Iowa DNR 2002 Annual Iowa DNR 2002 Annual 

MM5 Modeling Project MM5 Modeling Project

Matthew Johnson Matthew Johnson

Iowa Department of Natural Resources Iowa Department of Natural Resources 

Air Quality Bureau Air Quality Bureau

Regional Modeling Group Regional Modeling Group

matthew.johnson@dnr.state.ia.us
Overview Overview

 1) Basic info

– Configuration review

• Domain• Input data• Physics options

– Computational requirements

 2) Brief implementation discussion 3) Evaluation

– Metstat (surface statistics)– Raobplot (upper air)

 4) Summary & Conclusions
Domain

y National RPO specsy 36 km resy Projection

 -97°, 40° 33°, 45°

y Dimensions

 E-W 165 N-S 129
Data Sources Data Sources

y First-guess fields (REGRID)

 Eta 3D and surface analysis data (ds609.2 NCEP global tropospheric data (ds083.0)

– Provides ONLY the sea surface temperatu

• Eta data uses skin temperatures, which ca

by up to 12o C from observed SSTs along 

y Observational enhancement (LITTLE_R)

 NCEP ADP surface obs (ds464.0)

– Land = List E– Ship = List F

 NCEP ADP upper air obs (ds353.4)

– List A
Physics Options Physics Options

y Results from the cooperative sensitivity pr

yielded the following ‘optimum’ configura

Physics Option Selection configur

Moisture Simple Ice IMPHYS 

(MPHYST

Cumulus Kain-Fritsch ICUPA = Planetary Boundary Layer Pleim-Chang (ACM) IBLTYP Radiation RRTM FRAD = Land Surface Model Pleim-Xiu ISOIL = Shallow Convection No ISHALLNudging (Sfc and Grid) Yes FDDAGD 
Additional Configuration Choice

Additional Configuration Choice

y Initialization at 12 Z with 5 day runtime y Vertical structure

 34 Levels - Exactly matched to Dennis McNally’s 

configuration for EPA 2001 run

y PX LSM options

 Soil temperature and soil moisture fields continu

– Requires INTERPX– Indirectly nudged using surface conditions

y Additional details and discussion in the protocol

– http://www.state.ia.us/epd/air/prof/progdev/RegionMod– (Includes modeling system scripts)
Vertical Structure Vertical Structure

Level Si

gma

Hei

ght

(m)

p

(mb)

D

e

pth

(m) Level Si

gma Height (m)

p

(mb)

34 0.000 14662 100 1841 16 0.820 1400 838 33 0.050 12822 145 1466 15 0.840 1235 856 32 0.100 11356 190 1228 14 0.860 1071 874 31 0.150 10127 235 1062 13 0.880 911 892 30 0.200 9066 280 939 12 0.900 753 910 29 0.250 8127 325 843 11 0.910 675 919 28 0.300 7284 370 767 10 0.920 598 928 27 0.350 6517 415 704

9 0.930 521 937 

26 0.400 5812 460 652

8 0.940 445 946 

25 0.450 5160 505 607

7 0.950 369 955 

24 0.500 4553 550 569

6 0.960 294 964 

23 0.550 3984 595 536

5 0.970 220 973 

22 0.600 3448 640 506

4 0.980 146 982 

21 0.650 2942 685 480

3 0.985 109 987 

20 0.700 2462 730 367

2 0.990 73 991 

19 0.740 2095 766 266 1 0.995 36 996 18 0.770 1828 793 259

0 1.000

0 1000

17 0.800 1569 820 169

 ps = 1000 mb; Ptop = 100 mb; Ts = 275 K; A = 50 K/{ln(PA)}
Temporal Structure Temporal Structure

y Annual run consists of four quarters, each quarter en

first of a month 

 Dec 29th 2001 - Apr 1st 2002  Mar 14th 2002 -  Jun 14th 2002 - Oct 1st 2002  Sep 14th 2002 - 

y Each quarter consists of 27 five day blocks = 108 5 day Quarterly applications run sequentially (due to PX coy Quarters run simultaneously using 4 workstations

