CEOP Data Integration and theGlobal Land Data Assimilation
System (GLDAS)
Matt Rodell, P.R. Houser, C. Peters-Lidard,
M.Bosilovich, U. Jambor, J. Gottschalck, K. Arsenault
NASA's Goddard Space Flight Center, Hydrological Sciences Branch, Greenbelt, MD 20771

K. Mitchell, C.-J. Meng
NOAA's National Centers for Environmental Prediction, Camp Springs, MD 20746

GLDAS Overview
Goal:
Produce optimal output fields of land surface
states and fluxes
Problem:
Biases in modeled forcing ÆError accumulates in land stores ÆIncorrect surface water andenergy partitioningInaccurate initialization ofweather and climate models
Approach:
Force and constrain LSMs with data from newobserving systems
Specifications• LSMs: Mosaic, CLM, NOAH, Catchment, VIC …
• Resolutions: 0.25º, 0.5º, 1.0º, and 2.0ºx2.5º
spatial; 15 minute timestep• Mosaic-type subgrid tiles• Operational runs within 24-48 hours of realtime began 1 January 2001
• Multiple retrospective simulations

Summary of Data Sources for GLDASType of Data

SourceModeled Forcing

NASA Goddard Earth Observing System (GEOS)NOAA Global Data Assimilation System (GDAS)ECMWF forecasts and analyses
Berg et al. (2002) bias corrected ECMWF reanalysis
Berg et al. (2002) bias corrected NCEP/NCAR reanalysis
Derived at NASA/GSFC using U.S. Air Force Weather
Agency cloud and snow analyses

Observation-BasedSW and LW
Radiation Forcing
Observation-Based U.S. Naval Research Laboratory
Precipitation Forcing NASA/GSFC Mesoscale Atmospheric Processes Branch
NOAA Climate Prediction Center
Observation-Based Derived at NASA/GSFC using Terra-MODIS satellite
Snow Coverobservations
Observation-Based Boston University Department of Geography
Leaf Area Index
Observation-Based Television Infrared Observation Satellites (TIROS)
Surface Temperature Operational Vertical Sounder (TOVS)Vegetation Class
University of Maryland, AVHRR-derived
Boston University, MODIS-derived
Soils
USDA Agricultural Research Service
Elevation
GTOPO30 digital elevation model

Original
SpatialResolution
1.0°~ 0.7°~ 39 km
0.5°
0.5°
0.25°

12/2000 – present
1/1999 – present
10/2001 – present
1/1979 – 12/1993
1/1985 – 12/1993
3/2001 – present

0.25°
0.25°
2.5°
0.125°

4/2001 – present
3/2002 – present
1/1979 - present
11/2000 - present

16 km

7/1982 – 5/2001~ 15 km

1/1998 – 12/1998

1 km
1 km
5’30”

staticstatic
staticstaticTime Period

NearRealTimeΥΥΥΥΥΥ

U. Maryland AVHRR-derived Land Cover

1 = Water
2 = Evergreen Needleleaf Forest
3 = Evergreen Broadleaf Forest
4 = Deciduous Needleleaf Forest
5 = Deciduous Broadleaf Forest

6 = Mixed Cover
7 = Woodland
8 = Wooded Grassland
9 = Closed Shrubland
10 = Open Shrubland

11 = Grassland
12 = Cropland
13 = Bare Ground
14 = Urban and Build-Up

USDA Texture Class, 0 – 2 cm

1 = Sand
2 = Loamy Sand
3 = Sandy Loam
4 = Silty Loam

5 = Loam
6 = Sandy Clay Loam
7 = Silty Clay Loam
8 = Clay Loam

9 = Sandy Clay
10 = Silty Clay
11 = Clay

Derived from Reynolds, Jackson, and Rawls [1999]

Slope Angle [radians]

Derived From GTOPO30

Observation-based Leaf Area Index
July Leaf Area Index
Satellite observation of LAI on 8 km grid
(upper right)
Climatological average LAI on a 1 km grid,based on predominant vegetation type at 1km, latitude band, and month (lower left)
1 km LAI, where 8 km average equals
observation, 1 km pixels weighted byvegetation-specific climatology (lower right)

