# Comparison **of CENRAP, VISTAS, and**  **WRAP 36 km MM5 Model Runs for 2002**  **Task 3: Meteorological Gatekeeper**  **Report**

Susan Kemball

- Cook, Yiqin Jia, Chris Emery and Ralph Morris

ENVIRON International Corporation

Zion Wang and Gail Tonnesen
UCR CE

- CERT



# Western Regional Air Partnership (WRAP) **Regional Modeling Center (RMC)**

# Model Setup for CENRAP, **WRAP, and VISTAS**  **Runs**

• 

# All **use National RPO 36 km Grid, 164 x 128 x 34** 

• 

# Lambert **Conformal Projection**

• 

# Pleim

**- Xiu/ACM**  **LSM/PBL**

• 

# RRTM Longwave **and Dudhia Shortwave**

• 

# No shallow convection

# Model Setup for CENRAP, **Initial WRAP,**  **and**

**VISTAS Runs, continued.**

**CENRAP VIST** 

**AS WRAP**  **1**



## Cumulus **Convection Kain** 

****

**Fritsch 2**  **Kain**

 

** Fritsch** **2 Kain** 

****

**Fritsch** 

# Physics **Options Differ ences** 

**Moist Physics**  **Reisner 1**  **Reisner**

**1 Reisner** 

**2**

**Analysis Nudging** 
**Surface u/v**  **u/v u/v/t/q** 

**Analysis Nudging** 
**Aloft u/v/t/q**  **u/v/t/q u/v/t/q** 

1 Final WRAP

MM5 used Betts

- Miller Cumulus and u/v surface analysis nudging

# Method **for Comparing the CENRAP,** 

**WRAP, and VISTAS MM5 Runs** 

• Surface statistics for wind, temperature, and humidity For CENRAP

, WRAP
, and VIST

AS runs, we examine:

• Surface statistics for wind, temperature, and humidity

• Upper air soundings of temperature, dew point, and winds

• Precipitation
and compare with observations.

Focus on CENRAP

dom ain, and also give overview of performance in other regions.

# Surface **Evaluation Method** 

• Use Metstat statistical package to compare model surface output to surface station data

• Metstat calculates statistics which describe how well the model Metstat calculates statistics which describe how well the output fields agree with observations

• Metstat analysis divides U.S. into subdomains to show how MM5 performs region by region

• Metstat statistics summarized in scatter plots which contain a model performance benchmark box as well as results of other MM5/RAMS runs used for air quality modeling

# METSTAT Evaluation **Package**

• 

# Average **observed and predicted**

• 

# Absolute Bias and Error

• 

# RMSE

• 

# Index of Agreement (IOA)

• 

# Daily **and, where appropriate, Hourly Evaluation** 

• 

# Statistical **Performance Benchmarks** 

> 

# Based on an analysis of > 30 MM5 and RAMS runs

> 

# Not meant as a pass/fail test, but to put modeling results in the proper perspective

Wind Speed Wind Dir ection Te mper atur e Hu midity RM SE  2 m /s Me an Bi as   0. 5m/ s   10    0. 5K   1g/ kg Index of Agr eem en t  0 .6  0 .8  0 .6 Gr oss Erro r  3 0   2 K  2 g/ kg

# Subdomains **for Model Evaluation** 

1 = Pacific NW

2 = SW

3 = North

4 = Desert SW

5 = CenrapN

6 = CenrapS

7 = Great Lakes

8 = Ohio Va lley

9 = SE

10 = NE

11 = MidAtlantic

# Datasets **for Met Evaluation**

• 

# NCAR **dataset ds472 airport surface met observations** 

surface temperature surface pressure sea level pressure visibility cloud cover height of cloud base max imum temperature mi nimum

temperature dew poi nt precipitation sunshine snowfall

snow cover win d direction win d force (speed) u w ind component

v wind component win d gust descriptive weather



# • 

# Twice

**- Daily**  **Upper**

**- Air Profile**  **Obs (~120 in US)**

> 

# Temperature

> 

# Moisture v wind component win d gust descriptive weather

## • WRAP

has smallest error

• cenrapN subdomain outside goal for CENRAP

run **CENRA P**  **/ V IST**  **AS / WR**  **AP J**  **anuar y**  **Te mp**  **erat ur**  **e Per**  **fo rm**  **ance Com**  **par is**  **on Ov**  **er C ENRA**  **P Domain** 

