日本地球惑星科学連合2018年大会

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[EE] 口頭発表

セッション記号 A (大気水圏科学) » A-AS 大気科学・気象学・大気環境

[A-AS03] 最新の大気科学:台風研究の新展開~過去・現在・未来

2018年5月23日(水) 13:45 〜 15:15 201A (幕張メッセ国際会議場 2F)

コンビーナ:中野 満寿男(海洋研究開発機構)、和田 章義(気象研究所台風研究部)、金田 幸恵(名古屋大学宇宙地球環境研究所、共同)、伊藤 耕介(琉球大学)、座長:金田 幸恵(名古屋大学)、和田 章義(気象研究所)

15:00 〜 15:15

[AAS03-17] Improvements in the forecast of TC Lan (2017) by assimilating dropsondes from T-PARCII and DOTSTAR

*伊藤 耕介1,2山口 宗彦2中澤 哲夫2山田 広幸1長浜 則夫3清水 健作3大東 忠保4篠田 太郎5坪木 和久5 (1.琉球大学、2.気象研究所、3.明星電気、4.京都大学、5.名古屋大学)

Tropical cyclone (TC) Lan (2017) exhibited the lowest minimum sea level pressure (MSLP) of the year and made a landfall on Japan with the gale wind radius more than 800 km. Before its landfall, airborne dropsonde observations were condcuted by the projects of Tropical cyclones-Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T-PARCII) and Dropwindsonde Observations for Typhoon Surveillance near the TAiwan Region (T-PARC). Because the improvements in the TC forecast quality is critical in terms of disaster prevention and mitigation, we conducted data assimilation and following forecast experiments to evaluate the impacts of these observations. To do so, the Japan Meteorological Agency (JMA) non-hydrostatic model (NHM)-based variational data assimilation (JNoVA) system was used to conduct data assimilation experiments with and without these aircraft observations. JNoVA is a four dimensional variational (4D-Var) data assimilation system with the outer loop horizontal grid spacing of 5 km and to cover the dropsonde observations obtained we ran 12 assimilation cycles (from 0300UTC 21 October through 1500UTC 22 October) in which the length of each data assimilation window is 3 hour. At the end of each cycle, 36-h forecast experiments were conducted by using JMA-NHM. As a result, it was shown that track forecasts were improved by up to 16% around for the forecast time of 21-36 hour and MSLP forecasts were improved by up to 30% for the forecast time of 15-36 hour owing to the assimilation of dropsonde observations. Although further checks are still needed, current preliminary results are very encouraging toward the improvements in the forecast quality leading to the disaster prevention and mitigation.