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Soil Moisture Data Assimilation

  • Gabrielle Jacinthe Maria de Lannoy
  • Patricia de Rosnay
  • Rolf Helmut Reichle
Living reference work entry

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.

Keywords

Soil moisture retrieval Microwave brightness temperature Radar backscatter Terrestrial water storage Analysis Innovation Increment Kalman filter Observation operator Numerical weather prediction Initialization State update Calibration Radiative transfer model Land surface model Screen-level observations ASCAT AMSR2 SMOS SMAP GRACE 

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Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2015

Authors and Affiliations

  1. 1.NASA Goddard Space Flight Center, Code 610.1GreenbeltUSA
  2. 2.KU Leuven, Department of Earth and Environmental SciencesLeuvenBelgium
  3. 3.Data Assimilation SectionEuropean Center for Medium-Range Weather ForecastsReadingUK

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