# Comparison of CENRAP **36 km and 12 km MM5 Model Runs for 2002** 

Jeremiah Johnson, Yiqin Jia, Chris Emery and Ralph Morris

ENVIRON International Corporation Zion Wang and Gail Tonnesen University of California at Riverside 

# Central Regional Air Planning Association (CENRAP)

**Modeling Workgroup**  **May 23, 2006**

# Introduction

• 

# CENRAP **is performing regional**  **Particulate Matter**  **(PM) modeling to address the requirements**  **of the Regional Haze Rule (RHR)** 

• 

# Both the CMAQ and CAMx **models are being applied for the 2002 annual period on a 36 km continental**  **U.S. domain** 

• 

# 12 km sensitivity **modeling was conducted for three episodes**  **on a Central States domain:**

> 

### Spring: March 7 th 

### to April 23 th
> 

### Summ er: August 27 th 

### to Septem ber 13 th
> 

### Winter: Novem

ber 26 th 

### to Decem

ber 15 th



## • 

# Model Performance **was compared using 36 km and 12 km**  **grids**

• 

# CAMx 12 km flexi
**- nesting**  **was used to determine**  **whether effects**  **of 12 km domains are due to 12 km meteorology or**  **emissions**

# 12 km Sensitivity Results

• 

# Presented at Feb 6
**- 8, 2006 CENRAP**  **meeting in Baton Rouge:** 

> http://pah.cert.ucr.edu/aqm /cenrap/ppt_files/Morris_36vs12km_Feb6

- 8_2006.ppt


# • 

# CMAQ model performance fairly insensitive to **36 km and 12 km**  **grid**

• 

# CAMx exhibited more sensitivity **wh en**  **12 km wa**  **s used**
> 

# Summer SO4 over

- prediction bias greatly reduced using 12 km grid > 

# CAMx flexi

- nesting (12 km meteorology w/ 36 km emissions) found improved SO4 performance due to 12 km meteorology and not 12 km emissions

# Recommendations: 12 km Modeling

• 

# Key Findings:
> 

# Except for CAMx SO4 little difference in model performance using 36 and 12 km grid
> 

# Substantial more resources needed for 12 km grid
> 

# Effects of grid on emission controls not nested

• 

# Feb 6
**- 8, 2006 Baton Rouge CENRAP**  **Meeting Recommended the Following:** 
> 

# Perform most runs using just 36 km grid
> 

# Process 12 km MM5 for modeling in case needed

• 

# Need to evaluate 12 km MM5 data
> 

# If 12 km runs are needed, results suggest can just run with 12 km meteorological inputs

# Model Setup for CENRAP Runs

• 

# National RPO 36 km Grid, 164 x 128 x 34

• 

# 12 km Grid, 264 x 240 x 34

• 

# Lambert Conformal Projection

• 

# Pleim
**- Xiu/ACM LSM/PBL** 

• 

# RRTM Longwave **and Dudhia Shortwave**

# Model Setup for CENRAP **Runs (continued)**

• 

# No shallow convection

• 

# Kain
**- Fritsch**  **2 cumulus convection**

• 

# Reisner 1 moist physics

• 

# Nudging analysis: u/v surface

**u/v/t/q aloft** 

# 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 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

Wi nd Speed Wi nd Di re ction Te mp er atur e Hu mi dity 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** 

sur face tem perature sur face pressur e sea l ev el p re ssur e visibility cl oud c over height of cloud base ma xim um temperat ur e mi nimum temperat ure dewpoi nt preci pi tation sunshi ne snowfal l snow cover wind di rection wind force (speed) u wi nd component v wind com ponent wind gust descr iptive weathe r

# MM5 36 km vs. 12 km Model Performance

• 

# Previously **evaluated CENRAP 36 km MM5 simulation**  **across US domains and compared results**  **with VISTAS and WRAP 36 km MM5 runs:** 

