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

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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. The method permits the flexible modeling of errors in land surface models and observations. We also developed 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.

Keywords

  • Data Assimilation
  • Land Surface Temperature
  • Land Surface Model
  • Snow Water Equivalent
  • Surface Soil Moisture

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Reichle, R.H. et al. (2009). Recent Advances in Land Data Assimilation at the NASA Global Modeling and Assimilation Office. In: Park, S.K., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71056-1_21

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