Start BLOCK End BLOCK Start (Z) End (Z) Filename Hrs Spin

Start (Z) End (Z) Start (Z) En

12/13/01 12:00 12/18/01 12:00 12/13/01 12:00 12/13/01 12:00 MMOUT_DOMAIN1_00

12/13/01 13:00 12/14/01 12:00 MMOUT_DOMAIN1_01 12/14/01 0:00 12/14/01 12:00 13 11 12/14/01 5:00 12/14/

12/14/01 13:00 12/15/01 12:00 MMOUT_DOMAIN1_02 12/14/01 13:00 12/15/01 12:00 24 12/14/01 13:00 12/15/

12/15/01 13:00 12/16/01 12:00 MMOUT_DOMAIN1_03 12/15/01 13:00 12/16/01 12:00 24 12/15/01 13:00 12/16/12/16/01 13:00 12/17/01 12:00 MMOUT_DOMAIN1_04 12/16/01 13:00 12/17/01 12:00 24 12/16/01 13:00 12/17/12/17/01 13:00 12/18/01 12:00 MMOUT_DOMAIN1_05 12/17/01 13:00 12/17/01 23:00 11 12/17/01 13:00 12/18

12/17/01 12:00 12/22/01 12:00 12/17/01 12:00 12/17/01 12:00 MMOUT_DOMAIN1_00

12/17/01 13:00 12/18/01 12:00 MMOUT_DOMAIN1_01 12/18/01 0:00 12/18/01 12:00 13 11 12/18/01 5:00 12/18/

12/18/01 13:00 12/19/01 12:00 MMOUT_DOMAIN1_02 12/18/01 13:00 12/19/01 12:00 24 12/18/01 13:00 12/19/

12/19/01 13:00 12/20/01 12:00 MMOUT_DOMAIN1_03 12/19/01 13:00 12/20/01 12:00 24 12/19/01 13:00 12/20/12/20/01 13:00 12/21/01 12:00 MMOUT_DOMAIN1_04 12/20/01 13:00 12/21/01 12:00 24 12/20/01 13:00 12/21/12/21/01 13:00 12/22/01 12:00 MMOUT_DOMAIN1_05 12/21/01 13:00 12/21/01 23:00 11 12/21/01 13:00 12/22

Metstat Usable 
Computational Requirements Computational Requirements

y CPU

 Each 5 day block requires ~ 42 hours run time

*

 4536 CPU hours (189 CPU days) required for com

– 47.25 CPU days per quarter

y Storage

 Input data: 2.9 Gb per month MM5 output: ~ 3.4 Gb per 5 day blo Intermediate: ~ 4.0 Gb per 5 day blo TOTAL: ~ 800 Gb 

MM5 output only: ~ 354 Gb

* 1 Ghz Pentium III machines, 512 Mb ram 1 Ghz Pentium III machines, 512 Mb ram
Methods Methods

y A brief overview of the steps comprising the annu

process

 Determine the modeling system configuration 

– Accomplished through the MM5 sensitivity project

 Overview of subsequent tasks

1) Evaluate computational requirements2) Obtain necessary input data3) Determine the structure of blocks & quarters4) Process the first-guess data via pregrid5) Build and execute the master and slave scripts6) Be patient7) Evaluate results
Methods Methods

y Input data:

– Each month requires 12 input files (occupie

• 3 Eta files (two 3D files, one Sfc file)

• 1 NCEP global analysis file

• 6 Sfc ADP files

• 1 Ship obs ADP file

• 1 ADP upper air obs file 

 Process quarterly first-guess data throug

PREGRID

– This process is independent of when the M

blocks start/end 

– Make certain that all *.tm12 Eta files are 
Methods Methods

y Scripting invaluable...

– The master script is responsible for automa

quarterly simulations

• Assigns starting/stopping dates• Runs each piece of the MM5 modeling syste• Archives data• Ensures correct termination of MM5 before 

continuation

• Designed to optimize use of computational r

– The MM5 preprocessors are controlled via 

master

• Slight modifications made congruent with th
Master Script Master Script
...Master Script & LITTLE_R (as an example MM5 s

Master Script & LITTLE_R (as an example MM5 s
Results Results

y Model Performance

 A thorough evaluation of all meteorological data 

both objective and subjective evaluation of many across the entire modeling domain

– The time requirements for a thorough evaluation a

staggering, thus decisions must be made regarding nature of evaluation efforts

y Focus → The statistical evaluation produced via 

Metstat, in combination with analysis of observepredicted soundings
Statistical Model Evaluation Statistical Model Evaluation 

