Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM)
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- Miyoshi Takemasa
- Numerical Prediction Division, Japan Meteorological Agency
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- Aranami Kohei
- Numerical Prediction Division, Japan Meteorological Agency
書誌事項
- 公開日
- 2006
- DOI
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- 10.2151/sola.2006-033
- 公開者
- 公益社団法人 日本気象学会
説明
A four-dimensional local ensemble transform Kalman filter (4D-LETKF) is applied to the Japan Meteorological Agency (JMA)’s nonhydrostatic model (NHM) with explicit cloud microphysics to enable mesoscale ensemble prediction and data assimilation. Convective-scale data assimilation experiments in a perfect model scenario with 5-km grid spacing are performed, which indicates that the 4D-LETKF system works appropriately. Observations are taken every 10 minutes and every 2 × 2 × 2 grid points for horizontal winds, temperature, relative humidity, surface pressure, and precipitation rate. Although fixed lateral boundary conditions cause error reduction even without data assimilation, the advantages of 4D-LETKF are clear. When precipitation-rate observations are assimilated, some convective systems are better captured, although the impact is not always positive. Overall, 4D-LETKF shows encouraging results; it would be a tool adopted in future researches in convective-scale data assimilation and ensemble prediction.
収録刊行物
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- SOLA
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SOLA 2 128-131, 2006
公益社団法人 日本気象学会
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詳細情報 詳細情報について
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- CRID
- 1390001205221812224
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- NII論文ID
- 130004448413
- 80018110761
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- ISSN
- 13496476
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可
