An ensemble-based reanalysis approach to land data assimilation

Future pathfinder missions such as NASA's Hydrosphere State (Hydros) and ESA's Soil Moisture and Ocean Salinity (SMOS) will provide satellite-based global observations of surface (0 - 5 cm) soil moisture. In previous work an ensemble Kalman filter was used to estimate soil moisture, related states, and fluxes by merging noisy low-frequency microwave observations with the forecasts from a conventional yet uncertain land surface model. Kalman filter estimates are only conditioned on observations prior to estimation times. Here it is argued that soil moisture estimation is a reanalysis-type problem as observations beyond the estimation time are useful in the estimation. An ensemble smoother is used in which the state vector and measurement vector are distributed in time and updated as a batch. Its performance in a land data assimilation context is compared to that of the ensemble Kalman filter. Results demonstrate that smoothing yields an improved estimate compared to filtering, reflected in the decreased deviation from truth and the reduction in uncertainty associated with the estimate. Precipitation significantly impacts the performance of the smoother, acting as an information barrier between dry-down events. An adaptive hybrid filter/smoother is presented in which brightness temperature is used to break the study interval into a series of dry-down events. The smoother is used on dry-down events, and the filter is used when precipitation is evident between estimation times. An improved estimate is obtained as all observations in a given dry-down period are used to estimate soil moisture in that period, and backward propagation of information from subsequent precipitation events is avoided.