2 2.5 3 3.5**T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)** 

## • WRAP has smallest error

• cenrapN subdomain outside goal for CENRAP

run

0 0.5 1 1.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Temper ature**  **Bi as**  **(K )** **T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)**

Be nc hm ark CE NRA P VI ST AS WR AP i CE NRA P N

CE NRA P S 

## run

## CE **NRAP / VISTA**  **S / WR**  **AP Jan**  **ua ry**  **Hu mi**  **dity**

**P erfo**  **rman ce**  **Co mp**  **ari so**  **n O ver**  **CE NRAP**  **Do mai**  **n**

2 2. 5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)** 

## • Humidity performance is comparable

for all three runs

0 0. 5 1 1. 5

-1. 5 -1 -0. 5 0 0. 5 1 1. 5 2 2. 5 

### Hu **mi di**  **ty B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Be nc hmark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

## CE **NRAP / V**  **IS TA**  **S / WR AP**  **J an**  **ua ry**  **Wind P erfo**  **rman ce**  **Co mp**  **ari so**  **n ov er**  **CE NRAP**  **Do mai**  **n**

50 60 70 80**W i** **n d** **D i** **r e** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)** 

## • Wi nd performance is comparable for all three runs

0 10 20 30 40

0 0. 5 1 1. 5 2 2. 5 3 

### Wi **nd Speed**  **RM SE**  **(m/s )** **W i** **n d** **D i** **r e** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)**

Be nc hm ark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

## CENRAP **/ V ISTA**  **S / WR**  **AP Ma**  **rch Te**  **mp eratu**  **re Perfo**  **rman ce**  **Co mp**  **ari so**  **n O ver**  **CE NRAP**  **Do mai**  **n**

2 2. 5 3 3. 5**T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)** 

## • WRAP has best overall performance

• cenrapN subdomain outside goal for CENRAP

run

0 0. 5 1 1. 5

-2 -1. 5 -1 -0. 5 0 0. 5 1 

### Te **mp erat**  **ure Bi**  **as (K**  **) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Be nc hm ark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

## CENRAP **/ V ISTA**  **S / WR**  **AP Ma**  **rch H**  **um idity**  **Pe rfo**  **rman ce**  **Co mp**  **ari so**  **n O ver**  **CE NRAP**  **Do mai**  **n**

2 2. 5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)** 

## • Performance is comparable

for all three runs

0 0. 5 1 1. 5

-1. 5 -1 -0. 5 0 0. 5 1 1. 5 2 2. 5 

### Hu **mi di**  **ty B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Be nc hm ark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

### CENRA **P / V**  **IST AS**  **/ WR AP**  **Ma rch**  **Wi nd**  **Per fo**  **rm ance**  **Compar is**  **on Ov**  **er CENRA**  **P Dom**  **ai n** 

40 50 60 70 80**W i** **n d** **D i** **r e** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)** CENRA P N

CENRA P S

0 10 20 30 40

0 0.5 1 1.5 2 2.5 3
**Wi nd**  **Speed RM**  **SE (m/s**  **) W** **i n** **d D** **i r** **e c** **t i** **o n** **E r** **r o** **r (** **m /** **s )** 

Benc hm ar k CE NRA P VI ST AS WR

AP i

### CENRA **P / V**  **IST AS**  **/ W RA**  **P Jul**  **y Te**  **mp erat**  **ur e**  **Per fo**  **rm ance**  **Com par**  **is on**  **Ov er**  **CENRA P**  **Dom ai**  **n**