Comparison of Modeled Precipitation Forcing

Near-Real Time TRMM Microwave Image Field

Near-Real Time IR Temperature Field

U.S. Navy
Near-RealTime Precip
TRMM

SSM/I

Merged
TRMM + SSM/I

U.S. Navy Near-Real Time Blended Geostationary
Satellite IR, TRMM, and SSM/I Precipitation

CMAP Blended Precipitation Pentad Field

Comparison of Modeled SW Radiation Forcing

U.S. Air Force AGRMET Surface Insolation Scheme• Shapiro (1987) model• 13 vegetation types(Dorman and Sellers,1989)• Seasonal, snowdependant surface
albedo (NCEP)• Monthly greenness
fraction (Gutman andIgnatov, 1998)

U.S. Air Force RTNEPH Real-Time Cloud Analysis• Based on DMSP andgeostationary satellites,surface obs, and aircraft
reports.
• 2 polar stereographic
maps.
• 1024 x 1024 grids eachhemisphere.
• 24 km resolution, true at
60 degree latitude.
• Updated every 3 hours at
synoptic times.

U.S. Air Force Snow Analysis• Updated daily at 00Z

Globally Merged AGRMET Surface Insolation

Modeled vs. Observation-based Forcing
GEOS Model Forcing

AGRMET Radiation Option

Mean Downward SW (W/m2)

Mean Downward SW (W/m2)

Mean Output Surface Temp (K)28 February 2002

Mean Output Surface Temp (K)GLDAS Observation-based Snow Correction
A

Original MODIS visible snow
cover (%) A is modified using
MODIS confidence index (totalvisibility; %) B and a snow
impossible mask C in order toproduce an enhanced snow fieldD.

EDB

CF

This is used to update the modeledsnow on a daily basis. Output snow
depth (mm H2O) is shown for 30
November 2000, after running theMosaic LSM without E and with F thesnow correction for 30 days. Map G
shows the difference (mm H2O)
between the two results.

GGLDAS Observation-based Snow Correction
Uncorrected Mosaic snow (mm)Enhanced MODIS Snow Cover (%)

11 November 2000

Corrected Mosaic snow (mm)Mosaic Snow Difference (mm)

GLDAS Surface Temperature Assimilation

Assimilation withBias Correction
Observation

No Assimilation
Assimilation

Physical Space Analysis
System (PSAS) 3-D VAR(optimal interpolation) data
assimilation

GLDAS Simulations
Ongoing Real-Time:• Mosaic, GEOS forcing, 0.25°, 1/01-present• Mosaic, GDAS forcing, 0.25°, 1/01-present• Mosaic, Observation-based forcing (precip, ↓ SW & LW), 0.25°, 1/01-present• NOAH, GEOS forcing, 0.25°, 1/01-present• NOAH, Observation-based forcing, 0.25°, 1/01-present
Retrospective:• Mosaic, Bias-corrected ECMWF reanalysis forcing, 2° x 2.5° & 0.5°, 1979-1993• Mosaic, Bias-corrected NCEP/NCAR reanalysis forcing, 2° x 2.5° & 0.5°, 1979-1993
NWP and Climate Model Initialization Testing:• NASA Seasonal to Interannual Prediction Project (NSIPP)• NASA Data Assimilation Office FVDAS NWP model
Planned Near-Future Simulations:• Real-time CLM2
• Retrospective NOAH and CLM2
• ECMWF model forcing• Numerous experimental studies including assimilation

GLDAS Data Integration for CEOP
INTERCOMPARISON and OPTIMAL
MERGING of global land surface
forcing and state fields
Satellite observations ofmeteorological variables used asland surface model FORCINGASSIMILATION of satellite
observations of land surface
states into land surface models

Data
Inse rtion of Data
into the Mode lMoldeInteagrt ion

Ground-based observations andresults from field experiments usedto VALIDATE model output

Potential GLDAS Contributions to CEOP
Secondary archive ofselected global land surface
data to complement NASDA
CSDIC (primary CEOP archive)

ETSM

GLDAS products for land modelintercomparison, mesoscale model
initialization, and additional experiments
Model location time series(MOLTS) that are vegetationspecific
NEEDLELEAFTREES: 25%

GRASSLAND:50%
SHRUBS:10%
BARE SOIL: 15%