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)