Christa D. Peters-LidardHead, Hydrological Sciences BranchNASA Goddard Space Flight Center

Workshop Objectives

1. Describe the LIS-WRF Coupled System2. Present example case studies using LIS-WRF

3. Understand WRF-CHEM status and plans4. Discuss how GSFC and UMD can collaborate on WRF

The LIS-WRF Coupled Testbed

Christa D. Peters-Lidard1, Sujay V. Kumar2,1, Charles J. Alonge3,1, Joseph A. Santanello, Jr.4,1, Joseph L. Eastman2,1, Wei-Kuo Tao4

1NASA Goddard Space Flight Center Hydrological Sciences Branch, Code 614.3

2University of Maryland at Baltimore CountyGoddard Earth Sciences Technology Center

3SAIC

4University of Maryland at College ParkEarth System Science Interdisciplinary Center

5NASA Goddard Space Flight Center Mesoscale Atmospheric Processes Branch, Code 613.1

Acknowledgements: NASA ESTO, NASA NEWS, AFWA

LSM Initial Conditions

WRFLSM Physics

(Noah, Mosaic, CLM2,

Catchment, VIC, HySSiB)

Coupled orForecast Mode

Uncoupled or Analysis Mode

Global, RegionalForecasts and (Re-)Analyses

Station Data

Satellite Products

ESMFMYJ, YSU, MRF

PBL Schemes

Kumar, Peters-Lidard et al, EMS, 2006; 2007.

LIS-WRF Testbed for Studying Land-Atmosphere Coupling

GCE, LIN, WSM Microphysics

Schemes

Topography,Soils

Land Cover, Vegetation Properties

Meteorology

Snow Soil MoistureTemperature

Land Surface Models

Data Assimilation Modules

Soil Moisture &

Temperature

Evaporation, Sensible Heat Flux

Runoff

SnowpackProperties

Inputs OutputsPhysics

LIS Overview

LIS Software Structure

Central US, Southern Great Plains

IHOP 2002 Case Study

LIS vs. WPS/NARR

NARR

WRF-Noah

WRF-LIS

Soils Vegetation

Initial Soil Moisture Differences

00Z June 12, 2002

LIS vs. WPS/NARR Initial Soil Moisture

NARR

WRF-Noah

WRF-LIS

Offline LIS/Noah Spin-Up Results

• Near-surface fields spin up quickly (about 1.5 years), however, longer spin-ups are needed it can take longer than 2 years for layers 3 and 4 to spin up

• The 2 year spin-up removes most of anomalies introduced by initialization with the NARR land surface states. Although, a three year simulation is recommended in semi-arid to arid regions where anomalies can persist much longer

• A noteworthy benefit of using LIS for offline spin-ups is the execution time for offline spin-ups (all simulations executed over 64 processors @ 1.25GHz each)

Spin-up Time

Wall Clock Hours

CPU Hours

6-month 2.1 65.8

1-year 4.15 148.6

2-year 8.1 296.8

3-year 12.2 409.3

• NLDAS/Stage 2/4 + STATSGO + Noah LSM => NSN

• NLDAS/Stage 2/4 + FAO + Noah LSM => NFN

• GDAS + STATSGO + Noah LSM => GSN

• GDAS + FAO + Noah LSM => GFN

199710

199710

199710

199710 200201

200201

200001

200001BERG

BERG GDAS

GDAS

NLDAS + STG2

NLDAS + STG2

STG4

STG4

IHOP LIS Spin-Ups

• LIS/WRF configuration:– Goddard Shortwave Radiation

Scheme– RRTM Longwave Radiation– Ferrier Microphysics– Mellor-Yamada-Janic PBL Scheme

(TKE based)– Monin-Obukov Surface Layer (Janic)– No cumulus parameterization

• 1km horizontal grid spacing –> 6 second time step

• 44 Vertical Levels • Radiation packages called every 60

seconds• LIS invoked at every time step• All simulation were initialized at 00Z and integrated out to 36 hours

