6/30

m/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 6/11

 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 6/30

m/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

 6/11

 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

 6/30

deg

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

 6/10

 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

 6/30

deg

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

6/246/25

 6/26

6/27

 6/28

6/29

 6/30

K

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

6/186/19

 6/20

6/21

 6/22

6/23

6/246/25

 6/26

6/27

 6/28

6/29

 6/30

K

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

6/186/19

 6/20

6/21

 6/22

6/23

6/246/25

 6/26

6/27

 6/28

6/29

 6/30

g/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

6/186/19

 6/20

6/21

 6/22

6/23

6/246/25

 6/26

6/27

 6/28

6/29

 6/30

g/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

 7/10

 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

 7/26

7/27

 7/28

 7/29 7/30

 7/31

m/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

 7/10

 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

 7/26

7/27

 7/28

 7/29 7/30

 7/31

m/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

 7/10

 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

 7/26

7/27

 7/28

 7/29 7/30

 7/31

deg

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

 7/10

 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

 7/26

7/27

 7/28

 7/29 7/30

 7/31

deg

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

7/127/137/147/15

 7/16 7/17 7/18

7/197/207/217/22

 7/23 7/24

7/25

 7/26

 7/27

 7/28

 7/29

7/30

7/31

g/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

7/107/11

 7/12 7/13 7/14

 7/15

7/16

7/17

7/18

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

7/237/24

 7/25

7/267/277/287/29

 7/30

 7/31

g/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

7/107/11

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

7/167/177/18

 7/19 7/20

 7/21 7/22

7/23

7/24

 7/25

7/267/277/287/29

 7/30

 7/31

K

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

7/107/11

 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

7/267/277/287/29

 7/30

 7/31

K

ObsTemp 36 km Pred_T 12 km Pred_T

Ohio Valley July 2002
APPENDIX C 

 Performance for Summers 2001, 2002, and 2003 
JUNE 2001
June 2001
JULY 2001
July 2001
AUGUST 2001
August 2001
JUNE 2002
June 2002
JULY 2002
July 2002
AUGUST 2002
August 2002
JUNE 2003
June 2003
JULY 2003
July 2003
AUGUST 2003
August 2003
Upper Midwest High Ozone Da

• 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