Development of an Operational Hybrid Data Assimilation System at KIAPS

Autor: Hyo-Jong Song, Youngsoon Jo, Sihye Lee, Tae-Hun Kim, In-Hyuk Kwon, Han-Byeol Jeong, Ji-Hyun Ha, Hyoung-Wook Chun, Hui-nae Kwon, Hyun-Jun Han, Seoleun Shin, Jeon-Ho Kang, Sujeong Lim
Rok vydání: 2018
Předmět:
Zdroj: Asia-Pacific Journal of Atmospheric Sciences. 54:319-335
ISSN: 1976-7951
1976-7633
DOI: 10.1007/s13143-018-0029-8
Popis: This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.
Databáze: OpenAIRE
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