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”