- Sub Do

Sub Do

•12 Sub-domains

•CenrapN•CenrapS•DessertSW•GL•Iowa•MidAtlantic•North•NE•OhioVal•PacificNW•SE•SW

• Metstat data processed & plotted over all domains

PacificNW

SW

North

DessertSW CenrapS

CenrapN

GL

SE

O

Iowa
Hourly Results: CenrapN (Ws) Results: CenrapN (Ws)

Observed/Predicted Windspeed

02468

 1/ 31/ 41/ 51/ 61/ 71/ 81/ 9 1/101/111/121/131/14 1/15 1/16 1/17 1/18 1/19 1/20 1/21 1/22 1/23 1/24 1/25 1/

m/s

Ob

Observed/Predicted Windspeed

05

1015

 3/ 1 3/ 2 3/ 33/ 43/ 5 3/ 6 3/ 73/ 83/ 9 3/103/113/12 3/13 3/143/15 3/16 3/17 3/18 3/19 3/20 3/21 3/22 3/23 3/24 3/25 3/263

m/s

Ob

Bias Windspeed

-1.5

-1

-0.5

0

0.5

1

 8/ 1 8/ 2 8/ 38/ 4 8/ 5 8/ 6 8/ 78/ 8 8/ 9 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/188/19 8/20 8/21 8/228/23 8/24 8/25 8/26

m/s

RMSE Windspeed

0123

12/ 112/ 212/ 312/ 412/ 512/ 612/ 712/ 812/ 912/10 12/1112/1212/13 12/1412/1512/16 12/1712/18 12/1912/2012/21 12/2212/23 12/2412/2512/26 1

m/s

RMSEWndSpd RMSES
Results: CenrapN (T) Results: CenrapN (T)

Observed/Predicted Temperature

230240250260270280290

 2/4

 2/ 5

 2/6

 2/ 7

 2/8

 2/ 9

 2/10

 2/11

 2/12

 2/13

 2/14

 2/15

 2/16

 2/17

2/18

 2/19

2/20

 2/21

2/22

 2/23

2

K

Bias Temperature

-3-2-1

0123

 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

K

BiasTemp 

RMSE Temperature

012345

6/ 1

 6/ 2 6/ 36/ 4

6/ 5

6/ 6

6/ 7

6/ 8

6/ 9

6/10

6/11

 6/12

6/13

 6/14

 6/15

 6/166/17

6/18

 6/196/20

 6/21

 6/22

 6/23

 6/24

 6/25

 6/266/27

6/28

 6/29

 6/30

K

RMSETemp RMSESTemp RMSEUTemp 

Bias Temperature

-3-2-1

0123

 9/ 1

9/ 2

 9/ 3

 9/ 4

 9/ 5

 9/ 6

 9/ 7

9/ 8

 9/ 9

9/10

9/11

9/12

9/13

 9/14

9/15

9/16

9/17

9/18

9/

K

RMSE Temperature

01234

 9/ 1

9/ 2

 9/ 3

9/ 4

 9/ 5

9/ 6

9/ 7

 9/ 8

9/ 9

 9/10

 9/11

 9/12 9/13

 9/14

 9/15

 9/16

 9/17

 9/18

 9/

K

Observed/Predicted Temperature

250260270280290300

11/ 1

11/2

11/3

11/4

11/5

11/ 6

11/ 7

11/ 8

11/9

11/10

11/11

11/12

11/13

11/14

11/15

11/16

11/17

11/18

11/19

11/20

11/21

11/22

11/23

11/24

11/25

11

K

Observed/Predicted Temperature

240250260270280290

 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

K

ObsTemp 
Results: CenrapN (q) Results: CenrapN (q)

