### March 4, 2004

Analysis of MM5 Simu lations based on three PBL schemes over the eastern US for

August 6 to 16, 2002

Winston Hao, Mike Ku, and Gopal Sistla

NYS DEC- DAR

Albany, NY 12233

Introductio n: In a prior report 1 

### dated Decem ber 8, 2003, a comparison was

perform e d between m e teorological m easurem ents and the simulated MM5 fields for

August 6 to 16, 2002 based upon 3 approaches to th e PBL. In this report, we provide the

com p arison with TDL and CASTNet m easurem e n ts.

Purpose: The intent of this exercise was to investigate the response of three

PBL sche m e s and develop a recommendation for the use of a PBL m e thod for developing

m e teorological fields for the May through Se ptember of 2002, in support of air quality

modeling work.

Approach: In this study, Prof. Dalin Zhang of University of Maryland, applied

3 PBL schem e s for the August 6 to 16, 2002, a period in which the OTR experienced

high ozone as well as particulate levels. The th ree schem e s were (a ) m odifi ed Blackada r

[BL], (b) the Pleim

- Xiu schem e with the soil m o dule [PX], and (c) m odified Blackadar

with soil m odule [SSIB]. The sim u lated m e te orological fields were com p a r ed to the

m easure m ents from TDL (NW S ) and CASTNet.

Model setup: The MM5 model se tup is sim ilar to th e ear lie r exe r cise of

developing m e teorological fields for July 1997, with the first level at 10 m. The

projection f o r this exercise was that r ecomm ended by the R P Os, and has a spatial

resolution of 12 km (see Figure 1)

Analysis: The basic ap proach used is to com p are dom ain-wide averag ed

m easure m ents and pred ictions for surface te m p erature, wind speed and direction, and

where availa ble with hu m i dity. W h ile the CAST Net sites are m o re representative of rural

areas, the T D L are reflective of urban/s uburban settings. There are 47 CASTNet and

about 600 NWS sites in the TDL da ta set over the modeling dom a in.

TDL data and MM5 simulations:

Average w i nd speed and direction (see Figures 2a through 2c)

1 

### Hao, W ., Ku, M., and Sistla, G. (2002) ‘Pre lim inary analysis of MM5 simulations for

the August 6 to 17, 2002 – A status re port’, NYSDEC, Albany, NY 12233

1

### Overall, the 3 PBL schem e s provide good ag reem ent with the observed average wind

direc tion . In term s of wi nd speed:

BL: Under prediction of daytim e m a xim u m wind speed, but agre e m ent with nighttim e

low windspeed

P-X: System atic under pred iction during daytim e and over pred iction in th e nighttim e

SSIB: Under prediction during daytim e w ith phase lag, the predicted m a xim u m

occurring latter than the m easured maxim u m

Temperatu re (see Figures 3a through 3c)

BL: Good agreem ent throughout the episode days

P-X: Initial over prediction of tem p erature m i ni m u m, and under prediction of daytim e

ma x i mu m

SSIB: Over prediction of daytim e maxi m u m

Humidity (see Figures 4a through 4c)

BL: While the general trend is captured du ring the episode, there is poor agreem ent

between the observed and pred icted diurnal patterns, with the observation showing a

double peak versus one peak based on predictions.

P-X: The m odel yields the observed daily double peak, but with underprediction and a

phase lag.

CASTNet data and MM 5 simula tio ns:

Average w i nd speed and direction (see Figures 5a through 5c)

All 3 PBL approaches provide good agreem ent with the observed average wind direction.

In term s of wind speed:

BL: W i nd speed over prediction during the day tim e, a feature th at differs from the

TDL results, but good agreem ent with nighttim e m i ni m u m

P-X: W i nd speed over pred iction, for both day- and nig h ttim e hours

. SSIB: W i nd speed over prediction at the star t and end of the episode, and exhibiting a

phase-lag of 1 to 2 hours

Average Temperature (see Figures 6a through 6c)

BL: Overall goo d agreem ent

P-X: System atic under pred iction during daytim e and over pred iction in th e nighttim e

with phase lag

SSIB: Over prediction during the daytim e, but good agreem ent during nighttim e

Average Humidity

2

### There were no data to perform this com p ar ison, as m i xing ratio cannot be estim a ted due

to lack of station pressure.

Spatial d i str i bution of c o rrelation between TDL data and MM5 simulations

Wind Speed (see Figures 7a through 7c)

BL: The correlation levels are generally in the 0.7 or higher range over m o st portions

of the dom ai n, with lower values m a inly conf ined to the sou t h eastern and western parts

of the dom ai n.

P-X: The correlation levels are slightly lo wer compared to BL, with m o r e stations

exhibiting a correlation level of less than 0.6 in the Southeastern portion of the dom ain.

SSIB: The correlation le vels ar e sim ilar to P- X, but with increased num ber of stations

exhibiting correlation levels less than 0.6 over the dom ain

Temperatu re (see Figures 8a through 8c)

BL: The correlation levels are generally hi gher (>0.97) over the no rtheastern portions

of the dom ai n, with the rem a inder of the dom ai n exhibiting correlation levels in the range

of 0.94 to 0.96

P-X: Overall the corre lation levels a r e s l ightly lowe r than BL

SSIB: Sim ilar to P-X, with correlati on levels in the 0.95 throughout the dom ain

Humidity (see Figures 9a through 9c)

BL: The corre lation leve ls ov er the northeast are generally higher than the rest of the

dom a in, although m o st portions of the dom ain report correlation of 0.70 or higher

P-X: The correlation levels are com p ar able or slightly bette r than BLK

SSIB: The correlation levels are com p ar atively lower than the other two over the

northeastern portions of the dom ain

Discussion and conclusions

On an overall basis, it appear s that the BL scheme exhibits a better correspondence to the

m easured data than the other two schem e s. The e x ception being the poor capture of the

observed diurnal pattern of hum i dity in the c a se of the BL schem e . W h ile the P-X sch e m e

shows a better correspondence with the observe d diurnal pattern for hum idity, it f a ils to

perform well for wind speed and tem p erature. Further work is needed to improve the

perform a nce of these m e thods. An exam ination of other studies in which the P-X schem e

was applied suggests the predictive pe rform a nce is sim ilar to this study.

Other com p arisons of model to observed or m easured parameters such as cloud cover,

precipitation, and upper air soundings/profiler network are under exam ination to provide

a com p rehensive evaluation of the m e teorol ogical m odel. Also, the use of the m o del

sim u lated fields in air quality m odel and co m p arison to pollutant fields is also in

progress.

3

### Figure 2a M M 5 Simulat i on - U M D B L K & TD L - A ug 6 01Z t o A ug 17 00Z 2002

Obs e r v e d/P r e d ic te d Winds pe e d

0 1 2 3 4 5 6

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Obs W ndSpd Pr dW ndSpd

Bias W i n d s p eed

-1 -0. 5

0 0. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Bi as W ndSpd

RM S E Winds pe e d

0 0. 5

1 1. 5

2 2. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s R M SEW ndSpd R M SESW ndSp R M SEU W ndSp

IOA Winds pe e d

0 0. 2 0. 4 0. 6 0. 8

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AW ndSpd

O b serv e d / Pred ict e d W i n d Direct io n

0 60 120 180 240 300 360

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg O b sW n d Dir Pr dW ndD i r

Bias W i n d Direct io n

-90 -60 -30

0 30 60 90

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg Bi as W ndD i r



### 4

### Figure 2b MM5 Simulation - UMD PX & TDL Aug 06 01Z to Aug 17 00Z 200 2

5

### Figure 2c M M 5 Simulat i on - U M D SSIB & TD L A ug 06 01Z t o A ug 17 00Z 2002

Obs e r v e d/P r e d ic te d Winds pe e d

0 1 2 3 4 5 6

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Obs W ndSpd Pr dW ndSpd

Bias W i n d s p eed

-1. 5

-1 -0. 5

0 0. 5

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Bi as W ndSpd

RM S E Winds pe e d

0 0. 5

1 1. 5

2 2. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s R M SEW ndSpd R M SESW ndSp R M SEU W ndSp