2 2.5 3 3.5**T e** **m e** **p r** **a t** **u r** **e E** **r r** **o r** **( K** **)** 

## • Te mperature error is higher in all runs relative to January

0 0.5 1 1.5

-2 -1 .5 -1 -0 .5 0 0.5 1
**Tem per**  **at ur** 

**e Bi**  **as (K**  **) T** **e m** **e p** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Be nc hmark VI ST AS WR AP i CE NRA P CE NRA P N

CE NRA P S

## CENRAP **/ V ISTA**  **S / WR**  **AP Ju**  **ly H**  **um idit**  **y Pe**  **rf or**  **man ce**  **Co mp**  **ari so**  **n O ver**  **CE NRAP**  **Do mai**  **n**

2 2. 5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)** 

## • Humidity error is higher in

all runs relative to January

0 0. 5 1 1. 5

-1. 5 -1 -0. 5 0 0. 5 1 1. 5 2 2. 5 

### Hu **mi di**  **ty B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Be nc hm ark VI ST AS WR AP CE NRA P CE NRA P N

CE NRA P S

### CENRA **P / V**  **IST AS**  **/ W RA**  **P Jul**  **y Wi**  **nd Per**  **fo rm**  **ance Com**  **par is**  **on Ov**  **er CENRA**  **P Dom**  **ai n** 

40 50 60 70 80**W i** **n d** **D i** **e r** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)** 

## • Wi nd performance is comparable for all three runs

0 10 20 30

0 0.5 1 1.5 2 2.5 3
**Win d**  **Speed RM**  **SE (m/s**  **) W** **i n** **d D** **i e** **r c** **t i** **o n** **E r** **r o** **r (** **m /** **s )** 

Be nc hmark VI ST AS WR AP i CE NRA P CE NRA P N

CE NRA P S

## CE **NRAP / VISTA**  **S / WR AP**  **O cto**  **be r**  **Te mp**  **erat ur**  **e Pe**  **rf or**  **man ce**  **Co mp**  **ari so**  **n O ver**  **CE NRAP**  **Do mai**  **n**

2 2. 5 3 3. 5**T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)** 

## • WRAP has smallest error

0 0. 5 1 1. 5

-2 -1. 5 -1 -0. 5 0 0. 5 1 

### Te **mp erat**  **ure Bi**  **as (K**  **) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Be nc hm ark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

### CEN **RAP / V**  **IS TAS / W**  **RAP Oc**  **tober H**  **umi di**  **ty**

**P er**  **form ance**  **Com par**  **is on O**  **ve r**  **CEN RAP**  **Dom ai**  **n**

1. 5 2 2. 5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)** 

## • Performance is comparable

for all three runs

0 0. 5 1 1. 5

-1. 5 -1 -0 .5 0 0. 5 1 1. 5 2 2. 5
**Hum id**  **ity B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Be nc hm ar k CENRA P VI STA S WR AP i CENRA P N

CENRA P S

## CENRAP **/ V ISTA**  **S / WR**  **AP Oct**  **obe r**  **Wind P erf**  **or man**  **ce Co**  **mp ari**  **so n O**  **ver CE**  **NRAP Do**  **mai n** 

50 60 70 80**W i** **n d** **D i** **r e** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)** 

## • Wi nd performance is comparable for all three runs

0 10 20 30 40

0 0. 5 1 1. 5 2 2. 5 3 

### Wi **nd Speed**  **RM SE**  **(m /s**  **) W** **i n** **d D** **i r** **e c** **t i** **o n** **E r** **r o** **r (** **m /** **s )** 

