月別アーカイブ: 2018年10月

The 50th Data Assimilation Seminar 25th October

We held the 50th Data Assimilation Seminar at RIKEN Center for Computational Science (R-CCS) on October 25. The seminar talk was given by Dr. Jing-Shan Hong from the Central Weather Bureau (CWB) in Taiwan.

In this seminar Dr. Hong's talked about his research applying a multi-scale blending scheme on continuous cycling radar data assimilation. He showed how using a multi-scale blending scheme he can take advantage of analysis from both a global and regional model to improve the prediction of accumulated rainfall and typhoon track forecasts. He also showed how the blending scheme can be used to remove accumulated bias from continuous cyclic data assimilation.


We would like to thank Dr. Hong very much for visiting us at RIKEN and we look forward to seeing him again in the near future!

For details on upcoming DA seminars, please see the following DA seminar page:
http://www.data-assimilation.riken.jp/en/events/da_seminar/index.html

第50回データ同化セミナー (10月25日)のご案内

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。
http://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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理研データ同化合宿2018 (基礎編) 開催のお知らせ (12月3-7日)

一昨年・昨年に行われ好評を博した「理研データ同化合宿(基礎編)」を今年も開催します!

これまでの合宿では,Lorenz-96 モデルを題材に,多くの参加者がカルマンフィルタ―およびアンサンブルカルマンフィルタ―を自分の手で実装できるようになりました(さらに発展的な演習課題も用意しています).

参加申し込みの締め切り日は11月22日(木)となっています.
詳細はチームWEBサイト内に特設した以下のページをご覧ください.