IOA Winds pe e d

0 0. 2 0. 4 0. 6 0. 8

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AW ndSpd

O b serv e d / Pred ict e d W i n d Direct io n

0 60 120 180 240 300 360

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg O b sW n d Dir Pr dW ndD i r

Bias W i n d Direct io n

-90 -60 -30

0 30 60 90

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg Bi as W ndD i r



### 6

### Figure 3a MM5 Simulation - UMD BL & TDL Aug 6 01Z to Aug 17 00Z 2002

7

### Figure 3b M M 5 Simulat i on - U M D PX & TD L A ug 06 01Z t o A ug 17 00Z 2002

O b serv e d / Pred ict e d T e mp erat u r e

285 287 289 291 293 295 297 299 301 303 305

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Obs T em p Pr dT em p

Bias T e mp erat u r e

-3 -2 -1 0 1 2 3 4

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Bi as T e m p

RM SE T e mp erat u r e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K R M SET em p R M SEST em p R M SEU T e m p

IO A T e mp erat u r e

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AT em p



### 8

### Figure 3c M M 5 Simulat i on - U M D SSIB & TD L A ug 06 01Z t o A ug 17 00Z 2002

O b serv e d / Pred ict e d T e mp erat u r e

285 287 289 291 293 295 297 299 301 303 305

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Obs T em p Pr dT em p

Bias T e mp erat u r e

-2 -1. 5

-1 -0. 5

0 0. 5

1 1. 5

2 2. 5

3 3. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Bi as T e m p

RM SE T e mp erat u r e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4 4. 5

5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K R M SET em p R M SEST em p R M SEU T e m p

IO A T e mp erat u r e

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AT em p



### 9

### Figure 4a M M 5 Simulat i on - U M D B L & TD L A ug 6 01Z t o A ug 17 00Z 2002

P r e d ic te d/Obs e r v e d Humidity

8 9 10 11 12 13 14 15 16

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg ObsH um Pr dH um

Bias Humidity

-3 -2. 5

-2 -1. 5

-1 -0. 5

0 0. 5

1 1. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg Bi as H u m

RM SE Humidity

0 0. 5

1 1. 5

2 2. 5

3 3. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg R M SEH um R M SESH um RM S E UHu m

IO A Humidity

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AH um



### 10

### Fi gure 4b M M 5 Simul a t i on

- UM D PX A ug 06 01Z t o A ug 17 00Z

Pr edicted/O b s e r v ed H u midit y

8 9 10 11 12 13 14 15 16

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 g/

kg Ob s H u m P r dHum

Bia s Hum i d i t y

-1. 5

-1 -0. 5

0 0. 5

1 1. 5

2

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 g/

kg Bi a s H u m

RM SE Hum i dit y

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 g/

kg R M SEH u m R M SESH u m RM S E UHu m

IO A Humidit y

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 IOA H u m



### 11

### Figure 4c M M 5 Simulat i on - U M D SSIB & TD L A ug 06 01Z t o A ug 17 00Z 2002

P r e d ic te d/Obs e r v e d Humidity

8 9 10 11 12 13 14 15 16

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg ObsH um Pr dH um

Bias Humidity

-3 -2. 5

-2 -1. 5

-1 -0. 5

0 0. 5

1 1. 5

2

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg Bi as H u m

RM SE Humidity

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4 4. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16g/kg R M SEH um R M SESH um RM S E UHu m

IO A Humidity

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AH um



### 12

### Figure 5a M M 5 U M D

- B L & C A STN e t A ug 6 01Z t o A ug 17 00Z 2002

Obs e r v e d/P r e d ic te d Winds pe e d

0 1 2 3 4 5 6

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Obs W ndSpd Pr dW ndSpd

Bias W i n d s p eed

-0. 5

0 0. 5

1 1. 5

2

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Bi as W ndSpd

RM S E Winds pe e d

0 0. 5

1 1. 5

2 2. 5

3

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s R M SEW ndSpd R M SESW ndSp R M SEU W ndSp

IOA Winds pe e d

0 0. 2 0. 4 0. 6 0. 8

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AW ndSpd

O b serv e d / Pred ict e d W i n d Direct io n

0 60 120 180 240 300 360

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg O b sW n d Dir Pr dW ndD i r

Bias W i n d Direct io n

-90 -60 -30

0 30 60 90

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg Bi as W ndD i r



### 13

### Figure 5b M M 5

- U M D PX & C A STN e t A ug 06 01Z t o A ug 17 00Z 2002

Obs e r v e d/P r e d ic te d Winds pe e d

0 1 2 3 4 5 6

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Obs W ndSpd Pr dW ndSpd

Bias W i n d s p eed

-0. 5

0 0. 5

1 1. 5

2

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Bi as W ndSpd

RM S E Winds pe e d

0 0. 5

1 1. 5

2 2. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s R M SEW ndSpd R M SESW ndSp R M SEU W ndSp

IOA Winds pe e d

0 0. 2 0. 4 0. 6 0. 8

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AW ndSpd

O b serv e d / Pred ict e d W i n d Direct io n

0 60 120 180 240 300 360

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg O b sW n d Dir Pr dW ndD i r

Bias W i n d Direct io n

-90 -60 -30

0 30 60 90

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg Bi as W ndD i r



### 14

### Figure 5c M M 5

- U M D SSIB & C A STN e t A ug 06 01Z t o A ug 17 00Z 2002

Obs e r v e d/P r e d ic te d Winds pe e d

0 2 4 6

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Obs W ndSpd Pr dW ndSpd

Bias W i n d s p eed

-1. 5

-1 -0. 5

0 0. 5

1 1. 5

2

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s Bi as W ndSpd

RM S E Winds pe e d

0 0. 5

1 1. 5

2 2. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16m/s R M SEW ndSpd R M SESW ndSp R M SEU W ndSp

IOA Winds pe e d

0 0. 2 0. 4 0. 6 0. 8

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AW ndSpd

O b serv e d / Pred ict e d W i n d Direct io n

0 60 120 180 240 300 360

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg O b sW n d Dir Pr dW ndD i r

Bias W i n d Direct io n

-90 -60 -30

0 30 60 90

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16deg Bi as W ndD i r



### 15

### Figure 6a M M 5

- U M D B L & C A STN e t A ug 6 01Z t o A ug 17 00Z 2002

O b serv e d / Pred ict e d T e mp erat u r e

285 287 289 291 293 295 297 299 301 303 305

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Obs T em p Pr dT em p

Bias T e mp erat u r e

-2 -1. 5

-1 -0. 5

0 0. 5

1 1. 5

2 2. 5

3

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Bi as T e m p

RM SE T e mp erat u r e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K R M SET em p R M SEST em p R M SEU T e m p

IO A T e mp erat u r e

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AT em p



### 16

### Figure 6b M M 5

- U M D PX & C A STN e t A ug 06 01Z t o A ug 17 00Z 2002

O b serv e d / Pred ict e d T e mp erat u r e

285 287 289 291 293 295 297 299 301 303 305

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Obs T em p Pr dT em p

Bias T e mp erat u r e

-3 -2 -1 0 1 2 3 4

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K Bi as T e m p

RM SE T e mp erat u r e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4 4. 5

5

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16K R M SET em p R M SEST em p R M SEU T e m p

IO A T e mp erat u r e

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8/ 7 8/ 8 8/ 9 8/ 10 8/ 11 8/ 12 8/ 13 8/ 14 8/ 15 8/ 16 I O AT em p



### 17

### F i g u r e 6c M M 5 - U M D S S I B & C A

S T N e t A u g 06 01Z t o A u g 17 00Z 2002

O b se r v e d / P r e d i ct e d Tem p er a t ur e

28 5 28 7 28 9 29 1 29 3 29 5 29 7 29 9 30 1 30 3 30 5

8/ 6 8/ 7 8 / 8 8/ 9 8/ 10 8 / 11 8 / 12 8/ 1 3 8/ 14 8 / 15 8/ 16K Ob s T e m p P r dT e m p