Predicted/Observed Humidity

012345

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

g/kg

Predicted/Observed Humidity

02468

101214

5/ 2

5/ 3

5/ 4

 5/ 5

 5/ 6

 5/ 7

 5/ 8

 5/ 9

5/10

5/11

5/12

5/13

5/14

5/15

 5/16

 5/17

 5/18

 5/19

 5/20

 5/21

 5/22

 5/23

 5/24

 5/25

g/kg

Bias Humidity

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

7/ 1

 7/ 2

7/ 3

7/ 4

 7/ 57/ 6

 7/ 7

 7/ 8

7/ 9

7/10

7/11

7/12

7/13

7/14

 7/157/16

7/17

 7/187/19

7/20

 7/21

7/22

7/23

 7/24

7/25

 7/26 7/27

7/28

7/29

 7/30

7/31

g/kg

BiasHum 

RMSE Humidity

0

0.5

1

1.5

2

2.5

3

3.5

7/ 1

7/ 2

 7/ 3

7/ 47/ 57/ 6

 7/ 77/ 8

7/ 9

7/10

 7/11 7/12 7/137/14

 7/15 7/167/17

 7/18 7/19 7/20

 7/21 7/22 7/237/24

 7/25 7/267/27

 7/28 7/29

 7/30

 7/31

g/kg

RMSEHum RMSESHum RMSEUHum 

Predicted/Observed Humidity

0123456

12/ 112/ 2

12/ 3 12/ 412/ 512/ 6

12/ 7

12/ 812/ 9 12/10

12/11

12/12

12/13 12/14 12/15

12/16 12/17 12/18

12/19

12

g/kg

RMSE Humidity

0

0.10.20.30.40.50.60.70.80.9

1

12/ 1

12/ 2

12/ 3

12/ 4

12/ 5

12/ 6

12/ 7

12/ 8

12/ 9 12/10

12/11 12/12

12/13

12/14

12/15

12/16

12/17 12/18

12/19

12/

g/kg
CenrapN Hourly Results Summar

CenrapN Hourly Results Summar

y CenrapN

 Winter T prediction too cool 

– Lows too low, high temp prediction variab

 Winter moisture prediction elevated vs ob From late Spring through Fall: T&q pred

improved

– Towards December, cool bias returns 

→ 

resulting from lows being too cold

 Throughout the year, a positive moisture b

emerges; errors generally within acceptab

 Wind fields almost always within acceptab

throughout the annual simulation

– Worst trend: tendency to underpredict day

max – nighttime wind fields acceptable
Results: CenrapS Results: CenrapS

Observed/Predicted Wind Direction

060 120 180 240 300 360

 1/ 1 1/ 2 1/ 31/ 4 1/ 5 1/ 6 1/ 7 1/ 8 1/ 9 1/10 1/111/12 1/13 1/14 1/15 1/16 1/17 1/18 1/191/20 1/21 1/221/23 1/24 1/25 1/26 

deg

RMSE Temperature

012345

 3/ 1 3/ 2 3/ 3 3/ 4 3/ 5 3/ 6 3/ 7 3/ 83/ 9 3/10 3/11 3/123/13 3/14 3/15 3/163/173/18 3/19 3/20 3/213/22 3/23 3/24 3/25 3/26 3/27 3/28 3/29 3/30 3/31

K

RMSETemp RMSESTemp RMSEUTemp 

Bias Humidity

-1

-0.5

0

0.5

1

1.5

2

 3/ 13/ 2 3/ 3 3/ 4 3/ 5 3/ 6 3/ 7 3/ 8 3/ 9 3/103/11 3/12 3/13 3/14 3/15 3/16 3/17 3/18 3/19 3/20 3/21 3/22 3/23 3/24 3/253/26 3/27 3/28 3/29 3/30 3/31

g/kg

BiasHum 

Bias Temperature & Humidity

-4-3-2-1

0123

 6/ 16/ 2 6/ 36/ 4 6/ 56/ 6 6/ 76/ 8 6/ 9 6/106/11 6/126/13 6/14 6/15 6/16 6/176/18 6/

K & g/kg

Out of Pha

Observed/Predicted Temperature

285290295300305310

 8/ 1 8/ 2 8/ 3 8/ 4 8/ 5 8/ 6 8/ 7 8/ 8 8/ 9 8/10 8/11 8/12 8/13 8/14 8/15 8/16 8/17 8/18 8/1

K

T too cool (both 

Predicted/Observed Humidity

02468

101214

11/ 111/ 211/ 311/ 4 11/ 511/ 611/ 711/ 8 11/ 9 11/10 11/11 11/12 11/13 11/14 11/15 11/16 11/17 11/18 1

g/kg

Bias Windspeed

-3-2-1

01

11/ 1 11/ 2 11/ 3 11/ 4 11/ 5 11/ 6 11/ 7 11/ 8 11/ 9 11/10 11/11 11/12 11/13 11/14 11/15 11/16 11/17 11/18 11/19 11/20 11/21 11/22 11/23 11/24 11/25 11/26 11/27 11/28 11/29 11/30

