Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses

Authors: Clara Draper and Rolf Reichle

NASA generates global “retrospective analysis” (or “reanalysis”) datasets that describe atmospheric conditions across several decades at hourly time steps and at a spatial resolution of a few tens of kilometers. These datasets rely on the merger of a vast number of atmospheric observations from ground, aircraft, and satellite sensors into numerical models of the atmosphere, models that are also used for numerical weather forecasts and climate research. The most recent NASA reanalysis is the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2; Gelaro et al. 2017).

To date, however, land surface observations were not used in NASA reanalysis datasets, which resulted in reanalysis estimates of soil moisture and soil temperature conditions that are not directly constrained by observations. But accurate land surface conditions are important because under certain conditions they impact the subsequent evolution of the atmosphere. For example, the soil water that is available to plants is often limited, which results in reduced evaporative cooling of the near-surface atmosphere.

In recent years, improved estimates of soil moisture conditions across the globe were obtained by merging satellite observations of surface soil moisture into numerical models of only the land surface water and energy dynamics. In such land-only systems, the benefit of soil moisture assimilation cannot, by design, improve estimates of near-surface atmospheric conditions. In this paper, we present a coupled land-atmosphere reanalysis system that assimilates satellite soil moisture observations along with the usual atmospheric observations into a coupled land-atmosphere modeling system, thereby enabling the feedback of improved land surface conditions on the near-surface atmosphere. The satellite soil moisture observations are from the Advanced Scatterometer (ASCAT) and the Soil Moisture Ocean Salinity (SMOS) mission.

Based on experiments spanning the summer of 2013, we find that the use of soil moisture observations in the reanalysis resulted in regional improvements in near-surface atmospheric conditions (Draper and Reichle 2019). Compared to a control experiment in which only the atmospheric observations were used in the system, utilizing the soil moisture observations together with the atmospheric observations decreased the root-mean square errors against independent observations in a large region spanning from Western Europe across southern Russia. These errors are reduced by up to 0.4 °K for daily maximum 2-m temperature and up to 0.5 g/kg for daily average specific humidity. Improvements were also found in the water and energy fluxes from the land to the atmosphere.

Based on these findings, it is recommended that satellite soil moisture observations, including from the NASA Soil Moisture Active Passive (SMAP) mission, be used in future NASA reanalysis systems.

The plots illustrate the impact of additionally assimilating satellite soil moisture observations in the MERRA-2 reanalysis system. They show the difference in root-mean-square error (RMSE) with and without soil moisture assimilation for daily maximum 2-m air temperature (T2mmax; left) and average 2-m specific humidity (q2m; right). The RMSE is computed versus independent station observations for Apr. 14 to Aug. 31, 2013.  Red colors indicate better performance (reduced RMSE) with soil moisture assimilation.

In a large region spanning from Western Europe across southern Russia, the soil moisture assimilation decreased the RMSE of T2mmax by up to 0.4 °K and the RMSE of q2m by up to 0.5 g/kg, compared to those of the default MERRA-2 system without soil moisture assimilation.
slide graphic


Gelaro, R., and Coauthors (2017), The Modern-Era Retrospective Analysis for Research and Applications, Version-2 (MERRA-2).J. Climate, 30, 5419-5454, doi:10.1175/JCLI-D-16-0758.1.

Draper, C. S., and R. H. Reichle (2019), Assimilation of satellite soil moisture for improved atmospheric reanalyses. Mon. Wea. Rev., in press, doi:10.1175/MWR-D-18-0393.

« GMAO Science Snapshots