### B i as Tem p erat ur e

-1

. 5

-1 -0

. 5

0 0. 5

1 1. 5

2 2. 5

3 3. 5

8/

6 8 / 7 8/ 8 8/ 9 8 / 10 8 / 11 8/ 12 8/ 1 3 8/ 14 8 / 15 8/ 16K Bi a s T e m p



### R M S E T e m p er at ur e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

4

8/

6 8 / 7 8/ 8 8 / 9 8/ 1 0 8 / 11 8/ 12 8/ 13 8/ 14 8/ 15 8 / 16K R M SET e m p R M SEST e m p R M SEU T e m p



### IO

A

T e m p e r a t u r e

0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

8/ 6 8 / 7 8/ 8 8 / 9 8/ 1 0 8 / 11 8/ 12 8/ 13 8/ 14 8/ 15 8 / 16 IO

A T e m p



### 18

### F i g u re 7a Spatial Correla tion – W i nd speed – BL & TD L

19

### F i g u re 7b Spatial Corr elation – W i nd Speed – P- X & TD L

20

### F i g u re 7c Spatial Correl a tion – W i nd Speed SSI B & TD L

21

### F i g u r e 8a Spa tia l Cor r e l a ti on – Temperatur e – BL & TD L

22

### 23 F i g u r e 8b Spa tia l Cor r e l a tio n – Tem p eratur e – PX & TD L

### F i g u r e 8c Spa tia l Cor r e l a ti on – Temperatur e SS I B & TD L

24

### F i g u r e 9a Spa tia l Cor r e l a tion - Humidit y BL & TD L

25

### F i g u r e 9b Spa tia l Cor r e l ation – Humidit y PX & TD L

26

### F i g u r e 9c Spa tia l Cor r e l a tion – Humidit y SS I B & TD L

27

## An Assessment of a n MM5 Simulation of July 6 to 19, 1997 , in the Eastern United States

Go pal Si st l a

, M i ke K u

, an d W i nst o n Ha o

New Y o r k St at e De part m e nt of En vi r o nm ent a l C onse r vat i o n

October 2002

1As part o f t h e OTC ur ba n/ reg i onal ph ot o c he m i cal m odel i ng wo rk

, gri dde d m e t e orol o g i c al dat a we re si m u l a t e d by

th e Un iv ersity o f Maryland , C o lleg e Park (UMD) with sup p ort fr om t h e M a ry l a nd St at e Depa rt m e nt of En vi ro nm ent

(M DE ) usi n g t h e NC AR / P S U m e so-scal e m odel M M 5 ver s i o n 3.3. T h e goals of t h is effort was t o c r eate a

m e t e orol o g i cal dat a ba se f o r t h e sum m er ‘9 7 s easo n f o r use wi t h vari ous p hot ochem i cal gri d m odel s s u c h as C M AQ

,

CAMx , and o t h e rs to sim u lat e and assess air qu ality o f o z on e an d its precu r so rs. Th e d e sig n of th is MM 5 m o d e lin g

fram e wo rk is si m i lar to th at utilized b y USEPA, State of New York, State of North Caro l i n a and o t h e rs. Th is

analysis is aimed at a d dressi ng que stions s u c h as ‘ how g o o d ’ are t h e m e t e orol ogi cal para m e t e rs gene rat e d by M M 5

whe n c o m p are d t o t h e m easur ed dat a

. I n pri n ci pl e s u c h a n i ssue s h o u l d be m oot gi ve n t h e fact t h at M M 5 si m u l a t i on

u tilizes o b s ervatio n a l nudg ing

. Ho w e v e r, i n practice su ch a nu dg ing is weak an d is o f ten b a sed u pon po rtion s of th e

ro ut i n e dat a m easure d t h r o ug h t h e Nat i o nal Weat he r Se rvi ce (NWS), and th erefore it is cr itical to unde rtake s u c h a

co m p ariso n wi th th e NWS d a ta as well as o t h e r n e t w orks t h at are availabl e. In t h is study we unde rtook s u ch an

assessm ent of M M 5 si m u l a t i on co ve ri n g t h e hi g h oz one e v ent o f J u l y ‘ 9 7 wi t h m easure m ent s fr om NC AR TD L dat a

set (ds472.0) a n d from the Clean Air Status and Tre n ds Ne two r k (CA S T N et). T h e re sults su gg est t h at th ere m a y

b e a n e ed for fu rt h e r research and im p r ov emen ts in ex erci sin g MM5 for air qu ality related ap p lication s

.

Mo del De sign

The M M 5 desi gn co nsi s t s of 2- nest ed g r i d s at 36 an d 1 2 k m hori z o n t a l g r i d res o l u t i o n p r o j ect ed o n La m b ert

C o n f o r m a l C oor di nat e (LC C ) sy st em wi t h t h e g r i d ce nt ere d at

4 0 N an d 9 0 W a n d t h e pa ra l l e l s at 30 a n d 60 N. T h e

v e r tical stru ctur e co m p r i sed of 2 5 layer s an d ex tend ing up t o 5 0 m b o r abou t 20k m

, w ith 8 lev e ls b e l o w 1 500 m

.

Details o f th e m o d e l setu p are prov id ed at ft p ://www .state.n y.u s /air_research / h t do cs/mm5 -u m d -setup .h tm

.

Model Simul a tion

The si m u l a t i o n s we re per f o r m e d wi t h a ‘m odi fi ed ’ B l ack d a r PB L schem e an d st a nda rd n u d g i n g p r oces s

. T h e

nu d g i n g dat a c o m p ri sed of t h e anal y s i s fi el d s en ha nced wi t h obs er ved wi n d fi el d a n d t e m p erat ure dat a t a ken f r om

t h e NC E P ’s A D P a n d NC AR ’s N W S/ TDL net w or k. T h e det a i l s are p r ov id ed in th e att achm e nt (Zhe n g 20 0 2 )

. T h e

m o d e l si m u lati o n s were con d u c ted i n step s o f th ree and h a lf d a ys with t h e in itial 1 2 h con s id ered as ram p -u p tim e fo r

a to tal of f

i v e per i od s co v e r i ng Ju ly 6 to 19

.

2The success of a sim u lat i on is ofte n asse ssed by its ability to re produce observ e d varia b les, and i n this ca se we

restricted t o the surface m e te orological data , as t h ey are a v ailable m u ch m o re fre que ntly, eve r y hour, com p ared t o

the upper air m easurem ents which a r e a b out 12 h apa r t. A s e ries of tests were perform e d to select a PBL schem e

following t h e determination that the existing PBL sc hem e s in MM5 di d not yield consiste nt s u rface wind s p ee d

d i ur n a l v a r i ation co m p ar ed t o th e ob ser v ati o ns (K u 200 2)

. T h is has lead to the use of a m odified Blackda r PBL

schem e

( Zh ang 20 02)

Model Assess ment

The data base for this asse ssm ent com p ri se d of m easured h o u r l y wi nd spe e d a n d di rection, tem p erature, available

fr om t w o net w or ks, vi z, NC A R ’s N W S/ T D L arc h i v e ds 47 2. 0 wi t h st at i o ns t h at are oft e n l o cat ed nea r ur ban areas

,

an d C A STNet with sites th at are m o stly lo cated in ru ra l areas (h ttp ://www.ep a.go v / cast n et ). Th e MM5 d a ta fo r th is

anal y s i s we re s i m u l a t e d at 1 2 km hori z ont al gri d re sol u t i o n co rresp ond ing to layer 1 th at i s set at a h e ight o f abou t

10m . The TDL te m p erature a n d wi nd m easurem ents were a t 2m and 10m hei g ht

, respect i v el y

, w h i l e i n t h e case o f

CAST Net the t e m p erature a n d wind a r e h ourly avera g es , measure d at 9m and 10 m

, resp ectiv ely. In th is assessm en t

th e MM5 grid v a lu es were b i -lin early in terp olated to t h e m o n ito r lo cation i n th e horizon tal fro m th e fou r

su rroun d i ng g r id s, wh ile no su ch in terpo l atio n was performed in th e v e rt ical. Th e d a ta were pro c essed and statistical

p a ram e ters were esti m a ted u s i n g METSTAT, a pro cesso r availab l e with C A Mx at ftp ://ft p .en v i ron

. o r g

.

