Representing Soil Moisture Spatial Distribution by Assimilating Satellite-based Soil Moisture Data into a Land Surface Model

The spatial distribution of soil moisture (SM) is one of the key drivers of land-atmosphere interaction (LAI) and is often poorly represented in atmospheric models. The heterogeneity of SM can modulate the magnitude and scale of the local circulations in the planetary boundary layer through surface fluxes, and subsequently influence the formation of cloud and precipitation. In state-of-the-art atmospheric models, SM conditions are usually updated through land surface models (LSMs), which can also be run as a stand-alone model with prescribed atmospheric forcing. Regardless, the quality of atmospheric forcings and input land surface properties directly impact how well the SM is represented in the LSM simulations. Satellite-based SM data are advantageous in spatial coverage as opposed to much sparser in-situ observations, but remotely sensed SM datasets cannot fully describe the physical state of the land/soil system, in particular temperature and SM at deeper soil levels. Therefore, in this study, we aim to generate a high-resolution SM analysis by assimilating NASA’s satellite-based Soil Moisture Active and Passive (SMAP) SM data into the Noah multiparameterization (Noah-MP) LSM.

The Noah-MP LSM is configured with a domain encompassing the eastern part of the continental U.S. at 1-km grid spacing. Over the period from March 2015 to December 2016, the model-predicted soil moisture is constrained hourly by the SMAP soil moisture retrievals (36-km and 9-km resolutions). We assess the impact of SMAP data assimilation (DA) by using the in-situ observations collected by the Oklahoma Mesonet and Atmospheric Radiation Measurement (ARM)’s soil temperature and moisture profiles (STAMP) system. Our results show the approach significantly improves the top-layer SM representation by reducing the overall dry bias and producing more realistic spatial variability over Oklahoma. Improvement is also found in soil temperature and surface fluxes. Sensitivity experiments emphasize the benefits of using longer assimilation window, proper interpolation of meteorological forcing, and higher-resolution SMAP product. The analysis product has potential to better initialize large eddy simulation (LES) and mesoscale models for studies of land-atmosphere-cloud (LAC) coupling.