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Title: Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office

item Reichle, R
item Bosilovich, M
item Crow, Wade
item Koster, R
item Kumar, S
item Mahanama, S
item Zaitchik, B

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 4/1/2008
Publication Date: 3/9/2009
Citation: Reichle, R.H., Bosilovich, M.G., Crow, W.T., Koster, R.D., Kumar, S.V., Mahanama, S.P., Zaitchik, B.F. 2009. Recent advances in land data assimilation at the NASA Global Modeling and Assimilation Office. In: Pard, S.K., editor. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. London, United Kingdom: Springer-Verland. p. 407-428.

Interpretive Summary: Data assimilation systems are powerful tools for optimally combining information gleaned from a variety of sources – including remote sensing data obtained from spaceborne sensors. This manuscript provides a review of recent land data assimilation advances made through a collaboration between USDA ARS HRSL scientists and researchers at the Global Modeling and Data Assimilation Office at NASA Goddard Space Flight center. The review article describes a number of new data assimilation advances and contributions to state of the art coupled land-atmosphere modleing. The eventual application of these techniques may enhance our ability to make long-term weather and climate forecasts within agricultural regions.

Technical 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 primarily 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 and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation 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 surface 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 water 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 designed 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.