Com p ari s on w i th T D L d a t a

Fi gu res 1 t o 3 di spl a y t h e di u r nal vari at i o n o f t h e m easured and p r e d i c t e d m e t e orol o g i cal pa ram e t e rs average d o v er

th e do m a in th at co m p rised o f ab ou t 7 0 0 statio n s in th e TDL d a tab a se. The statistics co mp u t ed are th e bias, ro o t

m ean sq uare er ro r (RM S E) , a n d in dex o f a g r e em ent (I OA ). Vi sual e x am i n at i on of Fi g u re 1 s h ow s t h at t

h ere i s ve ry

go o d a g reem ent bet w ee n m easure d a n d pre d i c t e d t e m p eratures

, wi t h t h e i nde x o f a g ree m ent aro u n d 0

. 9 or bet t e r.

Ho we ver

, t h e b i as su gge st s t h at t h ere i s a di u r nal l y va ry i n g un de rp redi ct i o n i n t h e 1 t o 1. 5 K r a n g e d u ri ng t h e

d a yti m e. Th e RMSE p a n e l disp lays th e syst e m atic (R MSES) and u n s ystematic (RMSEU) alon g with th e o v e rall

RMSE, wh ich sh ows th at th e h i gh er lev e l o f erro r o c cu r dur in g t h e d a y time th an in t h e n i g h t tim e. Th is ap p e ars t o

3arise m a in ly fro m th e syste m atic p a rt o f th e erro r, su gg esti ng t h at t h i s pe r h ap s ca n be m i ni m i zed f u rt he r. Not e , t h e

u n s ystem a tic e r ro r is also contrib u ting sim i l a rly, with a m a x i m u m in th e nig h tti m e h o u r s. Th e ind e x of ag reem en t

(I OA ) s h ows e ssent i a l l y a co nst a nt l e vel ar o u n d 9 5 % or s o

, i ndi cat i n g a n ove ral l ag reem ent bet w een m odel

estim a tes and measured s u rfa ce te m p erature s . Figure 2 dis p la ys th e statistics o f wind sp eed a n d di rection ave r a g ed

ove r t h e dom ain. I n gene ral

, t h e m easured p eak wi n d s p ee d i s hi g h er t h a n t h at p r edi c t e d wi t h a sl i ght pha se of a b out

1 t o 2 h o u rs t h e p r edi c t i o ns l a ggi ng t h e o b se rvat i o ns a n d b i as vary i n g fr o m 0 t o - 1

. 0 m / s du ri n g t h e day t i m e

reachi n g the m a xim u m in the afternoon tim e. The RMSE pa nel indicates that the bulk of t h e bias is perhaps

associated with the unsyste m a tic error (R M S EU)

. H o we ve r

, i n t h e case o f wi n d di rect i o n, i t appea r s t o e xhi bi t t h e

l east bi as o v e r t h e si m u l a t i on peri od . Fi gu re 3 c o m p ares t h e m easured a n d pre d i c t e d h u m i di t y vari a b l e

, m i xi ng rat i o

,

agai n a v e r age d o v er t h e m ode l i ng dom ai n. Whi l e t h e m e asure d dat a s h o w s a shal l o w m o rni n g di p a n d a n a f t e rn o o n

r i se fo llow e d by a sh allow d i p and r i se tow a rd s t h e en d of t h e day

, t h e p r e d i c t e d dat a oft e n e xhi bi t s an aft e rn o o n

min i m u m fo r each d a y. Th is i s reflected in th e b i as w ith the un d e rp red i ctio n exh i b iting a m a x i m u m aro u n d m i d

- d a y

by as m u ch as 3. 0g/ kg

. The R M SE pa nel al so s h ows t h e s a m e feature wi th the syste m atic error contri buting m a jor

po rt i o n of t h e un de rp redi ct i o n.

The a b ove co m p ari s on of t h e TDL dat a d o e s n o t pr ovi de any i n fo rm at i o n as t o h o w t h e m odel has fare d s p at i a l l y

ove r t h e dom ain. Thi s was ac com p l i s hed by exam i n i ng t h e l e vel o f c o r r el at i on at eac h m oni t o r f

o r

t h e t h r ee

meteo r o l og ical p a ram e ters an d th e sp atial correlatio n ar e d i sp layed i n Fi gu r e s 4 thr oug h 6

. In th e case of

te m p eratu r e, the correlation is qu ite h i g h (> 0

. 9 ) (s ee Fi gu re 4) an d vi sual ex am in atio n shows that th ere is no

geographical bias. In t h e case of wi nd sp eed, th e co rrelatio n v a lu es ex h i b it a wi d e r a ng e (see Figu r e 5) w ith so m e o f

the m onitors at < 0.3. It also a ppea r s t h at the r e is s o m e t e nd ency t o war d s h i ghe r deg r ee of co rrel a t i o n i n t h e up pe r

hal f of t h e d o m a i n com p are d t o t h e l o wer hal f . Fi g u re 6 di s p l a y s t h e co rrel a t i on ac ros s t h e dom ai n f o r t h e m i xi ng

ratio (hu m id ity ) p a ram e ter, wh ich also v a ry fro m < 0

. 3 to >0 .9 , and ex h i b it a d i stin ct g e og raph ical d e m a rcatio n with

h i gh er correlatio n v a l u es b e ing asso ciated wi th m o n ito rs i n t h e u p p e r-h a lf of th e do m a in

.

Com p ari s on w i th C A ST Ne t d a t a

Sim i l a r t o t h e TDL a n al y s i s

, Fi gu res 7 a n d 8 di spl a y dom ai n ave r a g ed di ur nal f o r m easure d a n d pre d i c t e d

t e m p erat ure

, w i nd spee d a n d di rect i o n base d o n a b o u t 50 m oni t o ri n g si t e s. Si nce t h ere i s no m easured st at i on

press u re, m i xi ng rat i o s we re not cal c u l a t e d, even t h ou g h relativ e h u m id ity is av ailab l e at th ese m o n itors. Th e

4statistical parameters conside r ed are bias, root m ean sq uare err o r (RM S E) and in de x of a g reem ent (IOA), whic h

were com put ed usi n g t h e M E TSTA T pr og ra m

, as befo re. In the case of te m p erature (se e Fig u re 7 ) whil e the

obs erved and predicted di urna l avera g e tem p erature exhi bits a g ood ag r e emen t, th e b i as w a s f oun d to var y fr o m

- 1

to 2

. 5 K with ov er pred iction i n th e late aftern oon an d und er p r ed ictio n dur ing ear ly m o rn ing h ours. Th i s is in

o ppo site d i rectio n t o th e TDL co m p arison

. Ex am in in g th e R M SE pa nel s h o w s t h at t h e R M SE i s hi ghe r du ri n g t h e

l a t e aft e rn o ons coi n ci dent wi t h t h e ove r pre d i c t i on a n d i s c o m p ri sed m o st l y of sy st em at ic err o r (R M S E S ). Thi s

featu r e th at t h e error is arisin g fro m syste m a t ic erro r po rtion of th e RMSE i s con s isten t wi th th at o f th e TDL

co m p ariso n

, bu t th e tim e o f occurrence of t h e m a xim u m error differs bet w een t h e t w o dat a set s . T h e i n d e x of

agreem ent (I O A ) f o r t e m p era t ure i s a r ou n d 0. 9 fo r m o st p o r t i ons o f t

h e si m u l a t i on, i ndi c a t i ng t h at t h e m odel

p e rform s q u ite well fo r t h is p a ram e ter. Ex amin in g Figur e 8 sh ows th at th e p r ed icted wind sp eed s are g e nerally

h i gh er th an th e m easu r ed sp eed b y abou t 0.5 to 1 m / s w ith the m easured peak occ u rring in adva nce of the m odel

p r ed icted m a x i m u m

. Ex a m in i n g th e b i as, exh i b its th e ov er p r ed ictio n of t h e m a x i m u m

, d i am e t rically d i fferen t fro m

the com p aris on of the TDL da ta (see Fi gure 2). T h e RMSE p a n e l exh i b its t h e system atic ( R MSES) and un system a tic

(RM S EU ) e r r o rs, with t h e sy s t em atic error g e nerally in t h e less than 1

. 5 m / s. The in dex o f a g reem ent (I OA ) fo r

wind s p e ed is in fair agreem e n t av erag ing ab ou t 0

. 7

, qu ite si m ilar to th e co m p ariso n wit h th e TDL d a ta. Ex am in in g

t h e wi nd di rect i on pa nel s s h o w t h at t

h e resul t s are si m i l a r to t h ose o f TD L dat a

.

