データ同化研究チーム Data Assimilation Research Team 2018年の新着情報

10月25日のデータ同化セミナーについてのご案内です。 今回のセミナーでは、
Dr. Jing-Shan Hong  (Central Weather Bureau (CWB), Taiwan) よりご講演頂きます。
※どなたでもご参加いただけますが、入館に手続きが必要なため、 事前に下記までご連絡をお願い致します。
da-seminar(please remove here)@riken.jp

以下URLに随時情報を更新しています。
https://www.data-assimilation.riken.jp/en/events/da_seminar/
以下、詳細です

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  Date：   October 25, 15:30-16:30
  Place：   Room - C107 at R-CCS
  Language:  English
  Speaker：  Dr. Jing-Shan Hong, Central Weather Bureau (CWB), Taipei, Taiwan
  
  Title: Re-Center algorithm on the Continuous Cycling Radar Assimilation:
  Multi-scale Blending Scheme
  
  Abstract：
  The torrential rains result from the short duration extreme rainfall system
  is of most critical for the disaster prevention. However, the limited
  predictability is the essence of the short duration extreme rainfall system
  due to the multi-scale interaction, fast evolution and strong nonlinearity.
  The assimilation of the radar observation with rapid, continuous update
  cycle is a key to level up the predictability of such a system.
  
  The continuous rapid update cycle is able to capture and keep
  convective-scale structure and avoid the model spin-up problems. However,
  many challenges were faced in the continuous update cycle data
  assimilation. For example, the limited-area model systems in general suffer
  a deficiency to effectively represent the large-scale features and are
  unavoidable to experience the obvious large-scale forecast errors. In
  particular, the domain size is restricted due to the compromise of
  increasing model resolution and limited computer resources. Furthermore,
  the model errors are ease to accumulate over the sparse observation area,
  especially as the data assimilation system configured as a continuous cycle
  mode.
  
  In this study, a multi-scale blending scheme using a low-pass spatial
  filter (Hsiao et al. 2015) was applied to a continuous cyclic radar data
  assimilation system. The blending scheme combines the global model analysis
  and the convective scale model forecast. It is expected the blended field
  takes the advantage from the global large scale environment and the
  convective scale perturbations. The scheme was applied to the hourly
  updated 3DVAR based radar data assimilation system. In addition, it also
  applied to re-center the ensemble mean of the cyclic LETKF radar data
  assimilation system. Case studies show that the blending scheme is able to
  correct the bias of the large scale monsoon flow from the global model and
  keep the convective rainfall structure from the convective scale radar data
  assimilation system. The results also show that the performance of 
  quantitative precipitation forecasts from both the 3DVAR and LETKF radar
  data assimilation system improved significantly as applying the blending
  scheme. The more detailed sensitivity on the blending scheme also discussed
  in this study.
  
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