Global Land Data Assimilation System (GLDAS) ingests satellite and ground-based observational data products. Using advanced land surface modeling and data assimilation techniques, it generates optimal fields of land surface states and fluxes.
GLDAS-2.0 is one of two components of the GLDAS Version 2 (GLDAS-2) dataset, the second being GLDAS-2.1. GLDAS-2.0 is reprocessed with the updated Princeton Global Meteorological Forcing Dataset (Sheffield et al., 2006) and upgraded Land Information System Version 7 (LIS-7). It covers the period 1948-2010, and will be extended to more recent years as corresponding forcing data become available.
The model simulation was initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation used the common GLDAS datasets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012).
Documentation:
Provider's Note: the names with extension _tavg are variables averaged over the past 3-hours, the names with extension '_acc' are variables accumulated over the past 3-hours, the names with extension '_inst' are instantaneous variables, and the names with '_f' are forcing variables.
Dataset Availability
1948-01-01T00:00:00 - 2010-12-31T00:00:00
Dataset Provider
NASA GES DISC at NASA Goddard Space Flight Center
Collection Snippet
ee.ImageCollection("NASA/GLDAS/V20/NOAH/G025/T3H")
Resolution
27830 meters
Bands Table
Name | Description | Min* | Max* | Units |
---|---|---|---|---|
Albedo_inst | Albedo | 4.99 | 82.25 | % |
AvgSurfT_inst | Average surface skin temperature | 194.55 | 351.63 | K |
CanopInt_inst | Plant canopy surface water | 0 | 0.5 | kg/m^2 |
ECanop_tavg | Canopy water evaporation | 0 | 671.88 | W/m^2 |
ESoil_tavg | Direct evaporation from bare soil | 0 | 592.64 | W/m^2 |
Evap_tavg | Evapotranspiration | 0 | 0.0002 | kg/m^2/s |
LWdown_f_tavg | Downward long-wave radiation flux | 44.62 | 561.46 | W/m^2 |
Lwnet_tavg | Net long-wave radiation flux | -359.07 | 130.59 | W/m^2 |
PotEvap_tavg | Potential evaporation rate | -241.88 | 1513.78 | W/m^2 |
Psurf_f_inst | Pressure | 47824.13 | 109036.41 | Pa |
Qair_f_inst | Specific humidity | 0 | 0.06 | kg/kg |
Qg_tavg | Heat flux | -517.58 | 485.13 | W/m^2 |
Qh_tavg | Sensible heat net flux | -872.46 | 797.71 | W/m^2 |
Qle_tavg | Latent heat net flux | -243.71 | 716.69 | W/m^2 |
Qs_acc | Storm surface runoff | 0 | 131.39 | kg/m^2 |
Qsb_acc | Baseflow-groundwater runoff | 0 | 42.3 | kg/m^2 |
Qsm_acc | Snow melt | 0 | 27.58 | kg/m^2 |
Rainf_f_tavg | Total precipitation rate | 0 | 0.01 | kg/m^2/s |
Rainf_tavg | Rain precipitation rate | 0 | 0.01 | kg/m^2/s |
RootMoist_inst | Root zone soil moisture | 2 | 943.52 | kg/m^2 |
SWE_inst | Snow depth water equivalent | 0 | 117283.5 | kg/m^2 |
SWdown_f_tavg | Downward short-wave radiation flux | 0 | 1329.22 | W/m^2 |
SnowDepth_inst | Snow depth | 0 | 293.2 | m |
Snowf_tavg | Snow precipitation rate | 0 | 0.004 | kg/m^2/s |
SoilMoi0_10cm_inst | Soil moisture | 1.99 | 47.59 | kg/m^2 |
SoilMoi10_40cm_inst | Soil moisture | 5.99 | 142.8 | kg/m^2 |
SoilMoi40_100cm_inst | Soil moisture | 11.99 | 285.6 | kg/m^2 |
SoilMoi100_200cm_inst | Soil moisture | 20 | 476 | kg/m^2 |
SoilTMP0_10cm_inst | Soil temperature | 218.75 | 329.55 | K |
SoilTMP10_40cm_inst | Soil temperature | 227.3 | 317.08 | K |
SoilTMP40_100cm_inst | Soil temperature | 232.59 | 313.47 | K |
SoilTMP100_200cm_inst | Soil temperature | 234.5 | 311.86 | K |
Swnet_tavg | Net short wave radiation flux | 0 | 1128.86 | W/m^2 |
Tair_f_inst | Air temperature | 197.03 | 326.2 | K |
Tveg_tavg | Transpiration | 0 | 611.89 | W/m^2 |
Wind_f_inst | Wind speed | 0.06 | 30.31 | m/s |
* = Values are estimated数据引用:
影像属性
Name | Type | Description |
---|---|---|
end_hour | Double | End hour |
start_hour | Double | Start hour |
引用:
Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J.K. Entin, J.P. Walker, D. Lohmann, and D. Toll, The Global Land Data Assimilation System, Bull. Amer. Meteor. Soc., 85(3), 381-394, 2004.
代码:
var dataset = ee.ImageCollection('NASA/GLDAS/V20/NOAH/G025/T3H')
.filter(ee.Filter.date('2010-06-01', '2010-06-02'));
var averageSurfaceSkinTemperatureK = dataset.select('AvgSurfT_inst');
var averageSurfaceSkinTemperatureKVis = {
min: 250.0,
max: 300.0,
palette: ['1303ff', '42fff6', 'f3ff40', 'ff5d0f'],
};
Map.setCenter(71.72, 52.48, 3.0);
Map.addLayer(
averageSurfaceSkinTemperatureK, averageSurfaceSkinTemperatureKVis,
'Average Surface Skin Temperature [K]');