Impact of quality control of satellite soil moisture data on their assimilation into land surface model

A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models at the NOAA National Weather Service (NWS). A few studies have shown the benefits of assimilating satellite soil moisture data in land surface models (LSMs), which are the components of most NWP models. In this study, synthetic experiments are conducted to determine how soil moisture data quality control may impact the benefit of their assimilation into LSMs. It is found that using green vegetation fraction to quality control the SMOPS soil moisture product may significantly increase the benefit of assimilating it into Noah LSM in terms of increasing the agreement of Noah LSM surface and root zone soil moisture simulations with the corresponding in situ measurements. The quality control procedures and parameters are suggested for the assimilation of SMOPS data into NWS NWP models. Key Points Quality control rules of satellite soil moisture data product from NOAA‐NESDIS soil moisture product system (SMOPS) are established for their assimilation into Noah land surface model using green vegetation fraction criteriaApplying the quality control rules in the assimilation of SMOPS data products significantly improves the agreement of Noah LSM soil moisture simulations with in situ measurements