N: 90 S: -60 E: 180 W: -180
Description
NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.
This data set, GLDAS-2.0 1.0 degree 3-hourly, contains a series of land surface variables simulated from the Catchment Land Surface Model 3.6 in Land Information System (LIS) Version 7. The data set currently cover from January 1948 to December 2014, but will be extended as the forcing data becomes available. The GLDAS-2.0 data are archived and distributed in netCDF format.
The GLDAS-2.0 model simulations were initialized on January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulations were forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). Each simulation uses the common GLDAS data sets for land water mask (MOD44W: Carroll et al., 2009) and elevation (GTOPO30) along with the model default land cover and soils datasets. Catchment model uses the Mosaic land cover classification and soils, topographic, and other model-specific parameters were derived in a consistent manner as in the NASA/GMAO’s GEOS-5 climate modeling system. The MODIS based land surface parameters are used in the current GLDAS-2.0 and GLDAS-2.1 products.
In October 2020, all 3-hourly and monthly GLDAS-2 data were post-processed with the MOD44W MODIS land mask. Previously, some grid boxes over inland water were considered as over land and, thus, had non-missing values. The post-processing corrected this issue and masked out all model output data over inland water; the post-processing did not affect the meteorological forcing variables. More information can be found in the GLDAS-2 README. The MOD44W MODIS land mask is available on the GLDAS Project site.
If you had downloaded the GLDAS data prior to November 2020, please download the data again to receive the post-processed data.
Version Description
Product Summary
Citation
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Documents
READ-ME
GENERAL DOCUMENTATION
IMPORTANT NOTICE
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia | Ahmad, Jawairia A., Forman, Bart A., Getirana, Augusto, Kumar, Sujay V. | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Ground Water | |
| Generation and evaluation of energy and water fluxes from the HOLAPS | Garcia-Garcia, Almudena, Peng, Jian | Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Albedo, Anisotropy, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Precipitation, Precipitation Amount | |
| Assessment of long-term multisource surface and subsurface soil moisture products and estimate methods on the Tibetan Plateau | Zhang, Pei, Zheng, Donghai, van der Velde, Rogier, Zeng, Jiangyuan, Wang, Xin, Wang, Zuoliang, Zeng, Yijian, Wen, Jun, Li, Xin, Su, Zhongbo | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Decomposition and reduction of WRF-modeled wintertime cold biases over the Tibetan Plateau | Li, Yantong, Gao, Yanhong, Chen, Guoxing, Wang, Guoyin, Zhang, Meng | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff | |
| Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau? | Yan, Shirui, Chen, Yang, Hou, Yaliang, Liu, Kexin, Li, Xuejing, Xing, Yuxuan, Wu, Dongyou, Cui, Jiecan, Zhou, Yue, Pu, Wei, Wang, Xin | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Air Temperature, 24 Hour Maximum Temperature, 24 Hour Minimum Temperature, Albedo, Snow Depth, Snow Water Equivalent, Soil Heat Budget, Soil Heat Budget, Soil Temperature, Soil Infiltration, Soil Infiltration, Surface Soil Moisture, Root Zone Soil Moisture, Soil Moisture/Water Content, Evaporation, Surface Water, Runoff Rate, Average Flow, Average Flow, Precipitation, Snow/Ice, Snow Depth, Snow Melt, Snow/Ice Temperature, Leaf Area Index (LAI), Leaf Area Index (LAI) |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| ACond_tavg | ACond_tavg | m s-1 | float32 | -9999 | N/A | N/A | N/A |
| Albedo_inst | Albedo_inst | % | float32 | -9999 | N/A | N/A | N/A |
| AvgSurfT_inst | AvgSurfT_inst | K | float32 | -9999 | N/A | N/A | N/A |
| CanopInt_inst | CanopInt_inst | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| ECanop_tavg | ECanop_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| ESoil_tavg | ESoil_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Evap_tavg | Evap_tavg | kg m-2 s-1 | float32 | -9999 | N/A | N/A | N/A |
| lat | lat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| lon | lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| LWdown_f_tavg | LWdown_f_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Lwnet_tavg | Lwnet_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Psurf_f_inst | Psurf_f_inst | Pa | float32 | -9999 | N/A | N/A | N/A |
| Qair_f_inst | Qair_f_inst | kg kg-1 | float32 | -9999 | N/A | N/A | N/A |
| Qg_tavg | Qg_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Qh_tavg | Qh_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Qle_tavg | Qle_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Qsb_acc | Qsb_acc | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| Qsm_acc | Qsm_acc | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| Qs_acc | Qs_acc | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| Rainf_f_tavg | Rainf_f_tavg | kg m-2 s-1 | float32 | -9999 | N/A | N/A | N/A |
| Rainf_tavg | Rainf_tavg | kg m-2 s-1 | float32 | -9999 | N/A | N/A | N/A |
| SnowDepth_inst | SnowDepth_inst | m | float32 | -9999 | N/A | N/A | N/A |
| Snowf_tavg | Snowf_tavg | kg m-2 s-1 | float32 | -9999 | N/A | N/A | N/A |
| SnowT_tavg | SnowT_tavg | K | float32 | -9999 | N/A | N/A | N/A |
| SoilMoist_P_inst | SoilMoist_P_inst | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| SoilMoist_RZ_inst | SoilMoist_RZ_inst | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| SoilMoist_S_inst | SoilMoist_S_inst | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP0_10cm_inst | SoilTMP0_10cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP10_29cm_inst | SoilTMP10_29cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP29_68cm_inst | SoilTMP29_68cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP68_144cm_inst | SoilTMP68_144cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP144_295cm_inst | SoilTMP144_295cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SoilTMP295_1295cm_inst | SoilTMP295_1295cm_inst | K | float32 | -9999 | N/A | N/A | N/A |
| SWdown_f_tavg | SWdown_f_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| SWE_inst | SWE_inst | kg m-2 | float32 | -9999 | N/A | N/A | N/A |
| Swnet_tavg | Swnet_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| Tair_f_inst | Tair_f_inst | K | float32 | -9999 | N/A | N/A | N/A |
| time | time | minutes since 1948-01-01 03:00:00 | float64 | N/A | N/A | N/A | N/A |
| time_bnds | time_bnds | N/A | float64 | N/A | N/A | N/A | N/A |
| TVeg_tavg | TVeg_tavg | W m-2 | float32 | -9999 | N/A | N/A | N/A |
| TWS_inst | TWS_inst | mm | float32 | -9999 | N/A | N/A | N/A |
| Wind_f_inst | Wind_f_inst | m s-1 | float32 | -9999 | N/A | N/A | N/A |