We h a v e also ex am in ed on a sp atial b a sis t h e lev e l of co rrel ation at eac h of these m onitors and a r e displa yed in

Fi gu res 9 a n d 10 f o r

t e m p erat ure a n d wi n d s p eed

, respect i v ely. W h ile t h e d i stribu tio n of th e m o n ito rs is no t as

sp atially in tensiv e as t h at of TDL, a h i gh deg r ee of co rr el atio n

, > 0.9, is fo und for th e te m p er atu r e. Ho w e v e r

, i n th e

case of wind sp eed th e lev e l o f correlation i s con s id erab ly lo wer an d v a ries ov er th e domain

, similar to th at of TDL

(see Fi gu re 5)

. Al so, t h e r e a p p ears t o be sl i g h t l y hi ghe r l e vel o f c o r r el at i on i n t h e u ppe r- ha l f o f t h e d o m a in c o m p ared

to th e l o wer-h a lf.

On an ov erall b a sis, t h e C A STNet d a ta exh i b it slig h tly lower co rrelatio n t h an th e TDL data set wh ich is no t

une xpected since the form er were not part o f t h e dat a base use d f o r nu d g i n g

.

Discussion

Th e co m p arison s p e rfo rm ed in th is stud y are un iqu e in th at t h e com p ari s on i s m a de bot h wi t h m easured dat a f r om

5TDL t h at ha d b een use d t o d o obs er vat i onal n u d g i n g a n d wi t h dat a t a ke n fr om C A STNet t

h at we re n o t pa rt o f t h e

n udg ing

. Th e qu alitativ e resu lts sho w th at the m o d e l p r ed ictio n s are q u ite si m ilar fo r bo t h sets of d a ta, an d in term s

o f

sp atial correlatio n th e m o d e l ten d s to exh i b it slig h tly bett er c o rresponde nce for wind s p eed ove r portions of t h e

up pe r- hal f of t h e dom ai n t h a n i n t h e l o we r- hal f

. I n t h e cas e of m i x i n g ratio (relativ e h u mid ity), th e mo d e l app ears to

cap ture th e early m o rn ing d i p, bu t fails to exh i b it th e m i d

- m o rn in g rise seen in th e ob serv ed d a ta, bu t ap p a ren tly

track s fairly well th e ov er-all p a ttern of increasin g trend ob s e rve d d u rin g t h is peri od

. The s e com p arisons show, that

the MM5 sim u lation for s u rfa ce te m p erature

, wi nd speed a n d direction a r e com p arable with m easured data, a n d

per h a

p s c oul d be st re n g t h e n e d wi t h ot he r a d di t i onal ass e ss m e nt s.

Other com p ar i s ons an d i ssu es

Ad di t i onal ass e ssm ent s t h at can be per f o r m e d but we re not i n cl ude d i n t h i s st u d y are t h e cl ou d c ove r a n d

preci pi t a t i on. I n t h e case of t h e f o rm er, com p ari s o n ca n be p e rf orm e d i n di r ect l y bet w een gr o u n d l e vel p hot osyn t h e tically activ ated rad i atio n (PAR) deri v e d fro m satell ite i m ag es an d th at esti m a ted fro m th e MM5 sim u lat i o n

.

In t h e case o f p r eci pi t a t i on, t h e com p ari s o n c a n be per f o r m e d bet w ee n NC AR arc h i v e d s 50 5. 0 c o nsi s t i n g of h o u rl y

d a ta to t h at p r ed icted b y MM5

. It is an ticipated th at w ith t h ese typ e s of ad d ition a l co mp ariso n s b e tween p r ed icted

and m easured data, it perhaps woul d be possible to reach a better decision on the a b ility of MM5 t o sim u late the

o b s erv e d m e te o r o l og ical p a rameters wh ich are critical fo r ai r qu ality si m u latio n effo rt. In add itio n we wo u l d lik e t o

note t h at in some instances t h ere are sha r p spat i a l gra d i e nt s bet w een t h e c o m put ed m i xi ng hei g ht s, es pe ci al l y duri n g

t h e n o n

- d ay l i g ht h o u rs

. An e x am pl e of t h i s i s di spl a y e d i n Fi gu re 1 1 i n di c a t i ng t h e est i m at ed m i xi ng he i ght s at a

b out

2 200 EST. Wh ile we h a d no t yet in v e stig ated th e cause o f t

h ese ex cu rsi o ns, it is im p o r tan t to d e v e lop an d app l y

di ag no st i c t ool s f o r achi e vi n g bet t e r m e t e oro l ogi cal fi el d s f o r

ap pl i cat i on i n ai r p o l l u t i o n pr o b l e m s

.

In Fi g u re 1 2

, we di spl a y t h e com posi t e an d avera g e o b ser v ed a n d p r e d i c t e d di u r nal cy cl e s o f t h e wat e r vap o r m i xi ng

r a tio b a sed upon th e seaso n a l ( J un e, Ju ly, Aug u s t

, 19 95 ) si m u la tio n using RA MS and M M 5 at a g r i d spacin g of

36 km cove ri n g t h e east e r n U n i t e d St at es ( H o g re fe 20 0 2 )

. It i s i n t e rest i n g t o not e t h at bot h m odel s are f o u n d t o

p r ed ict th e ob ser v ed do ub le peak stru ctur e, su gg esting t h at th ere is a n e ed fo r fu rth e r assessm en t o f th is prob lem

.

6Nex t S t eps

The m e t e orol o g i cal dat a a r e b e i ng p r oces sed t o devel o p i n p u t s t o t h e p hot ochem i cal m odel s . The i ssue s suc h as t h e

m i xi ng hei g ht

, m i xi ng rat i o

, r e qui re f u rt her di ag no si n g of t h e m odel

. T h e pr o b l e m wi t h t h e m i xi ng rat i o o f w a t e r

vap o r a

ppea r s t o e x t e n d t o ot h e r pr og n o st i c m odel R A M S as wel l

, s u gge s t i ng t h e ne ed f

o r

care f ul as se ssm ent of t h e

code s. Whi l e t h ese a r e c r i t i cal com pone nt s f o r

de vel o pi n g r e l i a bl e m e t e orol o g i cal fi el ds

, t h ei r rol e o n t h e est i m at ed

air qu ality n e ed s furth e r assessm en t as ex ercises are un d e rtak en with t h e ‘im p ro v i ng’ meteo r o l og ical in pu ts i n

appl i cat i o n of ph ot oc hem i cal m odel s

.

Refere nces

H o g r ef e, C (200 2) Pri v ate communication

Ku, M., et al. (2002) Pres enta tion at the AMS, AW M A Jo i n t m eetin g at Virg in ia Beach, VA.

Zh an g, Dalin (2 002 ) Presen tatio n to MDE, Balti m o re, MD

Zhe n g, W (2002) See attache d

7

### Attachment



## Numerical Simulati ons of the Oz one Episodes of 5 - 20 Jul y 1997

Weizho ng Zh en g , Dep a rtm e n t of Meteoro l o g y , Un iv ersity o f Maryland at Co lleg e Park , C o lleg e Park , M D

Th e MM 5 simu latio n s were started fro m 1 2 0 0 UTC Ju ly 5 to 0 000 UTC 2 1 Ju ly 19 97 , an d th ey are re-i n itialized

every 3. 5 day s

. The ne xt cy cl e st art s on day 3 o f t h e pr ev i o us cycle, pr ov id i n g a 12

- h ove rl ap bet w ee n t w o

consecutive cycles.