Be nc hm ark CE NRA P VI ST AS WR

AP i CE NRA P N

CE NRA P S

## Su mma ry o f CEN RAP / V IS TAS / W RAP Me ts ta t S ta tis tic s Ja nuary Ma rc h Ma y Ju ly Oc to ber Dec em ber

ce nrap N

te mp erat ure WR AP WR

AP WR

AP WR

AP WR

AP WR

AP hum id ity WR AP WR

AP None VI ST AS None WR

AP wi nd No ne V/C VI ST AS CE NRA P VI ST AS CE NRA P

ce nrap S

te mp erat ure No ne WR AP WR

AP CE NRA P None WR AP hum id ity VI ST AS VI ST

AS None No ne WR AP WR

AP wi nd No ne V/W VI ST AS CE NRA P WR

AP None

ov eral l

te mp erat ure WR AP WR

AP WR

AP No ne WR AP WR

AP hum id ity No ne VI ST AS None No ne WR AP WR

AP wi nd No ne VI ST AS VI ST

AS CE NRA P None WR AP


## Entry in table indicates which run performed best in that subdomain for that field. Note that initial WRAP

r un used surface t/q nudging that can improve performance statistics but introduce instabilities

# Overview **of CENRAP/WRAP/VISTAS**  **Surface Performance**  **in CENRAP Region**

• Overall, performance over CENRAP region was within benchmarks.

• All runs had similar wind and humidity performance.

• CENRAP

and VIST

AS had lar ger temperature error than WRAP

• For CENRAP

run, the cenrapN subdomain fell outside the benchmark in January and December

# Overview **of CENRAP/WRAP/VISTAS**  **Surface Performance**  **in CENRAP Region**

• 

# In general, surface performance is better in winter than in summer for all three runs.

• Humidity error is higher in the summer than in the winter for all three runs; there is wet bias in the winter . Less clear cut in other

seasons.

• Wi nd speed RMSE and wind direction error increase going from winter to summer , then decrease again as winter approaches.

• For all runs, cenrapN subdomain has cold bias in winter and a warm bias in summer . Less clear cut for cenrapS.

# Performance **Outside CENRAP region**

• 

# Surface performance had strikingly dif ferent characteristics in the
eastern vs. western U.S. subdomains.

• The following soccer plots emphasize performance issues over western U.S common to all three runs.

• We stern U.S. subdomains are shown with filled icons. All other subdomains denoted by open circles colored according to run.

• When an eastern subdomain falls outside the benchmark, it is identified.

### CENRA **P / V**  **IST AS**  **/ WR AP**  **J anuar**  **y Te**  **mp erat**  **ur e**  **Per fo**  **rm ance**  **Com par**  **is on** 

2 2.5 3 3.5**T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)**

SE NE ce nr apN 

### • WRAP has sm allest error

• We stern subdom ains have lar ge cold bias

0 0.5 1 1.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Temper ature**  **Bi as**  **(K )** **T e** **m p** **e r** **a t** **u r** **e E** **r r** **o r** **( K** **)**

Be nc hm ark VI ST AS WR AP CE NRA P ohiov al

De se rt SW

Pa cN W

SW

No rt h

### CENRA **P / V**  **IS TA**  **S / WR**  **AP Januar**  **y Hum**  **id it**  **y Per**  **fo rm**  **ance Com**  **par is**  **on**

1.5 2 2.5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

De se rt SW



## • All subdomains lie within benchmark

0 0.5 1 1.5

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5
**Hum id**  **ity B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Benc hm ar k VI ST

AS WR

AP CE NRA P De se rt SW

Pa cN W

SW

No rt h

### CENRA **P / V**  **IST AS**  **/ WR AP**  **J anuar**  **y Wi**  **nd Per**  **fo rm**  **ance Compar**  **ison**

40 50 60 70 80**W i** **n d** **D i** **r e** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)** De se rt SW

Pa cN W

SW

No rt h 

## • Wi nd performance does not meet benchmark in any western U.S. subdomain

We stern subdom ains

0 10 20 30 40

0 0.5 1 1.5 2 2.5 3
**Wi nd**  **Speed RM**  **SE (m/s**  **) W** **i n** **d D** **i r** **e c** **t i** **o n** **E r** **r o** **r (** **m /** **s )** 

