Accelerating assimilation development for new observing systems using EFSO
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 10, 2017
- Source ID
- 10.5194/npg-2017-45
Entities
People
- Daisuke Hotta
- Eugenia Kalnay
- Guo-Yuan Lien
- Takemasa Miyoshi
- Tse-chun Chen
Organizations
- Core Research for Evolutional Science and Technology
- Japan Aerospace Exploration Agency
- National Aeronautics and Space Administration
- National Oceanic and Atmospheric Administration
- Office of Naval Research