m /s

BiasWndSpd
CenrapS: Diurnal Moisture ‘Trend

CenrapS: Diurnal Moisture ‘Trend

Humidity Prediction 

CenrapS, July 2002 

14.515.015.516.016.517.017.5

7/16/98

12:00 AM

7/16/98

12:00 PM

7/17/98

12:00 AM

7/17/98

12:00 PM

7/18/98

12:00 AM

7/18/98

12:00 PM

7/19/98

12:00 AM

7/19/98

12:00 PM

Mixing Ratio g/kg

Humidity Prediction 

CenrapS, Augst 2002 

12.012.513.013.514.014.515.015.516.016.517.0

8/4/02

12:00

AM

8/4/02

12:00

PM

8/5/02

12:00

AM

8/5/02

12:00

PM

8/6/02

12:00

AM

8/6/02

12:00

PM

8/7/02

12:00

AM

8/7/02

12:00

PM

8/8/02

12:00

AM

8/8/02

12:00

PM

Date

Mixing Ratio g/kg
CenrapS Hourly Results Summar

CenrapS Hourly Results Summar

y Wind speeds consistently underpredicted

throughout year (gross error generally < 

y Temperature

 Winter prediction improved vs CenrapN Jan - May: prediction acceptable, with no 

predominant biases emerging

 June through Mid September: neg biases  Mid-Sep - Dec: neg T bias erodes, improv

temperature prediction
CenrapS Hourly Results Summar

CenrapS Hourly Results Summar

y Mixing ratio

 Jan-May mixing ratio predominantly pos

biased

 May-Sep: q prediction improves, but epis

degraded performance occur

 Oct-Dec mixing ratio prediction improves When biases occur, T&q predominantly o

phase

 Hourly and episodic performance variabi

dominant feature in moisture fields
Daily Averaged Data Daily Averaged Data

y While certain nuances and biases exist in 

CenrapN and CenrapS regions, in generasimulation is tolerable over these regions

y ...Time to examine other areas of the coun

 Focusing upon hourly statistics far too tim

consuming

 Daily statistics only partially improves th

problem

– Solution: Monthly averaged statistics

• (caveat...not a perfect solution)
-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Wind Speed Bias

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

m/s
0

0.5

1

1.5

2

2.5

3

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Wind Speed Gross E

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

m/s
-20

-15-10

-5

05

101520

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Wind Direction Bia

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPacSW

Degrees
0

5

1015202530354045505560657075

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Wind Direction Gross 

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

Degrees
-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Temperature Bias

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

o 

C
0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Temperature Gross E

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPacSW

o 

C
-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Mixing Ratio Bias

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

g/kg
0

0.5

1

1.5

2

2.5

3

3.5

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Monthly Averaged Mixing Ratio Gross E

CenrapNCenrapSDessertSW

GLIowaMidAtlantic

NorthNEOhioVal

SEPaSW

g/kg
Continuous PX Soil Moisture Field

Continuous PX Soil Moisture Fiel

y Due to inclusion of ~14 day spin up perio

the beginning of each quarter, the impact continuous soil fields can be examined

 ...by comparing the 1st 5 day simulation 

a quarter with the corresponding data froprevious quarterly simulation

– March, June, and September time periods 

available

Example

JFM AMJ

24th Sim: March 15th-19th 

1st Sim: March 15
Continuous Soil Fields vs. Re-Initialization:

Temperature Bias

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o

C

CenrapN-CCenrapN-I

Continuous Soil Fields vs. R

Temperature Bi

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 

o

C

C ontinuous Soil Field s v s. R e-Initialization:

Tem perature Bias

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o

C

DessertSW -CDessertSW -I

Continuous Soil Fields vs. R

Temperature Bi

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 

o

C

Continuous Soil Fields vs. Re-Initialization:

Temperature Bias

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 6/18 9/15 9/16 9/17 9/18

o

C

Iowa-CIowa-I

Continuous Soil Fields vs. R

Temperature Bi

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 

o

C
Continuous Soil Fields vs. R

Temperature Bia

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3/15 3/16 3/17 3/18 6/15 6/16 6/17 

o

C

Continuous Soil Fields vs. Re-Initialization:

Temperature Bias

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

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Temperature Bia

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Continuous Soil Fields vs. Re-Initialization:

Temperature Bias

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Continuous Soil Fields vs. R

Temperature Bia

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Continuous Soil Fields vs. Re-Initialization:

Temperature Bias

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NE-CNE-I
Continuous Soil Fields vs Re Continuous Soil Fields vs Re