> 

## http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VIST **AS_WRAP_2002_36km_MM5_eval.ppt**



## • 

# Now **evaluate CENRAP**  **12 km MM5 and compare with**  **36 km MM5 results:**
> 

# CenrapN and CenrapS domains
> 

# Wi nd Speed and Direction, Temperature and Moisture
> 

# Compare against performance “Benchmarks”
> 

# Follows are examples for Jan, Mar, Jul and Oct 2002

### CE **NRAP 3**  **6km v**  **s. 1**  **2km J**  **an ua**  **ry Te**  **mp er**  **at ur**  **e Pe**  **rf orm**  **an ce**  **Co mp**  **ar is** 

**on**

0 0.5 1 1.5 2 2.5 3 3.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Te mp**  **era tur**  **e Bi**  **as (**  **K) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Be nc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Highest temperature error for CenrapN subdomain in January

### CENRAP **3 6km**  **v s.**  **1 2km**  **J an**  **ua ry**  **Hu mid**  **it y**  **Pe rf**  **orm an**  **ce C**  **om pa**  **ri so**  **n**

0 0.5 1 1.5 2 2.5 3

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5 **Hu mi**  **di ty**  **B ias**  **( g/**  **kg )** **H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

Be nc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable humidity performance for both runs

### CE **NRAP 3**  **6km v**  **s. 1**  **2km J**  **an ua**  **ry Wi**  **nd Pe**  **rf orm**  **an ce**  **C om**  **pa ri**  **so n** 

0 10 20 30 40 50 60 70 80

0 0.5 1 1.5 2 2.5 3 **Wi nd**  **Sp eed**  **R**

**MS E**  **(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 ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable wind performance for both runs**CENRAP 3**  **6km v**  **s. 1**  **2km Ma**  **rc h**  **Te mp**  **er at**  **ure Pe**  **rf orm**  **an ce**  **C om**  **pa ri**  **so n** 

0 0.5 1 1.5 2 2.5 3 3.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Te mp**  **era tur**  **e Bi**  **as (**  **K) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Benc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • CenrapN subdomain outside goal, but better than January

### CE **NRAP 3**  **6km v**  **s. 1**  **2km Ma**  **rc h**  **Hu mid**  **it y**  **Pe rf**  **orm an**  **ce C**  **om pa**  **ri so**  **n**

0 0.5 1 1.5 2 2.5 3

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5 **Hu mi**  **di ty**  **B ias**  **( g/**  **kg )** **H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

Be nc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable humidity performance for both runs

### CE **NRAP 3**  **6km v**  **s. 1**  **2km Ma**  **rc h**  **Wi nd**  **Pe rf**  **or ma**  **nc e**  **Co mp**  **ar is** 

**on**

0 10 20 30 40 50 60 70 80

0 0.5 1 1.5 2 2.5 3 **Wi nd**  **Sp eed**  **R**

**MS E**  **(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 ar k 36k m 12k m CE NRA P N CE NRA P S 

### • CenrapS subdomain falls just within goal**CE NRAP**  **3 6km**  **v s.**  **1 2km**  **J ul**  **y Te**  **mp er**  **at ure**  **Pe rf**  **orm an**  **ce C**  **om pa**  **ri so**  **n**

0 0.5 1 1.5 2 2.5 3 3.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Te mp**  **era tur**  **e Bi**  **as (**  **K) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Benc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Greatest temperature performance improvement using 12km grid

• CenrapS outside goal for 36km run

### CE **NRAP 3**  **6km v**  **s. 1**  **2km J**  **ul y**  **Hu mid**  **it y**  **Pe rf**  **orm an**  **ce C**  **om pa**  **ri so**  **n**

0 0.5 1 1.5 2 2.5 3

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5 **Hu mi**  **di ty**  **B ias**  **( g/**  **kg )** **H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

Be nc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Highest humidity error in July , still within goal**CENRAP 3**  **6km v**  **s. 1**  **2km J**  **ul y**  **Wi nd**  **Pe rf**  **or ma**  **nce C**  **om pa**  **ri so**  **n**

