日本地球惑星科学連合2019年大会

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[E] 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-AS02] 台風研究の新展開~過去・現在・未来

2019年5月30日(木) 09:00 〜 10:30 104 (1F)

コンビーナ:金田 幸恵(名古屋大学宇宙地球環境研究所)、和田 章義(気象研究所台風・災害気象研究部)、伊藤 耕介(琉球大学)、宮本 佳明(慶應義塾大学 環境情報学部)、座長:金田 幸恵(名古屋大学)、和田 章義(気象研究所)

09:15 〜 09:30

[AAS02-02] Evaluation of operational Ensemble based Global Forecast System prediction for Tropical Cyclones over the North Indian Ocean

*Medha Deshpande1Radhika Kanase1R P M Krishna1Emmanuel Rongmie1P Mukhopadhyay1 (1.IITM, Pune, INDIA)

キーワード:Tropical Cyclones, North Indian Ocean, High Resolution ensemble Prediction System

The importance of accurate prediction of Tropical Cyclone (TC) genesis, rapid intensity changes, location and intensity during landfall is well known. Reliable prediction of these at 3-5 days lead time is crucial for disaster managing point of view. Bearing in mind the uncertainty in initial condition and model physics there was a need for high resolution ensemble based forecast system for region specific probabilistic prediction of weather over India. Considering this along with the existing Global Forecast System deterministic model (GFS T1534), the ensemble forecast system GEFS (T 1534) with 20 members is implemented and is in operation since June 2018 for the probabilistic prediction.

This paper evaluates the skill of operational high resolution (12.5 km) modelling system in predicting track and intensity of recent cases of TCs over North Indian Ocean at various lead time. Ensemble prediction of tracks of cyclones and its strike probability are evaluated. Ensemble mean track is better than the deterministic track particularly at longer lead time (figure 1). The vertical thermo-dynamic structure at the mature stage of the cyclone is well captured by the deterministic model. Evaluation of deterministic model (figure 2) specify average track error is ranging from < 100 km at Day 1 lead up to about 280 km at day 5 lead time and average error in intensity prediction is ranging from 10 kt – 20 kts. In general model indicates early intensification and slightly overestimates the peak intensity in majority of the cases. It is challenging to predict the variation in the intensity. Skill in the prediction of genesis is also evaluated by calculating Genesis Potential Index (GPI). Further developments like improving the physics and dynamics of the model along with use of proper land use land cover data are underway.