Benc hm ar k VI ST

AS WR

AP CENRA P NE 

### We stern subdom ains

### CENRA **P / V**  **IST AS**  **/ WR AP**  **J ul**  **y Te**  **mp erat**  **ur e**  **Per fo**  **rm ance**  **Com par**  **is on** 

2 2.5 3 3.5**T e** **m e** **p r** **a t** **u r** **e E** **r r** **o r** **( K** **)** 

### Lar ge cold bias in western U.S.for all three runs

0 0.5 1 1.5

-2 -1 .5 -1 -0 .5 0 0.5 1
**Tem per**  **at ur** 

**e Bi**  **as (K**  **) T** **e m** **e p** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Be nc hm ark VI ST AS WR AP CE NRA P De se rt SW

Pa cN W

SW

No rt h

### CENRA **P / V**  **IS TA**  **S / WR**  **AP J**  **ul y**  **Hum id**  **it y**  **Per fo**  **rm ance**  **Com par**  **is on** 

1.5 2 2.5 3**H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

De se rt SW



## • Lar ge wet bias in WRAP desert SW

and North

0 0.5 1

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5
**Hum id**  **ity B**  **ia s**  **(g /k**  **g) H** **u m** **i d** **i t** **y E** **r r** **o r** **( g** **/ k** **g )** 

Be nc hm ark VI ST AS WR AP CE NRA P De se rt SW

Pa cN W

SW

No rt h

### CENRA **P / V**  **IST AS**  **/ WR AP**  **J ul**  **y Wi**  **nd Per**  **fo rm**  **ance Compar**  **ison**

40 50 60 70 80**W i** **n d** **D i** **e r** **c t** **i o** **n E** **r r** **o r** **( m** **/ s** **)**

SE

De se rt SW

Pa cN W

SW

No rt h 

## Wi nd speed RMSE and direction error increased relative to January

0 10 20 30

0 0.5 1 1.5 2 2.5 3
**Win d**  **Speed RM**  **SE (m/s**  **) W** **i n** **d D** **i e** **r c** **t i** **o n** **E r** **r o** **r (** **m /** **s )** 

Be nc hm ark VI ST AS WR AP CE NRA P NE Oh io V alle y



## We stern subdomains

# Overview **of Performance**  **Outside**

**CENRAP Domain** 

• 

## For wind, all three runs perform ed similarly . Note that western U.S. subdom ains were outside the benchmark for all seasons for all three runs.

• For temperature WRAP r un had best overall perform ance. Exception is in summer in the west, where it is the outlier

.

• however this is partly due to surface t/q nudging which is not desirable

• For hum idity

, all three runs are comparable. Exception is WRAP during sum mer in western U.S., which has a lar ge wet bias associated with excessive rainfall.

• CENRAP has lar gest cold bias in the west during winter , but smallest cold bias in the west during the summer

.

• For hum idity

, most subdomains have a wet bias all year

. We stern subdom ains SW and PacNW are the exceptions and have a dry bias.

# Method **for Evaluating Upper Air** 

**Performance**

• Use Matthew Johnson’ s (IDNR) RAOB program to compare observed 0Z and 12Z soundings with MM5 soundings.

• Based on Metstat evaluation, compare soundings for January and July only

. These two months are representative of MM5’ s best and worst performance during the 2002 36 km run. Examined soundings from 2 stations in CENRAP dom ain.

• Analysis is based on inspection of the soundings, and is necessarily subjective

• Tw o sets of soundings will be shown here to summarize results of January and July analysis.

# Comparison of Soundings **for Midland AFB, TX for January 7, 12Z** 

**CENRAP VIST**  **AS WRAP** 

WRAP T colder than VIST

AS and CENRAP

# Comparison of Soundings **for Midland AFB, TX. for July 16, 0Z** 

**.**

**CENRAP VIST**  **AS WRAP** 

PBL top inversion underestimated

# Summary **of Upper Air Evaluation** 

• For July soundings with a deep, convecting boundary layer
, CENRAP and VIST

AS frequently better reproduced the observed temperature inversion at the top of the PBL, while WRAP s howed a smoother decrease of temperature with increasing altitude.

