a status report on the second global soil wetness project gswp-2 paul dirmeyer and xiang gao center...
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A Status ReportA Status Reporton the Secondon the Second
GlobalGlobal Soil Wetness Soil Wetness Project GSWP-2Project GSWP-2
Paul Dirmeyer and Xiang Gao Center for Ocean-Land-Atmosphere Studies
Calverton, Maryland, USA
Context Context
GLASSGCSSGABLS
(AMMA)(AMMA)
GAPP
CliC
COPES
GCM inter-comparisons
Single column model analyses
Land-surface model intercom-parisons (in situ)
Global griddedmodel analyses
GEWEX Global Land-GEWEX Global Land-Atmosphere System StudyAtmosphere System Study
Submitted
Imminent
Probably
Maybe
Bowed out
GSWP-2 Modeling StatusGSWP-2 Modeling StatusMODEL InstituteBucket University of Tokyo
CLM-TOP University of Texas at Austin
CBM/CHASM Macquarie University, Australia
CLASS Meteorological Service of Canada
CLM NASA GSFC/HSB
COLA-SSiB COLA
ECMWF ECMWF
HY-SSiB NASA GSFC/CRB
ISBA MétéoFrance/CNRM
LAPUTA Meteorological Research Institute, Japan Meteorological Agency
LaD USGS & NOAA/GFDL
MATSIRO Frontier RSGC
MECMWF KNMI (Dutch MetOffice), Netherlands
Mosaic NASA GSFC/HSB
MOSES-2 Met Office, UK
NOAH NOAA NCEP/EMC
NSIPP-Catchment NASA GSFC/NSIPP (GMAO)
ORCHIDEE IPSL, France
SiBUC Kyoto University
Sland University of Maryland
SPONSOR Institute of Geography, Russian Academy of Sciences
SWAP Institute of Water Problems, Russian Academy of Sciences
VIC U. Arizona
VISA, CLM-Top University of Texas at Austin
Sensitivity ExperimentsSensitivity Experiments
Computing and storage burdens are not trivial
Three suites of experiments A: 15 May 2004
B: 31 August
C: 15 October
Exp Description
N1 Native Parameters (if applicable)
P1 Hybrid ERA-40 precipitation (instead of NCEP/DOE)
P2 NCEP/DOE hybrid with GPCC corrected for gauge undercatch (no satellite data)
P3 NCEP/DOE hybrid with GPCC (no undercatch correction)
P4 NCEP/DOE precipitation (no observational data)
P5 NCEP/DOE hybrid with Xie daily gauge precipitation
R1 NCEP/DOE radiation
RS NCEP/DOE shortwave only
RL NCEP/DOE longwave only
R2 ERA-40 radiation
M1 All NCEP meteorological data (no hybridization with observational data)
M2 All ECMWF meteorological data (no hybridization with observational data)
V1 U.Maryland vegetation class data
I1 Climatological vegetationA
A
B
B
B
C
C
C
A
R3 ISCCP radiation
C PE Hybrid ERA-40 precip.
ERA-40 precipitation (no observational data)
P1 GlitchP1 Glitch
P1 correction! - The precipitation files to use for P1 were listed incorrectly. The files listed were not hybrid ERA-40 precipitation. They were the original ERA-40 precipitation. We have added a new experiment PE to represent what we had intended originally in P1.
We ask everyone doing P2 and/or P3 to perform PE as part of the precipitation suite. If you have already submitted Suite B, we ask for PE to be submitted as part of Suite C, with the 15 October deadline.
If you have already submitted P1, we will use it. It would be especially useful, though, if you also do P4. That will give us a direct comparison between the original ERA-40 and NCEP/DOE precipitation.
R3 AddedR3 Added
ISCCP radiation (thanks to Yuanchong Zhang and Bill Rossow for providing us with this data). This is an observationally-based alternative to the SRB radiation used in the baseline simulation.
It does not have the problems at the month boundaries that SRB does
It uses a different set of retrieval and QC algorithms than SRB.
You may wish to try this as an alternative to SRB or reanalysis radiation, but see the FAQ page for information on how the time averaging has been performed for this product (it is different than the other radiations). Please see the ISCCP web site for more information on this product.
Multi-Model AnalysisMulti-Model Analysis
1986-1995 daily mean fluxes, state variables at 1° over land (excl. Antarctica)
Consider all available land models (~16)Now investigating methods for
compositing (can we do better than simple average?)