1. M odel desc ri pt i on.

The ver s i o n 3. 3 of M M 5 i s u s ed fo r t h ese si m u l a t i ons. T h e d o m a i n and ve rt i cal l e vel s are de fi ne d t h e sa m e as

pre v i o usl y re p o rt e d

. The m a in physical schem e s used i n these runs i n clude:

(a) The l a t e st v e rsi o n of Kai n

- F ri t s ch c o nvect i v e sc hem e used fo r bot h 36

- and 1 2

- km res o l u t i o n dom ai ns;

(b ) An ex pl i c i t m o i s t u re sche m e (wi t h out t

h e m i xed pha se) co nt ai ni n g pr o g n o st i c e quat i o ns fo r cl ou d w a t e r (i ce) an d

rain water (sn o w );

(c) The Blacka d ar pla n etary boundary layer (PBL) sc hem e ;

(d) A sim p le rad i ativ e coo ling sch e m e ;

(e) A m u lti-layer soil m odel to pre d ic t land surface tem p eratures usi n g th e surface e n ergy budget equation.

The PBL sc he mes we tested i n clude the followi ng 5 sc hem e s: t h e Gay n o

- S eam an TKE s c hem e , B u rk -T hom pso n

, Black ad ar, MR F, an d Miller-Yam a d a -Janj i c. On ly th e no n- lo cal sch e m e of th e Black ad ar PBL p a ram e te rizatio n can

reproduce the right diurnal cy cle of Vs fc, while the othe r sc hem e s tend to produce to o weak s u rface wi nds during

the daytim e and too strong s u rface winds ne ar m i dnight. Howe ve r, t h e ori g inal Blacka d a r sc hem e tends to s h ow too

well-m i x e d m o men t u m in th e PBL and t o o fast up ward tran sp ort o f m o istu re. After so m e m o d i ficatio n s o f th e

Blackadar sc he me by cha ngi ng the m a ss exchange betwee n the s u rface lay e r a n d the laye rs a b ove, the s c hem e is

able to produc e reas ona ble diurnal cycle of Vsfc , Ts fc a n d surface relative hum i dity. The s e cha n ges a r e give n as

fo llows

(a) The K-coe f ficient is dete rmined by t

h e R i chardson number accordi n g to Z h a n g & Anthes (1982), where the

critical Richards on num b er is set to be 0

. 25 rath er th an to be calcu lated with S h i r a n d B o r n st ei n m e t hod (1 9 7 7 )

. A n d

th e leng th l is set to b e d e p t h o f th e m o d e l layer.

(b) Using po ten tial te m p erature rat h er th an virtu a l po ten tial te m p erature t o calculate the bulk Ric h ards on num b er

Rb

. (c) W e i ght i n g fu nct i o n i s use d not o n l y f o r m o m e nt um but al so fo r p o t e nt i a l t e m p erat ure an d m i xi ng rat i o

.

2. Nudging Processes

The dy nam i cal n u d g i n g i s use d fo r eac h dom ai n. T h e o b ser v ed u

p p er -ai r wi n d , t e m p erat ure an d m i xi ng rat i o fi el ds

are nudged eve r y 6 h, while the surf ace wi nd and tem p erature field a r e n udged eve r y 3 h, but t h e tem p erature

(exce p t for at t h e s u rface) and the m i xing ra tio field is e x cluded the PBL nudging.

3

. M o d e l in itializatio n

Th e m o d e l is i n itialized b y usin g NCEP's Et a m o d e l an alysis as a first g u e ss th at is t h en en h a n c ed b y observ a tion s at

the upper level s and t h e s u rfa ce.

(a) NCEP ’s A D P glo b al up p e r-air o b se rvati o n s ( N C A R ar chiv e ds 353.4) are used to furt her enha nce the uppe r-le v el

Eta analysis.

(b) The followi ng two sets of surface observations ha ve be en int r oduced into t h e m odel initializat ion to i m prove the

Eta analysis of surface winds and s u rface te m p erature fields:

• NCE P ' s ADP global s u rface wind obse rvations (NC A R archive ds 464.0). T h is dataset pr ovides 6-hourl y surface

observations over land in one str eam , and 3-hourly over both land a n d oce a n s u rfaces i n anot her stream.

• N W S/ T D L' s U.S

. a n d C a na di an su rface o b s ervat i o ns ( N C A R arc h i v e d s 47 2. 0 ) . T h i s da t a set pr ovi des ho u r l y

surface observations over t h e U. S. a n d Cana dian re gions.

The C r essm an objective a n alyses option is us ed to enha n ce t h e Eta a n alysis. Howeve r, we analyzed the re sults and

foun d th at it still co u l d no t reprodu ce th e right d i urn a l cy cle o f Vsfc and Tsfc. Thu s , we rep eat th e C r essman

proce d ures t h ree tim e s m o re to e nha nce t h e s u rface a n alys es. Results i ndica te that this procedure si gni ficantly

i m p r ov es th e resu lts.

8

### Figure 1 Comparison of TDL obs erved and MM5 predicted temp eratures

fo r J u ly 6 to 19 , 19 97



## M M 5 S i m u la tio n b y U M D (r e v is e d )

Ob se r v

e d / P r e d i ct ed T e m p e r at u r e

28 5 28 7 28 9 29 1 29 3 29 5 29 7 29 9 30 1 30 3 30 5

7/

6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K Ob s T e m p Pr d T e m p



### B i a s T e m p er at u r e

-2 -1 .5

-1 -0 .5

0 0. 5

7/

6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K Bi a s T e m p



### R M SE T e m p erat u r e

0 0. 5

1 1. 5

2 2. 5

3

7/

6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K R M SET e m p R M SES T e m p RM S E U T em p



### IO A

Te m p e r a t u r e

0. 7 0. 8 0. 9

1

7/ 6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9 IO A

T e m p

9

### O b s e rv e d / P re di c t e d W i nds pe e d

0 1 2 3 4 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/ 19m/s O b s W nd S p d P r d W nd S pd



### B i a s W i nds pe e d

-1 .5

-1 -0 .5

0 0. 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/ 19m/s B i a s W n dS pd



### R M S E W i nds pe e d

0 0. 5

1 1. 5

2 2. 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/ 19m/s R M SEW n d Sp d R M SESW n d S p R M SEU W n d S p



### I O

A

W i nds pe e d

0. 4 0. 5 0. 6 0. 7 0. 8 0. 9

1

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 I O

A W ndS p d



### Ob se r v

e d / P r e d i ct ed W i n d D i r e c t i o n

0 60 12 0 18 0 24 0 30 0 36 0

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/ 19deg Ob s W n d D i r Pr d W n d D i r



### Bi a s W i n d Di r e c t i o n

-9 0 -6 0 -3 0

0 30 60 90

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/ 19deg Bi a s W n d D i r 

### Figure 2 MM5 Predicted and TDL measured w s and w d , July 6 to 19, 1997



### M M 5 S i m u la tio n b y U M D (r e v is e d )

10Figure 3 MM 5 Predict e d and T D L measure d mi xing r a ti o ( h u m idity)

f o r July 6 t o 19 , 19 97



### MM5 Simula t i o n b y U M D ( r e v is e d )

P r ed i c ted / O b served M i xi n g R a ti o ( H u m i d i t y)

8 10 12 14 16 18

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 / 19g/kg Ob s H u m P r dH um

B i a s Mi x i ng R a t i o ( H um i d it y )

-3.5

-3 -2.5

-2 -1.5

-1 -0.5

0 0.5

1

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 / 19g/kg Bi a s H u m

R M S E M i x i n g R a t i o ( H um idi t y )

0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

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 / 19g/kg RM S E Hu m R M S ESH u m RM S E UH u m