• In January and July

, CENRAP

a nd VIST

AS were better able to simulate PBL

temperature inversions than was WRAP

.

• WRAP s urface temperature was generally closer than CENRAP

or VIST AS to the observed surface temperature, but profiles above surface were less accurate in WRAP t han in CENRAP

o r VIST AS.

# Summary **of Upper Air Evaluation** 

• CENRAP

and VIST

AS dew point temperature profiles were generally closer than that of WRAP to observed profile, and were better able to handle extreme excursions in temperature and dew point profiles in the lower troposphere.

• Ve rtical wind profiles were very similar , particularly above the boundary layer (this is reasonable since all runs were analysis nudged above the PBL).

• Even in cases where the PBL temperature profiles were dif ferent, wind structure was similar

. This suggests that local thermal
processes are driving the temperature dif ferences, rather than advection.

# Method **for Evaluating Precipitation** 

**Performance**

• For each model run, calculate the total MM5 precipitation for each hourly output time:

To tal Precip = Convective Precip + Lar ge

- Scale Precip

and sum over a month to obtain a total monthly precipitation
amount.

• Compare with CPC 0.25x0.25 degree gridded precipitation analysis.

# January **Precipitation Comparison** 

• Rainfall predicted with good skill in all three runs.

• Rainfall overpredicted in all three runs over cenrapN subdomain

.

# Mar **ch Pr ecipitation**  **Comparison**

• Rainfall predicted with good skill in all three runs.

• Rainfall overpredicted in all three runs over desertSW subdomain

.

• All three runs underpredict the maximum over Arkansas.

# July **Pr ecipitation**  **Comparison**

• Rainfall overpredicted in all three runs over most of the U.S

.

• Convective rainfall is over

predicted

• Problem most severe in the WRAP r un

• Overprediction of rainfall associated with wet humidity bias seen in surface plots

# October **Precipitation Comparison** 

• Rainfall predicted with fair skill in all three runs.

• Rainfall underpredicted in all three runs over CENRAP region

.

• All three runs underpredict the maximum over Louisiana.

• Metstat shows dry bias in cenrapS subdomain humidity for this month

# December **Precipitation Comparison** 

• Rainfall predicted with good skill in all three runs.

• Rainfall overpredicted in all three runs over cenrapN subdomain

.

# Summary **of Precipitation**  **Comparison**

• In winter , all three runs showed considerable skill in reproducing the observed precipitation field.

• In summer all runs had a wet bias, particularly in the southern U.S. This bias was strongest in the WRAP r un.

• This precip bias is consistent with the wet bias in the metstat surface humidity statistics for SW , DesertSW

, and North.

• All model runs produced too much rain over CenrapN region in winter , and underpredicted maxima in the CenrapS region in fall and spring.

# Preliminary Conclusions from 2002 MM5 **Comparison of CENRAP,VISTAS, and WRAP** 

• 

# Overall, **VISTAS performed**  **best. Unclear whether this is**  **due to the large**

**- scale**  **moist physics or convection**  **schemes or to the interaction**  **between them, or to effects**  **of differences in FDDA.** 

• 

# CENRAP **performance fell**  **between VISTAS and**  **WRAP.**

• 

# In all **runs, MM5 performed better in winter than in** 

**summer.**

• 

# All **three runs had excessive convective**  **rainfall in** 

**summer**

# Preliminary Conclusions from 2002 MM5 **Comparison of CENRAP,VISTAS, and WRAP** 

• 

# Although WRAP **had the best surface statistics for**  **temperature, upper air performance**  **was worst, which**  **suggests that nudging surface temperature is not a**  **good idea.**

• 

# A new **WRAP 2002 36/12 km run has been made to address issues of performance**  **in the western U.S., particularly**  **in summer, and does not include surface analysis**  **nudging of temperature and mixing ratio**  **and the Betts** 

**- Miller**  **cumulus scheme.**