Target: complete product by end of 2004
Unbiased Forecast VariantsUnbiased Forecast Variants Let {Xi(t), i=1,…M}
denote an ensemble of soil wetness forecasts produced by M models at a fixed location; an arbitrary linear combination of these forecasts is given by:
MitXaatF i
M
ii ,...,1),()(
10
Regression-improved individual forecast RRegression-improved individual forecast R
)()( 0 tXaatF iii
M
ii tX
MtF
1
)(1
)(
M
ii tXaatF
10 )()(
Regression-improved multimodel ensembleRegression-improved multimodel ensembleMean forecast RMean forecast REMEM
Arithmetic Average CArithmetic Average C
M
iii tXaatF
10 )()(
Regression-improved multimodel forecastRall
Kharin, V. V., and F. W. Zweirs, 2002, J. Climate, 15, 793-799.
Station1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Co
rrel
atio
n C
oef
fici
ent
0.0
0.2
0.4
0.6
0.8
1.0
Station1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
RM
SE
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
C (M = 6)REM (M = 6)
RAll (M = 6)
Skill Score Comparison Skill Score Comparison for C, Rfor C, REMEM, and R, and Rallall 18 years, deep layer(s), 6 models
TransferabilityTransferability First step (½ Illinois to other ½ Illinois)Individual models and simple compositing is unaffected.More complex compositing shows a small loss in skill.Real test – Illinois to China…
Remote Sensing Remote Sensing ApplicationApplication
To develop and test large-scale validation and assimilation techniques over land, by coupling the land surface models with the “validated” state-of-the art L-band microwave emission model (L-MEB*) to simulate prognostic brightness temperature observable remotely from satellite microwave radiometers.
Land Surface Model
- 0~5cm soil moisture- 0~5cm soil T.- 50/100cm soil T.- Vegetation canopy T.- Canopy interception- LAI, Air T.
- Landmask- Soil texture class (sand% and clay%) - Elevation- Vegetation type
Microwave Emission Model
Brightness Temperature
[*acknowledgement: Jean-Pierre Wigneron (INRA), Thierry Pellarin (CNRM), and Jean-Christophe Calvet (CNRM)]
L-MEB Model Validation L-MEB Model Validation DataData
Experiment Location Time &Date
Vegetation Soil Instrument& platform
Configuration Image Size(pixel size)
Monsoon’90
Walnut GulchWatershed, AZ(31°44.617’N,110°3.083’W)
16 ~ 18UTC,
7/31/1990~
8/9/1990
Mixedgrass-shrub
rangeland
SandyLoams
PBMR,NASAC-130
Aircraft
Monoangular(=8º) H pol-arization only
8 x 20 km2
(180 m)
Washita’92
Little WashitaWatershed, OK(34°57.624’N,97°58.7337’W)
16 ~ 19UTC,
6/10/1992~
6/18/1992
Rangeland,Pasture
Variable
ESTAR,NASAC-130
aircraft
Monoangular(=0º) H pol-arization only
18 x 46 km2
(200 m)
SGP’97
Little WashitaWatershed, OK(34°57.624’N,97°58.7337’W)
15 ~ 18UTC,
6/18/1997~
7/17/1997
Rangeland,Pasture
VariableESTAR,
NASAP3B aircraft
Monoangular(=0º) H pol-arization only
irregular(800 m)
Portos’91INRA, Avignon,France, (43°55N,
4°53E)
7/24/1991~
9/30/1991Soybean
Silty ClayLoam
PORTOS,Crane broom
MultiangularH & V pola-
Rization
N/A(point-based)
Portos’93INRA, Avignon,France, (43°55N,
4°53E)
4/19/1993~
7/8/1993Wheat
Silty ClayLoam
PORTOS,Crane broom
MultiangularH & V pola-
Rization
N/A(point-based)
Validation of L-MEB Model Validation of L-MEB Model
Observed brightness temperature200 220 240 260 280
ME
B-S
imul
ated
bri
ghtn
ess
tem
pera
ture
200
220
240
260
280 Washita'92Monsoon'90Portos'93Portos'91