IO A H u mi dit y

0.5 0.6 0.7 0.8 0.9

1

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 IO A H um

11

### Figure 4 Correlation betw een MM5 P redicted and TDL-based

Temperatu res for July 6 to 19, 1997

12

### Figure 5 Correlation betw een MM5 Pred icted and TDL-based

w i nd speeds for July 6 to 19, 1997

13

### Figure 6 Correlation between MM5 Pred icted and TDL-based m i xing

ratio (hum idity) for July 6 to 19,1997

14

### Figure 7 MM5 Predicted and CASTNet measured temperatures, July 6 to 19, 1997



### M M 5 S i m u l a t i o n b y

U M D ( r ev i s ed ) Ju l y 6 t o 19 1997 v s C A

S T N e t

O b se r v ed / P redi ct e d T e m p e r a t ur e

27 5 28 0 28 5 29 0 29 5 30 0 30 5

7/ 6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K Ob s T e m p P r d T em p



### B i as T e m p er at ur e

-1

. 5

-1 -0

. 5

0 0. 5

1 1. 5

2 2. 5

3

7/ 6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K Bi a s T e m p



### R M S E Tem p erat ur e

0 0. 5

1 1. 5

2 2. 5

3 3. 5

7/ 6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9K R M S E T em p R M SEST e m p R M SEU Te m p



### I O

A

T e m p er at ur e

0. 6 0. 7 0. 8 0. 9

1

7/ 6 7/ 7 7/ 8 7/ 9 7/ 1 0 7/ 1 1 7/ 1 2 7/ 1 3 7/ 1 4 7/ 1 5 7/ 1 6 7/ 1 7 7/ 1 8 7/ 1 9 IO

A T e m p

15

### Figure 8 MM5 Predicted and CASTNet meas ured w s and w d for July 6 to 19, 2007

M M 5 S i m u latio n b y U M D (r ev ised ) Ju ly 6 to 19 1997 v s C A S T N e t

O b served / P red i ct ed W i n d s p eed

0 1 2 3 4 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/ 19m/s O b s W ndS pd P r dW ndS pd

Bi as W i n d s p eed

- 1 - 0.

5 0 0.

5 1 1.

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/ 19m/s B i a s W ndS pd

R M S E W i nds pe e d

0 0.

5 1 1.

5 2 2.

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/ 19m/s R M S E W ndS pd R M SESW n d S p R M SEU W n d S p

IOA W i nds pe e d

0.

4 0.

5 0. 6 0. 7 0. 8 0. 9

1

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 I O A

W ndS pd

O b served / P red i ct ed W i n d Di rect i o n

0 60 120 180 240 300 360

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/ 19deg O b s W ndD i r P r dW ndD i r

Bi as W i n d Di rect i o n

- 90 - 60 - 30

0 30 60 90

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/ 19deg B i a s W ndD i r

16

### Figure 9 Correlation betw een MM5 predicted and CASTNet meas ured

temperatures for July 6 to 19, 1997

17

### Figure 10 Correlation betw een MM5 predicted and CASTNet measured

w i nd speeds for July 6 to 19, 1997

18

### Figure 11 Spatial distribution of PBL heights estimated by MM5 on

0300 GMT on July 7, 1997

19

### Figure 12 Observed a nd Predicted mixing ra tios based o n MM5 and RAMS for

the June, July and August of 1995 (from Hogrefe 2002)

20

### Further comparison of MM5 simulation s for the July 11 to 15, 1997 period

The M C NC E n vi r onm ent a l gr ou p usi n g M M 5 has si m u l a t e d t h e J u l y 1 9 9 7 hi g h oz one epi s od e. T h e res u l t s

of

t h ei r a n al y s i s we re post e d a t h ttp ://www.emc. m cn c.o r g / proj ects/NC DAQ/Met/9 7 e

. Th e desi gn an d

exerci si n g o f t

h e M M 5 m odel by t h i s gr o u p di ffe rs fr om t h at of UM D (se e

ftp ://www.d ec.state.n y .u s/ d a r/ air_ research /h t d o c s/mm 5

- u m d

- set u p.h t m ) ev en tho ugh th e d e fin itio n of t h e

p r oj ectio n of t h e g r i d is th e sa m e b e tween t h e two sim u lat i o n s

.

Som e of t h e sa lient differe n ce s are: ve rtical gri d stru cture; areal exte nt of the gr id s ; and th e sh orter p e ri o d

o f

sim u lat i o n [Ju l y 10 ( 1 2 Z ) to Ju ly 16 (12 Z ) 1 997 o f MC N C co m p ar ed t o Ju ly 5 (1 2Z) to Ju ly 21 (0 0Z)

, 1 997 b y UM D]. Details of the setup can b e foun d at th e a b ove re fere nce d URLs. In t h is analysis, we focus

o n th e relativ e respon se o f th e two sim u latio n s o v er t h e commo n r e g i on of th e 12k m g r id to th e TD L an d

CASTNet m e a s u r em en ts. To th is end

, we obtain e d the g r idd e d d a ta fo r t h e MCNC run i d en tified as Case E.

The 1 2 km gri d , i d ent i f i e d as D 0 2

, c o vers p o rt i o ns o f VA

, WV

, KY , T N

, GA , SC besi de s NC a n d i s s h ow n

i n Fi g u r e 1, a d apt e d fr om t h e M C NC we b si t e

. B r i e fl y

, t h e UM D si m u l a tion co nsi s t s of 2- way nest ed 3 6 / 1 2

k m g r id , wh ile th e MCNC u tilized 3grid s at 36 /12 / 4 k m o n e -way n e stin g. Th e co m p ariso n is m a d e b e tween

D02

, th e area sh own in Figure 1o f th e MC NC do m a in to the correspo n d i ng p o rtion o f t

h e UMD do m a i n

(see Fi gure 2). The statistical measures were de vel o ped u s i n g t h e M E TST A T fr om Envi r o n (see

ftp ://ca m x

. p a ss4 cam x @ftp .env iro n .org / p ro cesso rs/m e t stat. 2 4j an 02 tar.z).

TDL dat a c o m p ari s on

Fi gu re 3 a n d 4 di spl a y t h e co m p ari s on of t h e M M 5 pre d i c t i ons t o m easur ed wi n d s p ee d, t h at we re part of

th e TDL d a ta utilized in th e nu dg ing fo r MC NC an d UM D si m u latio n s

, resp ectiv ely. Th e MCNC estim a t es

o f

w i n d sp eed ex h i b it bo th over an d und er pred icti o n

, wh ile th e UMD estimate sh ows u n d e r pred iction o f

t h e m easured wi n d s p ee ds. Whi l e t h e un de r pre d i c t i on i n bot h cases is as sociated with t h e peak wi nd s p eed,

th e MCNC d a t a sho w s o v e r pred iction for the ho urs with m i n i m u m win d sp eed as well un lik e th e UM D

dat a . T h e wi n d di rect i o n o n t h e ot he r ha nd sh ows ve ry go o d agreem ent wi t h bot h m odel s and bi as t h at i s

gene ral l y nea r t h e zer o l i n e

.

Fi gu res 5 a n d 6 di spl a y t h e c o m p ari s on o f t

h e pre d i c t e d t e m p erat ures f r o m MC NC an d UM D t o t h at

measured unde r T D L, res p ectively. Bot h m odels s h ow go od ag reem en t to th e TDL d a ta, with th e UM D

dat a ex hi bi t i n g sl i ght l y l o wer bi as t h a n t h e M C NC si m u l a ti on. T h e bi as r a nge f o r

UM D i s fr om 0.5 t o

– 2

. 5

C

, w h i l e f o r

M C NC i t ra nges fr om 1.5 t o –4 C

.

Fi gu res 7 a n d 8 di spl a y t h e c o m p ari s on o f t

h e pre d i c t e d a n d m easured m i xi n g rat i o base d on M C NC a n d

UM D si m u l a tions , resp ect i v e l y

. In ge neral

, bot h si m u l a tio n s ten d to un d e r p r ed ict with th e UMD esti mates

sho w i n g an oc casi onal a g r e e m ent wi t h t h e m easured dat a

. H o we ve r, t h e di u r nal vari at i o n a ppea r s t o s h ow a

b e tter m a tch v i su ally with ob serv ation s fo r t h e MCNC sim u l a tio n

.

