NCA-LDAS Noah-3.3 Land Surface Model L4 Daily 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_D) at GES DISC

The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document. This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0. NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product.

Tags: earth-science-atmospheric-radiation-atmosphere-heat-flux, earth-science-atmospheric-radiation-atmosphere-longwave-radiation, earth-science-atmospheric-radiation-atmosphere-shortwave-radiation, earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes-evaporation, earth-science-atmospheric-water-vapor-atmosphere-water-vapor-processes-evapotranspiration, earth-science-precipitation-atmosphere-liquid-precipitation-rain, earth-science-precipitation-atmosphere-solid-precipitation-snow, earth-science-snow-ice-cryosphere-snow-cover, earth-science-snow-ice-cryosphere-snow-depth, earth-science-snow-ice-cryosphere-snow-melt, earth-science-snow-ice-terrestrial-hydrosphere-snow-water-equivalent, earth-science-soils-land-surface-soil-moisture-water-content, earth-science-soils-land-surface-soil-temperature, earth-science-surface-radiative-properties-land-surface-albedo, earth-science-surface-thermal-properties-land-surface-land-surface-temperature, earth-science-surface-water-terrestrial-hydrosphere-surface-water-features-rivers-streams, earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements-floods, earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements-runoff, earth-science-vegetation-biosphere-canopy-characteristics, earth-science-vegetation-biosphere-leaf-characteristics, earth-science-vegetation-biosphere-vegetation-cover
Organization: National Aeronautics and Space Administration