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Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

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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.

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Authors and Affiliations

  1. Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, Korea

    Seoleun Shin, Jeon-Ho Kang, Hyoung-Wook Chun, Sihye Lee, Kwangjae Sung, Kyoungmi Cho, Youngsoon Jo, Jung-Eun Kim, In-Hyuk Kwon & Sujeong Lim

  2. Korea Institute of Science and Technology Information (KISTI), Daejeon, Korea

    Ji-Sun Kang

  3. Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, 07071, Korea

    Seoleun Shin

Authors
  1. Seoleun Shin
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  2. Jeon-Ho Kang
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  3. Hyoung-Wook Chun
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  4. Sihye Lee
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  5. Kwangjae Sung
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  6. Kyoungmi Cho
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  7. Youngsoon Jo
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  9. In-Hyuk Kwon
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  10. Sujeong Lim
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  11. Ji-Sun Kang
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Correspondence to Seoleun Shin.

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Shin, S., Kang, JH., Chun, HW. et al. Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere. Asia-Pacific J Atmos Sci 54 (Suppl 1), 351–360 (2018). https://doi.org/10.1007/s13143-018-0022-2

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