CASTNet d a ta co m p arison

Fi gu res 9 a n d 10 di s p l a y t h e avera g e o b ser v ed a n d p r e d i c t e d wi n d s p ee d a n d di rect i o n f o r M C NC a n d

UM D si m u l a tions

, resp ect i v e l y for t h e c o m m on 12 km do main. The ave r age wind s p ee ds m easure d at the

CASTNet sites are i n th e 1 to 2 m /s ran g e com p ared to t h e TDL sites wh ich rang e fro m 1 to 4 m / s. Th e

M C NC si m u l a ti on e x hi bi t s ge neral l y a posi t i ve bi as, w h i l e t h e UM D sh o w s a negat i v e bi as, i n b o t h case s

as hi gh as 1m / s

.

Figure 11 a n d 12 dis p lay the avera g e observed a n d p r e d i c t e d t e m p erat ures base d o n M C NC a n d UM D

si m u latio n s , resp ectiv ely. While th e MCNC si m u latio n shows u n d e r p r ed i c tio n o f th e m a x i m u m

te m p eratu r e, the UMD sim u la tio n tend s to ov er pred ict t h e max i m u m

. On th e o t h e r h a nd , th e m i n i m u m

te m p er atu r e sho w s sligh t under p r ed ictio n in th e case of UMD sim u latio n wh ile th e MC N C sim u latio n

sho w s o v er p r e d i c t i on. E x am inat i o n o f t

h e bi as sh o w s t h at t h e UM D e x c u r s i ons are hi ghe r i n m a gni t ude

t h an t h e ove r- or u n d er - pre d i c t i on of M C N C , al t h o u g h t h e m a gni t ude of t h e un de r pre d i c t i ons a r e a b o u t

th e sam e

.

1Sp atial correlat i o n

We al s o e x am ined t h e c o rrel a t i on of m e t e orol o g i cal pa ram e t e rs bet w een m odel sim u l a t e d a n d m easured

(TD L a n d C A STNet ) dat a f o r eac h of t h e m oni t o rs. T h e t o t a l num ber of l o cat i o n s c onsi d ere d i n t h i s

an alysis, ar e 98 an d 14 fo r t

h e TDL and C A STNet, res p ect ively.

Fi gu res 1 3

, 1 4

, an d 15 di s p l a y spat i a l di st ri b u t i o n o f co rrel a t i on fo r wi n d spee d, t e m p erat ure, an d m i xi ng

rat i o , res p ect i v el y

, bet w ee n T D L base d- dat a and M C NC ’s MM5 sim u lati o n . Sim ilar l y, Fig u r e s 16 ,1 7, an d

18 dis p lay the correlation for wind s p ee d, t e m p erat ure a n d m i xi ng ra tio

, resp ectiv ely fo r th e UMD’s M M 5

sim u al t i on. In t h e case o f t e m p erat ure (see F i gu res 14 an d 1 7 ) t h e UM D si m u l a t i on a ppea r s t o y i el d hi gh er

co rrelatio n th an th e MCNC si m u la tio n

, wh il e fo r wind sp eed (see Fi g u re 13 an d 16) b o t h si m u latio n s

exhi bi t si m i l a r l e vel o f c o r r el at i on. I n t h e cas e o f m i xi ng r a t i o ( S ee Fi gu res 1 5 a n d 1 8 ) t h e r e a ppea r s t o b e

slig h tly h i gh er lev e l of co rrelatio n for th e M C NC sim u latio n th an UM D.

Fi gu res 1 9 t h r o ug h 2 2 di spl a y t h e s p at i a l di st r i but i o n of co rr el at i on fo r wi n d s p ee d a n d t e m p erat ure

b e tween C A STNet and t h e two MM5 sim u la tio n s . As no te d above the sample of stations i s m u ch sm aller

co m p ared t o the TDL d a ta. Interestin g l y, bo th si m u latio n s ex h i b it lower lev e l of co rrelatio n fo r

te m p er atu r e ( s ee Figu r e s 20 an d 22) an d fo r th e w i n d sp eed ( s ee Figu r e s 19 an d 21) whe n c o m p ared t o the

TDL data.

D i scu ssi on an d Su mm ar y

Bo th MM 5 si m u la tio n s u tili zed o b serv ation a l nud g i n g wh ich was b a sed up on TDL d a t a , bu t n o t on the

m easurem ent s fr om C A STNe t

. Th us i n a wa y C A ST Net p r ovi des fo r a n i nde pe nde nt ass e ssm ent

, al t h o u g h

th e nu m b er o f statio n s is qu ite li m ited in th e presen t assessmen t

.

The a g reem ent bet w ee n t h e M M 5 si m u l a t i ons i s qui t e go od , e v en t h ou g h bot h si m u l a t i ons we re c o nd uct e d

i nde pen d e n t l y wi t h di ffe ri n g vert i cal gi rd st r u ct u r e. Fo r e x a m pl e, t h e l a y e r-1 hei ght s a r e 38m and 2 0 m for

MCNC and UMD sim u latio n s , resp ectiv ely. Also, n o ad ju st m e n t s were m a d e to th e m o del esti m a tes fo r

m oni t o r hei ght w h i c h i n t h e c a se o f t e m p erat ure p r o b e i s ab out 2m and wi nd sp eed an d d i rect i on at 1 0 m f

o r

TDL and for CAST Net at 9m for tem p erat u r e and 10 m f

o r

w i nd sp eed an d d i r ectio n.

The lac k of a better a g reem ent bet w een the m odel esti m a te d a n d m easured m i xing ratio suggests t h at t h ere

i s a nee d fo r fu rt her exam i n at i o n o f t

h e m e t hods u s ed f

o r

t h e est i m a t i on o f t h e param e t e r.

2

### Figure 1 MM5 Modeling Domain used in MCNC Simulation

3

### Figure 2 MM5 Modeling Domain used in UMD Simulation

4Figure 3 MM5 b y MCNC doma i n2e July 11 to July 15 1997 v s TDL

5Figure 4 MM5 by UMD domain2e July 11 to July 15 1997 v s TDL

6Figure 5 MM5 b y MCNC doma i n2e July 11 to July 15 1997 v s TDL

7Figure 6 MM5 b y UMD domain2e July 11 to July 15 1997 v s TDL

8Figure 7 MM5 by MCNC do main2e July 11 to July 15 1997 v s TDL

9Figure 8 MM5 b y U M D domain2 e July 11 to July 15 1997 v s TDL

10Figure 9 MM5 by MCNC dom ain2 e July 11 to July 15 1997 v s CASTNe t

11Figure 10 MM5 b y UMD d o main 2e July 11 to July 15 1997 v s CASTNet

12Figure 11 MM5 b y MCNC domain 2e July 11 to July 15 1997 v s CASTNet

13Figure 12 MM5 b y UMD d o main 2e July 11 to July 15 1997 v s CASTNet

14

### Figure 13 Spatial dis t ribution of correl a t ion for w i nd speed betw een TDL and

MCNC’s MM5 prediction

15

### Figure 14 Spatial dis t ribution of correl a t ion for tempera ture betw een TDL and

MCNC’s MM5 prediction

16

### Figure 15 Spatial dis t ribution of correla t io n for mix i ng ratio (humidity) betw een

TDL an d MCNC’s MM5 prediction

17

### Figure 16 Spatial dis t ribution of correl a t ion for w i nd speed betw een TDL and

UMD’s MM5 prediction

18

### Figure 17 Spatial dis t ribution of correl a t ion for tempera ture betw een TDL and

UMD’s MM5 prediction

19

### Figure 18 Spatial dis t ribution of correla t io n for mix i ng ratio (humidity) betw een

TDL and UMD’s MM5 prediction

20

### Figure 19 Spatial dis t ribution of correl a t ion for w i nd speed betw een CASTNet

and MCNC’s MM5 prediction

21

### Figure 20 Spatial dis t ribution of correl a t ion for tempera ture betw een CASTNet

and MCNC’s MM5 prediction

22

### Figure 21 Spatial dis t ribution of correl a t ion for w i nd speed betw een CASTNet

and UMD’s MM5 prediction

23

### Figure 22 Spatial dis t ribution of correl a t ion for tempera ture betw een CASTNet

and UMD’s MM5 prediction

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