developing consistent earth system data records for the global terrestrial water cycle
DESCRIPTION
Reprocessed satellite Precipitation, P (CSU). Reprocessed ISCCP Net radiation, Rn (UMD). Satellite P, Rn. Re- analysis. In-situ P, Ta. Princeton 50yr Pathfinder Forcing data set (P, Rn, Ta, q, w) (PU, UW). SMMR, TMI, AMSR-E surface moisture (GMU/CREW, PU). AVHRR, MODIS - PowerPoint PPT PresentationTRANSCRIPT
Developing Consistent Earth System Data Records forthe Global Terrestrial Water Cycle
Alok Sahoo1, Ming Pan2, Huilin Gao3, Eric Wood2, Paul Houser1, Dennis Lettenmaier3, Rachel Pinker4 and Christian Kummerow5
GMU/CREW; Princeton University2; University of Washington3, University of Maryland4; Colorado State University5
IN51B-1150, AGU 2008 Fall Meeting
PrecipitationSurface soil moisture
Snow Extent/SWEEvapotranspiration
Irrigation water use
River dischargeReservoir storage
Reprocessed satellitePrecipitation, P
(CSU)
Princeton 50yr PathfinderForcing data set(P, Rn, Ta, q, w)
(PU, UW)
Updated andReprocessed data set
VIC Land Surface Model(PU, UW)
Irrigation Model
River routing andReservoir model
Reprocessed ISCCPNet radiation, Rn
(UMD)
SMMR, TMI, AMSR-Esurface moisture(GMU/CREW, PU)
AVHRR, MODISSnow Extent
(UW)
ISCCP-basedLatent heat/ET
(PU)
GRDC river basinRunoff data set
SatelliteP, Rn
In-situP, Ta
Re-analysis
Merging/Data Assimilation,for water budget consistency
(GMU/CREW, PU, UW)
Project Goals
The overall goal is to develop consistent, long-term Earth System Data Records (ESDRs) for the major components (storages and fluxes) of the terrestrial water cycle at a spatial resolution of 0.5 degrees (latitude-longitude) and for the period 1950 to near-present.
Data Records to Produce
The resulting ESDRs are intended to provide a consistent basis for estimating the mean state and variability of the land surface water cycle at the spatial scale of the major global river basins. The ESDRs to produce include:
surface meteorology (precipitation, air temperature, humidity and wind), surface downward radiation (solar and longwave) and derived and/or assimilated fluxes and storages such as surface soil moisture storage, total
basin water storage, snow water equivalent, storage in large lakes, reservoirs, and wetlands, evapotranspiration, and surface runoff.
Methodology
The ESDR’s will be created by updating and extending previously funded Pathfinder data set activities to the investigators. The ESDRs will utilize algorithms and methods that are well documented in the peer reviewed literature. The ESDRs will merge satellite-derived products with predictions of the same variables by LSMs driven by merged satellite and in situ forcing data sets (most notably precipitation), with the constraint that the merged products will close the surface water budget.
Data Sources
The primary land surface forcing variable, precipitation, will be formed by merging model (reanalysis) and in situ data with satellite-based precipitation products such as TRMM, GPCP, and CMORPH. Derived products will include surface soil moisture (from TRMM, AMSR-E, SMMR, SSM/I passive microwave and ERS microwave scatterometers), snow extent (from MODIS and AVHRR), evapotranspiration (model- derived using ISCCP radiation forcings from geostationary and LEO satellites), and runoff (from LSM predictions and in-situ measurements).
Data records will be produced at a number of different levels – each of them will have different sources and levels of processing, for example:
• Downscaled Pathfinder Water Cycle Forcing Data;• Merged Multi-sensor Precipitation;• VIC Model Derived Water Budget Variables;• Reservoir Storage and Irrigation Water Use;• Radiative Fluxes;• Satellite Soil Moisture, Evapotranspiration and Snow;• Merged model derived and satellite products.
Left Figure:
Simulated (using the VIC model) mean seasonal cycle and variability, and observations for four large global rivers.
Project Team
Princeton George Mason Univ. University of University of Colorado University Ctr. for Res. in Env. & Water Washington Maryland State Univ.
(PU) (GMU/CREW) (UW) (UMD) (CSU)
Figure above: Monthly mean water budgets for selected basins, 1950-2000. P = precipitation, E = evapotranspiration, Qs = surface runoff, Qb = baseflow, dS/dt = change in storage (soil moisture + snow).
Schematic of the Elements of the Water Budget ESDRs
Bias Correct and Downscale• corrected rainday statistics, undercatch• removal of biases in monthly values• removal of spurious trends in SW • adjustment for elevation effects• downscale in time and space
Precipitation
Temperature
SW Radiation
Precipitation
Temperature
SW Radiation
Diurnal Cycle
CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, Cld
CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, CldGPCP
1997-, Daily, 1.0deP
GPCP1997-, Daily, 1.0deP UW
1979-2000, Daily, 2.0degP
UW1979-2000, Daily, 2.0degP TRMM
2002-, 3hr, 0.25degP
TRMM2002-, 3hr, 0.25degP SRB
1985-2000, 3hr, 1.0degLw, Sw
SRB1985-2000, 3hr, 1.0degLw, Sw
Reanalysis
High temporal/low spatial resolution
Observations
Generally low temporal resolution
Global Forcing Dataset
High temporal/high spatial resolution, bias corrected, trend corrected, etc…
Pathfinder – the Pilot Project
Supported by NASA Grant NNX08AN40A“NRA/Research Opportunities in Space and Earth Sciences-2006”