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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 25, issue 1 | Copyright
Nonlin. Processes Geophys., 25, 129-143, 2018
https://doi.org/10.5194/npg-25-129-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Mar 2018

Research article | 01 Mar 2018

Accelerating assimilation development for new observing systems using EFSO

Guo-Yuan Lien1,2, Daisuke Hotta3,1, Eugenia Kalnay1, Takemasa Miyoshi2,1,4, and Tse-Chun Chen1 Guo-Yuan Lien et al.
  • 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, 20742, USA
  • 2RIKEN Advanced Institute for Computational Science, Kobe, 650-0047, Japan
  • 3Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, 305-0052, Japan
  • 4Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, 236-0001, Japan

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

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The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under what conditions observations are beneficial or detrimental for assimilation. Based on EFSO, an offline assimilation method is proposed to accelerate the development of data selection strategies for new observing systems. The usefulness of this method is demonstrated with the assimilation of global satellite precipitation data.
The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under...
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