Recent Advances in Land Data Assimilation at the NASA Global Modeling and Assimilation Office

Abstract

Research in land surface data assimilation has grown rapidly over the last decade. We provide a brief overview of key research contributions by the NASA Global Modeling and Assimilation Office (GMAO). The GMAO contributions pri- marily include the continued development and application of the Ensemble Kalman filter (EnKF) for land data assimilation. In particular, we developed a method to generate perturbation fields that are correlated in space, time, and across variables. The method permits the flexible modeling of errors in land surface models and ob- servations. We also developed an adaptive filtering approach that estimates obser- vation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of sur- face soil moisture. Assimilation of such data into the ensemble-based GMAO land data assimilation system (GMAO-LDAS) provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). Satellite-based terrestrial wa- ter storage observations were also successfully assimilated into the GMAO-LDAS. Furthermore, synthetic experiments with the GMAO-LDAS support the design of a future satellite-based soil moisture observing system. Satellite-based land surface temperature (LST) observations were assimilated into a GMAO heritage variational assimilation system outfitted with a bias estimation module that was specifically de- signed for LST assimilation. The on-going integration of GMAO land assimilation modules into the Land Information System will enable the use of GMAO software with a variety of land models and make it accessible to the research community. 1