NCCS Hosts New Land Data Assimilation System for the
National Climate Assessment


Scientists from NASA Goddard Space Flight Center’s Hydrological Sciences Laboratory (HSL) developed and ran one of the world’s first successful multisensor, multivariate land data assimilation systems (LDASs) encompassing soil moisture, snow depth, snow cover, and irrigation intensity environmental data records from multiple generations of NASA satellite instruments. Notably, the LDAS is among among the earliest systems to assimilate data from NASA’s Soil Moisture Active Passive (SMAP) satellite.

Chronological schematic of the satellite remote sensing retrievals and the satellite sources used in the National Climate Assessment Land Data Assimilation System (NCA-LDAS). The dashed lines for irrigation represent the fact that irrigation is applied throughout the NCA-LDAS time period, though the irrigation intensity map from MODIS was derived for the single year of 2001 (shown as a solid box). Figures by Kumar et al.

The new LDAS leverages NASA’s Land Information System (LIS), aiming to produce an integrated terrestrial water analysis for the U.S. National Climate Assessment (NCA)—an interagency effort to understand the impact of changing climate to support decision-making across the country. “The goal of NCA-LDAS is to provide an observation-informed reanalysis to quantify the multidecadal land surface changes over the continental U.S.,” said Sujay Kumar, HSL research physical scientist.

The Fourth NCA (NCA4) was published in November 2018. The NCA-LDAS is available as an enabling tool for NCA5, scheduled for publication in 2022.

Impact: Multivariate constraints from remote sensing measurements are necessary to improve the fidelity of hydrological and weather model simulations. NCA-LDAS represents a first-of-its-kind, land-surface-focused effort establishing a comprehensive record of changes in the terrestrial water cycle components over the continental U.S. for the past four decades.


Using NCA-LDAS, the scientists ran a one-of-a-kind, 37-year analysis of the period 1 January 1979 to 1 January 2016 on the NASA Center for Climate Simulation (NCCS) Discover supercomputer. Discover stored 2.9 terabytes of input data for the analysis. “The data- and computation-intensive model runs would not be possible without the high-performance computing resources at NCCS,” Kumar said.

The LIS simulations spanned the continental U.S. at 12.5-kilometer (km) resolution. Scientists ran the analysis twice, with each run employing 512 computing cores on Discover for approximately 5 days.

Scientists analyzed NCA-LDAS output of 550 gigabytes (GB) using software tools such as MATLAB, NCL, and GrADS installed on Discover. Approximately 200 GB of output representing the key terrestrial water budget variables is available from the Goddard Earth Sciences Data and Information Services Center (GES DISC).

Improvements in snow depth root mean square error (millimeters) compared to (left) Canadian Meteorological Centre and (right) Snow Data Assimilation System reference data. The bottom row shows improvement maps for the entire analysis period (1998–2015). The top row shows improvement for 2013–15, which represents the newest generation of satellite instruments. The warm and cool colors indicate improvements and degradations from data assimilation, respectively. Figures by Kumar et al.

Comparing results against a large suite of reference data products, NCA-LDAS shows systematic improvements in simulated soil moisture and snow depth but marginal effects on the accuracy of simulated streamflow and evapotranspiration. Across all evaluated variables, assimilation of data from more modern sensors (e.g., SMOS, SMAP, AMSR2, ASCAT) yields more skillful results than assimilation of data from older sensors (e.g., SMMR, SSM/I, AMSR-E). The evaluation also demonstrates the high skill of NCA-LDAS compared to other land surface modeling products.

“The paper showed the high accuracy of NCA-LDAS outputs, which then was used to estimate trends of hydrological extremes,” Kumar said. “For example, drought indicators based on NCA-LDAS suggest a trend of longer and more severe droughts over the southwestern U.S.”

Besides serving the NCA, other applications for NCA-LDAS include land hydrological modeling, monitoring of droughts and floods, food security and agricultural management, and water resources management.

More Information:

Kumar, S.V., M. Jasinski, D.M. Mocko, M. Rodell, J. Borak, B. Li, H. Kato Beaudoing, and C.D. Peters-Lidard, 2019: NCA-LDAS Land Analysis: Development and Performance of a Multisensor, Multivariate Land Data Assimilation System for the National Climate Assessment.
J. Hydrometeor., 20, 1571–1593, doi:10.1175/JHM-D-17-0125.1.

Companion Paper: Jasinski, M.F., J.S. Borak, S.V. Kumar, D.M. Mocko, C.D. Peters-Lidard, M. Rodell, H. Rui, H.K. Beaudoing, B.E. Vollmer, K.R. Arsenault, B. Li, J.D. Bolten, and N. Tangdamrongsub, 2019: NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment. J. Hydrometeor., 20, 1595–1617, doi:10.1175/JHM-D-17-0234.1.

Jarrett Cohen, NASA/Goddard Space Flight Center