Correction of Forcing-Related Spatial Artifacts in a Land Surface Model by Satellite Soil Moisture Data Assimilation

Abstract

Retrieved soil moisture estimates from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) radiometer are assimilated into the Noah land surface model (LSM) within the NASA Land Information System. The experimental testbed is based on a real-time LSM system produced by the NASA Short-Term Prediction Research and Transition Center. A nonlocalized cumulative distribution function-matching bias correction (BC) is applied to the SMAP retrievals, with separate correction curves calculated based on soil texture categories. We show that the assimilation of SMAP soil moisture retrievals with nonlocalized BC can mitigate two types of artifacts due to spatially varying errors in the forcing data from: 1) bad point (rain gauge) data and 2) strong gradients along the eastern U.S.–Canada border, resulting from blending different observing systems.