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Handbook of Hydrometeorological Ensemble Forecasting pp 1–43Cite as

Soil Moisture Data Assimilation

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Abstract

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity, and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain, and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydrometeorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

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Authors and Affiliations

  1. NASA Goddard Space Flight Center, Code 610.1, Greenbelt Road, Greenbelt, MD, 20771, USA

    Gabrielle Jacinthe Maria de Lannoy & Rolf Helmut Reichle

  2. KU Leuven, Department of Earth and Environmental Sciences, Celestijnenlaan 200 E box 2411, Leuven, 3001, Belgium

    Gabrielle Jacinthe Maria de Lannoy

  3. Data Assimilation Section, European Center for Medium-Range Weather Forecasts, Shinfield Park, Reading, Berkshire, RG2 9AX, UK

    Patricia de Rosnay

Authors
  1. Gabrielle Jacinthe Maria de Lannoy
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  2. Patricia de Rosnay
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  3. Rolf Helmut Reichle
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Corresponding authors

Correspondence to Gabrielle Jacinthe Maria de Lannoy , Patricia de Rosnay or Rolf Helmut Reichle .

Editor information

Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, USA

    John C. Schaake

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de Lannoy, G.J.M., de Rosnay, P., Reichle, R.H. (2015). Soil Moisture Data Assimilation. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_32-1

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  • DOI: https://doi.org/10.1007/978-3-642-40457-3_32-1

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  • Online ISBN: 978-3-642-40457-3

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