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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/npg-2017-45
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
10 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).
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 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, 20742, USA
2RIKEN Advanced Institute for Computational Science, Kobe, 650-0047, Japan
3Mete orological Research Institute, Japan Meteorological Agency, Tsukuba , 305-0052 , Japan
4Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokoh ama, 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, which are very time- and resource-consuming. This study proposes a new, efficient methodology to accelerate the development using the Ensemble Forecast Sensitivity to Observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection rules are designed based on the EFSO results, 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 rules that significantly improve the assimilation results.

Citation: Lien, G.-Y., Hotta, D., Kalnay, E., Miyoshi, T., and Chen, T.-C.: Accelerating assimilation development for new observing systems using EFSO, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2017-45, in review, 2017.
Guo-Yuan Lien et al.
Guo-Yuan Lien et al.
Guo-Yuan Lien et al.

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Short summary
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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