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- Publisher
- Springer Journals
- Copyright
- 2018 Korean Meteorological Society and Springer Nature B.V.
- ISSN
- 1976-7633
- eISSN
- 1976-7951
- DOI
- 10.1007/s13143-018-0022-2
- Publisher site
- See Article on Publisher Site
Abstract
Abstract An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.
Journal
"Asia-Pacific Journal of Atmospheric Sciences" – Springer Journals
Published: Jun 1, 2018