トップページ イベント・広報 R-CCS Cafe R-CCS Cafe 第202回 第2部
R-CCS Cafe 第202回 第2部
講演題目
Impacts of dense surface observations on predicting a torrential rainfall event on September 9 and 10, 2015 in Ibaraki and Tochigi prefectures.
開催日 | 2020年11月13日(金) |
---|---|
開催時間 | 16:20 - 16:40(16:40 - 17:00 講演者を交えたフリーディスカッション(冒頭に1-2分の小休止を挟みます)、17:00 - 自由討論(参加自由)) |
開催都市 | オンライン |
場所 | BlueJeansによる遠隔セミナー |
使用言語 | 発表・スライド共に英語 |
登壇者 |
講演要旨
Abstract: On September 9 and 10, 2015, River Kinu in Tochigi Prefecture had a catastrophic flooding due to torrential rainfalls over 600 mm in the 2-day period. This historic heavy rain occurred due to an active line-type rainband maintained for an extended period in the middle of two typhoons. This study explores a potential use of additional dense surface observations to better predict this rainfall event. To investigate the impact of these dense surface data that were not included in the operational weather forecasting systems at that time, we perform data assimilation (DA) experiments using the Local Ensemble Transform Kalman Filter (LETKF) with the SCALE regional numerical weather prediction model. Two DA experiments were performed: the control experiment (CTRL) at 4-km resolution with only hourly conventional observations that were included in the operational weather forecasting systems at that time, and the other TEST experiment with additional hourly dense surface observation data. CTRL showed general agreement with the observed weather patterns, although the track of Typhoon Etau was shifted westward. As a result, the heavy rainfall area was shifted to the west compared to the JMA analyzed precipitation based on the radar and gauge observations. By contrast, TEST showed stronger rainfall intensity, better matching with the observed precipitation likely due to an improvement of the track of the Typhoon. To evaluate the impact of the individual surface station data on the rainfall forecast, we also implemented the Ensemble Forecast Sensitivity to Observation (EFSO) technique with SCALE-LETKF. The EFSO showed that about 53 % of surface data contributed to improve the rainfall forecast. The results suggest that the dense surface DA have a potential to improve the forecast accuracy for severe rainfall events.
注意事項
- 参加の際はPCマイクの音声・ビデオをオフにされるようお願いいたします。
- 当日の会場環境や通信状態により、やむなく配信を中止・中断する場合がございます。
- プログラムの内容、時間は予告なく変更される場合があります。
- ご使用の機器やネットワークの環境によっては、ご視聴いただけない場合がございます。
- インターネット中継に関する著作権は、主催者及び発表者に帰属します。なお、配信された映像及び音声、若しくはその内容を、理化学研究所の許可無くほかのウェブサイトや著作物等への転載,複製,改変等を行うことを禁じます。
(2020年11月5日)