Observed brightness temperature200 220 240 260 280
ME
B s
imul
ated
bri
ghtn
ess
tem
pera
ture
200
220
240
260
280 SGP97 ( )
Use observed soil moisture, soil temperature, etc. as inputs to L-MEB
Statistics of L-MEB Statistics of L-MEB ValidationValidation
Experiment
Regression Statistics Error Statistics
Intercept Slope R Bias (K) RMSE (K)Sample Size (site x day)
Monsoon’90
61.03 0.76 0.94 -1.90 6.66 48 (8 x 6)
Washita’92
30.10 0.87 0.89 -0.42 7.10 80 (10 x 8)
SGP97 8.78 0.97 0.93 0.34 5.42 212 (15 x 15)*
Porots’91 52.39 0.78 0.89 -2.41 7.57 28 (28 x 1)
Portos’93 -57.67 1.23 0.98 -0.60 5.94 26 (26 x 1)
All 17.36 0.93 0.94 -0.34 6.15 394
Coupled LSS-MEB ValidationCoupled LSS-MEB Validation(Washita’92, OK)(Washita’92, OK)
Date
162 163 164 165 166 167 168 169 170
LS
S-M
EB
Bri
ghtn
ess
Tem
pera
ture
210
220
230
240
250
260
270
Grid 1
Use LSS soil moisture, soil temperature, etc. as inputs to L-MEB
Date
162 163 164 165 166 167 168 169 170
LS
S-M
EB
Bri
ghtn
ess
Tem
pera
ture
210
220
230
240
250
260
270SSiBNSIPPISBAVISAObservation
Grid 2
Coupled LSS-MEB ValidationCoupled LSS-MEB Validation(Soil Characteristics (Soil Characteristics
Comparison)Comparison)
DOY
162 163 164 165 166 167 168 169 170
Sur
face
Soi
l Moi
stur
e (c
m3 / c
m3 )
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
SSiBNSIPPISBAVISAObservation
DOY
162 163 164 165 166 167 168 169 170
Sur
face
Soi
l Tem
pera
ture
(K
)
296
298
300
302
304
306
Grid 1
Grid 1
DOY
162 163 164 165 166 167 168 169 170
Sur
face
Soi
l Moi
stur
e (c
m3 / c
m3 )
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
DOY
162 163 164 165 166 167 168 169 170
Sur
face
Soi
l Tem
pera
ture
(K
)
296
298
300
302
304
306
Grid 2
Grid 2
Coupled LSS-MEB ValidationCoupled LSS-MEB Validation(Precipitation Comparison)(Precipitation Comparison)
DOY135 140 145 150 155 160 165 170 175 180 185
Dai
ly R
ainf
all (
mm
)
0
10
20
30
40
50
60
70
80
RaingageGSWP2Washita'92 flight days
DOY135 140 145 150 155 160 165 170 175 180 185
Dai
ly R
ainf
all (
mm
)
0
10
20
30
40
50
60
70
80
Grid 1 Grid 2
Microwave AnalogsMicrowave Analogs
Example global 1° map of the synthetic L-band H-polarized brightness temperature corresponding to the incidence angle and equator crossing time of HYDROS Satellite for June 01, 1992.
Presenting GSWP-2Presenting GSWP-2
Session at AMS Annual Mtg., Hydrology Conf. San Diego, CA, USA – 10-13 January 2005
GEWEX 5th Int’l. Science Conf. Costa Mesa, CA, USA – 20-24 June 2005 Abstract submission deadline 16 January 2005
EGU (Apr 2005 – Vienna)Spring AGU (May 2005 – New Orleans)AMS (Jan 2006 – Atlanta)
Journal Special Journal Special Issue/Section?Issue/Section?
Do we want to do a special issue?J. Geophys. Res. (efficient – no
delays)J. Hydrometeor. (better targeted
audience)Glob. Planet. Change (easier?)…
Proposal for Baseline, etc.Proposal for Baseline, etc. COLA = Continue with multi-model analysis based on
B0 simulations Climatology (12 months), Monthly (120), Daily Call it “GSWP-2 Version 1.0”
Japan = Produce a new baseline forcing (including spin up) Improved based on problems found, solutions suggested Call it “B1”, release, and encourage modelers to submit in
2005
Sensitivity studies continue based on B0 Sensitivity studies not as sensitive to systematic errors as
analysis
COLA = Produce a multi-model analysis based on B1 simulations Call it “Version 2.0”