-Initializ

Initializ

Temperature Temperature

y Temperature Bias

 In general, re-initialization reduces the mag

the negative and positive biases

– Sign reversal occasionally occurs (Iowa:Mar

 In June, CenrapN shows a noticeable disben

re-initialization (not using continuous fields

y Temperature Error

 Overall, there isn’t a striking decrease in er

re-init; Most comparisons show similar per

 However, if errors differed, the errors were 

always lower with re-initialization (except wCenrapN & Iowa)
Continuous Soil Fields vs Re Continuous Soil Fields vs Re

-Initializ

Initializ

Temperature Error Temperature Error

Continuous Soil vs Re-Initialization: 

Temperature Gross Error

0

0.5

1

1.5

2

2.5

3

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Degrees C

CenrapN-CenrapN-CenrapS-CenrapS-MidAtlantMidAtlantNE-CNE-I

Regions not shown did not display significant changes
Moisture Moisture – Impact Not as Noticeab

Impact Not as Noticeab

Continuous Soil Fields vs. Re-Initialization:

Moisture Bias

-2.5-1.5-0.5

0.51.52.5

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CenrapN-CCenrapN-I

Continuous Soil Fields vs. Re-Initialization:

Moisture Bias

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DessertSW-CDessertSW-I

Continuous Soil Fields vs. R

Moisture Bias

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oC

Continuous Soil Fields vs. Re-Initialization:

Moisture Bias

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Continuous Soil Fields vs. R

Moisture Bias

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1

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Continuous Soil Fields vs. R

Moisture Bias

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oC
Continuous Soil Fields vs Re Continuous Soil Fields vs Re

-Initializ

Initializ

Mixing Ratio Mixing Ratio

y Little change overall

 Bias tends to be slightly worse with re-initializati Worst impact occurred in the CenrapN region, w

increasing by up to ~0.5 g/kg (total error still lesg/kg

)

y Majority of gross error differences < 0.2 g/kg
Upper Air Evaluation Upper Air Evaluation

y Reprocessing 360 Gb of output for upper air eva

not practical

y Partial solution

 Developed RAOBPLOT 

(freely available

)

– Post-processing software tool designed to produce 

vs predicted soundings, based on FSL archived da

• Acknowledgements:

– Dennis McNally (borrowed portion of code fro

which reads TDL datasets)

– The authors of GRAPH (borrowed SKEW-T c

– Mark Stoelinga (borrowed code from RIP)– (I did actually write new code)– Bret Anderson – beta tester/accessory improve

 Tremendous amount of information producible 

~18,000 soundings created examining only 23 sta
Results Results

y Preliminary findings: (finite number of soundings e

 Wind field prediction throughout the troposphere su

categorized as accurate

 The cool temperature bias & positive moisture bias i

through Metstat evaluation identifiable in the sound

– The depth through which this trends occur is highly v

25 mb to 200+ mb above the surface)

– Results highly variable, as expected

 Examining DVN (Davenport, Iowa) summertime PB

layer appears slightly shallow

– Typically shifted by 50 mb or less
Example RAOBPLOT Example RAOBPLOT

y Davenport, IAy 2002-07-15_00Zy An example of the 

cold bias/high moisture bias, and PBL depth inaccuracies (at 7 PM...)

y Note: No single 

sounding is representative
Summary Summary

 Overall, investigation of: wind fields, temperatures, a

gross errors found to be within acceptable limits. How

 Bias trends apparent

– Temperatures underpredicted (cause=location and time 

Feb PacificNW, the entire diurnal cycle too cold; not justemperatures being underpredicted)

– Mixing ratio positively biased throughout most of the an

simulation: true for most regions

– Daily peak wind speeds fall short of the observed values, 

associated error is generally small

 Upper Air

– Wind field prediction acceptable, throughout the troposp– Preliminary... Highly variable with mixed results, no gri

trends yet discovered
Conclusions Conclusions

y CenrapN: overall, performance acceptabley Other regions: Biases apparent, but associated error

tolerable (must consider the course grid resolution)

y SIP quality simulation?

 There is room for improvement... The ultimate answer will depend upon a performance 

evaluation comparison involving additional annual sim

y Implementation of continuous soil fields within PX

 Is this option favorable?

– Generally: appears beneficial for CenrapN & Iow– Performance measures do not indicate the generat

continuous soil fields is an advantageous practice

• Caveat: only 12 days available for comparison

– Couple the above statements with operational 

restrictions

• ...the computational burden incurred with the 

sequential run requirement difficult to justify