0 10 20 30 40 50 60 70 80

0 0.5 1 1.5 2 2.5 3 **Wi nd**  **Sp eed**  **R**

**MS E**  **(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 ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable wind performance for both runs

### CE **NRAP 3**  **6km v**  **s. 1**  **2km O**  **cto be**  **r Te**  **mp er**  **at ure**  **Pe rf**  **orm an**  **ce Co**  **mp ar**  **is**

**on**

0 0.5 1 1.5 2 2.5 3 3.5

-2 -1 .5 -1 -0 .5 0 0.5 1 **Te mp**  **era tur**  **e Bi**  **as (**  **K) T** **e m** **p e** **r a** **t u** **r e** **E r** **r o** **r (** **K )** 

Benc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Te mperature performance comparable for 36 and 12km runs

### CE **NRAP 3**  **6km v**  **s. 1**  **2km O**  **cto be**  **r Hu**  **mid it**  **y Pe**  **rf orm**  **an ce**  **C om**  **pa ri**  **so n** 

0 0.5 1 1.5 2 2.5 3

-1 .5 -1 -0 .5 0 0.5 1 1.5 2 2.5 **Hu mi**  **di ty**  **B ias**  **( g/**  **kg )** **H u** **m i** **d i** **t y** **E r** **r o** **r (** **g /** **k g** **)**

Benc hm ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable humidity performance for both runs

### CE **NRAP 3**  **6km v**  **s. 1**  **2km O**  **cto be**  **r Wi**  **nd Pe**  **rf orm**  **an ce**  **C om**  **pa ri**  **so n** 

0 10 20 30 40 50 60 70 80

0 0.5 1 1.5 2 2.5 3 **Wi nd**  **Sp eed**  **R**

**MS E**  **(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 ar k 36k m 12k m CE NRA P N CE NRA P S 

### • Comparable wind performance for both runs

# Overview **of CENRAP 36/12**  **km Surface**

**Performance**

• 

# Generally, **performance was**  **within the benchmarks over the two CENRAP**  **regions, and little**  **improvement was**  **gained with the addition of the 12km grid (exception**  **of 12 km temperature for July)** 

• 

# Both 36 and 12km runs had very similar **wind and**  **humidity performance**

• 

# Temperature **performance for the CenrapN**  **subdomain fell**  **outside the benchmark in January and March for both the 36 and 12km runs** 

# Overview **of CENRAP 36/12**  **km Surface**

**Performance**

• 

# Wind performance **slightly better**  **in the summer than in winter for both runs** 

• 

# Both runs display a cold bias in the winter and warm **bias in the summer over the CenrapN subdomain**

• 

# Humidity **performance is worse in summer than in the**  **winter for the 36 and 12 km runs**

# Overview **of CENRAP 36/12**  **km Surface**

**Performance**

• 

# Adding the 12 km CENRAP **grid resulted in only**  **modest performance increases for both CENRAP**  **subdomains**

• 

# 12 km grid requires significantly **more computer time**  **than for the 36 km coarse grid** 

• 

# It may not be worth the extra **processing time to include the 12km CENRAP**  **grid, especially for annual**  **modeling purposes.**

# CENRAP 36/12 **km Surface Performance** 

• 

# Evaluation of 36 and 12 km MM5 runs found:
> 

# Little difference in MM5 model performance
> 

# No new problems found with 12 km MM5 fields
> 

# Conclusions from CMAQ/CAMx 36/12 km modeling still hold:

• 

## For regional haze modeling of Class I areas the 36 km grid appears to be sufficient

• 

## Conclusions may not be true for 8

- hour ozone and PM 2.5 

## modeling of urban areas

– 

## Midwest and Southeast U.S. modeling indicates that higher resolution than 36 km (e.g., 12 km and/or 4 km) is needed for 8

- hour ozone and PM 2.5 

## issues