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FLDAS Specifications

 

FLDAS-Global* LIS7.4 Noah 3.6 monthly final and preliminary runs
Projection          Lat-Lon
Spatial Extent Global (180°W-180°E, 60°S-90°N) 
Spatial Resolution 0.1° x 0.1° 
Time Period 1/1/1982 - present
Temporal Resolution         15 minute time step, output saved daily  
Latency ~20th (final run) and ~5th (preliminary run) of the next month
Forcing** Datasets derived from satellite measurements and atmospheric analyses (Final runs: CHIRPS precipitation forcing 50°S-50°N and MERRA-2 reanalysis; Preliminary runs: CHIRPS preliminary precipitation forcing and GDAS)
Forcing Heights 2 m for air temperature and specific humidity, 10 m for wind
Elevation Definition SRTM
Vegetation Definition IGBP-MODIS 
Soils Definition STATSGO-FAO texture
Albedo NCEP monthly climatology
Max Snow Albedo NCEP
Greenness Fraction NCEP monthly climatology
Landmask MODIS
Land Surface Model Noah 3.6
Soil Layers 4 layers: 0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m  
Output Format  NetCDF

 

FLDAS-Central Asia*  LIS7.4 Noah-MP 4.0.1 and Noah 3.6 daily runs
Projection Lat-Lon
Spatial extent Central Asia Domain (30-100°E, 21-56°N)
Spatial Resolution 0.01° x 0.01° 
Time Period 10/1/2000 - present
Temporal Resolution 15 minute time step, output saved daily
Latency ~1 day
Forcing** Datasets derived from satellite measurements and atmospheric analyses (GDAS)
Forcing Heights 2 m for air temperature and specific humidity, 10 m for wind
Elevation Definition SRTM
Vegetation Definition IGBP-MODIS
Soils Definition STATSGO-FAO texture
Albedo NCEP monthly climatology
Max Snow Albedo NCEP
Greenness Fraction NCEP monthly climatology
Landmask MODIS
Land Surface Models NoahMP 4.0.1 and Noah 3.6
Soil Layers  4 layers: 0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m
Output Format NetCDF

 

FLDAS-Forecast  LIS7.1 Noah-MP 3.6 monthly runs LIS7.1 CLSM 2.5 monthly runs
Projection Lat-Lon Lat-Lon
Spatial Extent Continental Africa and the Middle East (20°W-60°E, 40°S-40°N)  Continental Africa and the Middle East (20°W-60°E, 40°S-40°N) 
Spatial Resolution 0.25° x 0.25°  0.25° x 0.25° 
Time Period

1/1/1981 – present; Climatology: 1991-2020

1/1/1981 – present; Climatology: 1991-2020
Temporal Resolution 15 minute time step, output saved monthly   15 minute time step, output saved monthly  
Forecast Lead Extent 6 months 6 months
Forcings**
Historic-forecast/Hindcast: CHIRPS-final precipitation and MERRA-2                           
 
Near-Real-time Forecast: CHIRPS-preliminary precipitation and MERRA-2                    
 
Dynamical Forecast:  NMME Precipitation from models: CCSM4, GEOSv2, CFSv2, GEM5-NEMO, CanCM4i, and GFDL-SPEAR with GEOSv2 non-precipitation met-forcings.  
 
Climatological Forecast or ensemble streamflow prediction: CHIRPS-final precipitation and MERRA-2  

Historic-forecast/Hindcast: CHIRPS-final precipitation and MERRA-2  

Near-Real-time Forecast: CHIRPS-preliminary precipitation and MERRA-2  

Dynamical Forecast:  NMME Precipitation from models: CCSM4, GEOSv2, CFSv2, GEM5-NEMO, CanCM4i, and GFDL-SPEAR with  GEOSv2 non-precipitation met-forcings.  

Climatological Forecast or ensemble streamflow prediction: CHIRPS-final precipitation and MERRA-2  

Forcing Heights 2 m for air temperature and specific humidity, 10 m for wind 2 m for air temperature and specific humidity, 10 m for wind
Elevation Definition SRTM SRTM
Vegetation Definition IGBP-MODIS  University of Maryland-AVHRR
Soils Definitions ISRIC texture (Hengl et al., 2017) Catchment-tile based Soil parameters derived from FAO’s estimated spatial soil water-holding capacities, derived by linking global pedon databases to the Soil Map of the World
Albedo NCEP monthly climatology Catchment-tile based Monthly Climatology derived from MODIS
Max Snow Albedo NCEP model estimated
Greenness Fraction NCEP monthly climatology Catchment-tile based, derived from GSWP-2
Landmask MODIS AVHRR
Land Surface Model Noah-MP 3.6 Catchment Land Surface Model Fortuna 2.5
Soil Layers

4 layers: 0-0.1, 0.1-0.4, 0.4-1.0, 1.0-2.0 m  

3 layers: 0-0.02, 0-1.0, 0-2.0 m

Routing Model HYMAP2 HYMAP2
Output Format NetCDF NetCDF

*Additional datasets (e.g., forcing and static inputs), updated and new land surface models, and data assimilation algorithms are also being used to improve these systems.

**coarser scale meteorological forcing data are spatially disaggregated in the LIS core software using bilinear interpolation. Daily precipitation inputs are temporally disaggregated with the LDT software, matching the precipitation distribution specified in the forcing. 

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