Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #373482

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Sources and forecast implications for soil moisture/ evapotranspiration over-coupling during L-band brightness temperature assimilation

item Crow, Wade
item ARROYO, C. - US Department Of Agriculture (USDA)
item MUNOZ, J. - European Centre For Medium-Range Weather Forecasts (ECMWF)
item HOLMES, T. - Goddard Space Flight Center
item HAIN, C. - Nasa Marshall Space Flight Center
item LEI, F. - Mississippi State University
item DONG, J. - US Department Of Agriculture (USDA)
item Alfieri, Joseph
item Anderson, Martha

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/25/2020
Publication Date: 10/1/2020
Citation: Crow, W.T., Arroyo, C., Munoz, J., Holmes, T., Hain, C., Lei, F., Dong, J., Alfieri, J.G., Anderson, M.C. 2020. Sources and forecast implications for soil moisture/ evapotranspiration over-coupling during L-band brightness temperature assimilation. Journal of Hydrometeorology. 21:2359-2374.

Interpretive Summary: In theory, the integration of satellite-based soil moisture estimates into numerical weather prediction (NWP) systems should improve our ability to forecast air temperature and precipitation within the central United States. However, recent attempts to demonstrate such improvements have yielded disappointing results. Using a combination of satellite-based soil moisture and evapotranspiration estimates, this paper demonstrates that the land surface model component of NWP systems tends to overestimate the impact on soil moisture variations on evapotranspiration. This overestimation, in turn, causes the model to misinterpret soil moisture information acquired from satellites and dictates that improvements in soil moisture are not translated into improved evapotranspiration and near-surface air temperature NWP forecasts. Results from this paper will be used by operational NWP centers to fix this existing flaw in land surface models and allow them to better leverage satellite-based soil moisture products to improve short-term weather forecasts within the central United States.

Technical Abstract: The assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 12- and 24-hr T2m forecasts within a portion of the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remotely sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves 24-hr ECMWF SM analyses in the central United States while simultaneously degrading the quality of 24-hr ET forecasts. Based on a recently derived map of true global SM/ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM over-couples SM and ET and, as a result, unable to effectively convert an improved description of SM conditions into enhanced ET and T2m forecasts. We demonstrate that this over-coupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within in the United States Corn Belt region.