LIS-WRF Configuration

Multiple networks were used to validate of the output of LIS/WRF simulations

IHOP Verification Data

Fair Weather Test CaseJune 6, 2002 Case

• Trough axis passing to east, anticyclonic vorticity advection -> subsidence

• Light surface winds -> good for examining impacts of land surface

Fair Weather Test Case Results

• NSN and GSN runs best for top two soil moisture layers

•GDAS runs validate best in the third soil moisture layer of Noah

•NARR good at 10cm, too dry below

Soil Moisture Evaluation

Soil Moisture Bias - 20020606

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1 2 3

Soil Level

Bia

s

NSN

NFN

GSN

GFN

NARR

Soil Moisture RMSE - 20020606

0.06

0.07

0.08

0.09

0.1

0.110.12

0.13

0.14

0.15

0.16

1 2 3

Soil Level

RM

SE

NSN

NFN

GSN

GFN

NARR

Fair Weather Test Case Results

• Goddard Shortwave Radiation scheme exhibiting a high bias in SWDN

• RRTM Longwave performs well with respect to LWDN (small high bias during the day and into the evening)

Downward Radiation Fluxes

SWDN Comparison

0

200

400

600

800

1000

1 4 7 10 13 16 19 22 25 28 31 34

Forecast Hour

(W/m

2)

SWDN WRF SWDN OBS

LWDN Comparison

330

340

350

360

370

380

390

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Forecast Hour

(W/m

2)

LWDN WRF LWDN OBS

Convective Test CaseJune 12, 2002 Case

• Light winds at the surface, southwesterly and westerly flow aloft

• Weak synoptic forcing

• Small Capping Inversion

• Difficult to forecast convective intiation

Convective Test Case Results

• NLDAS land analyses exhibiting more of a dry bias than the GDAS based runs

• NARR initial conditions too dry

• GDAS provides better initial soil moisture conditions for all three layers validated

Soil Moisture Evaluation

Soil Moisture Bias - 20020612

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1 2 3

Soil Level

Bia

s

NSN

NFN

GSN

GFN

NARR

Soil Moisture RMSE - 20020612

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1 2 3

Soil Level

RM

SE

NSN

NFN

GSN

GFN

NARR

Convective Test Case Results

Precipitation Verification

Used Stage II/IV analyses from NCEP

Convective Test Case ResultsPrecipitation Verification

Total Precipitation 20020612

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.01 0.1 0.25 0.5 0.75 1 1.5 2 3

Threshold (in.)

BIAS

Sco

re

NSN

NFN

GSN

GFN

Total Precipitation 20020612

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.01 0.1 0.25 0.5 0.75 1 1.5 2 3

Threshold (in.)

Prob

abili

ty o

f Det

ectio

n NSN

NFN

GSN

GFN

Total Precipitation 20020612

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.01 0.1 0.25 0.5 0.75 1 1.5 2 3

Threshold (in.)

Fals

e Al

arm

Rat

io

NSN

NFN

GSN

GFN

Total Precipitation 20020612

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.01 0.1 0.25 0.5 0.75 1 1.5 2 3

Threshold (in.)

Equi

tabl

e Th

reat

Sco

re NSN

NFN

GSN

GFN

Wet Soil Moistures

IntermediateSoil Moistures

Dry Soil Moistures

= MRF

= YSU

= MYJ

IHOP 2002 PBL vs. EF Stratified by Soil Moisture

= MRF

= YSU

= MYJ

x = 30% Veg

▪ = 60% Veg

o = 90% Veg

90%

30%

IHOP 2002 PBL vs. EF Stratified by GVF

Conclusions and Future Work

• LIS-WRF coupled system is a testbed for studying mesoscale land-atmosphere interactions

• Choice of parameters and spin-up data can have significant impacts on results

• In general, the GDAS runs outperformed the NLDAS runs (better fluxes and 2m temperature/dewpoint, and heaviest total precipitation amounts), which indicates spin-up forcing may be more important than the parameter datasets

• Interactions between various parameterizations (LSM, PBL, Radiation, Microphysics) complex and probably tuned.

• Currently working to add CLM2 runs to the series of experiments and NARR runs to the analysis

• Possibly need to explore object-based verification methods (Ebert and McBride 2002, Davis et al. 2006)

• Need to further examine the quality of each offline simulation (verify more than just the initial conditions)