An EKF assimilation of AMSR‐E soil moisture into the ISBA land surface scheme

Abstract

An Extended Kalman Filter (EKF) for the assimilation of remotely sensed near‐surface soil moisture into the Interactions between Surface, Biosphere, and Atmosphere (ISBA) model is described. ISBA is the land surface scheme in Météo‐France's Aire Limitée Adaptation Dynamique développement InterNational (ALADIN) Numerical Weather Prediction (NWP) model, and this work is directed toward providing initial conditions for NWP. The EKF is used to assimilate near‐surface soil moisture observations retrieved from C‐band Advanced Microwave Scanning Radiometer (AMSR‐E) brightness temperatures into ISBA. The EKF can translate near‐surface soil moisture observations into useful increments to the root‐zone soil moisture. If the observation and model soil moisture errors are equal, the Kalman gain for the root‐zone soil moisture is typically 20–30%, resulting in a mean net monthly increment for July 2006 of 0.025 m3 m−3 over ALADIN's European domain. To test the benefit of evolving the background error, the EKF is compared to a Simplified EKF (SEKF), in which the background errors at the time of the analysis are constant. While the Kalman gains for the EKF and SEKF are derived from different model processes, they produce similar soil moisture analyses. Despite this similarity, the EKF is recommended for future work where the extra computational expense can be afforded. The method used to rescale the near‐surface soil moisture data to the model climatology has a greater influence on the analysis than the error covariance evolution approach, highlighting the importance of developing appropriate methods for rescaling remotely sensed near‐surface soil moisture data.