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Challenges in forecasting super typhoon Yagi(2024)
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作者 Yumei Li johnny cl.chan +2 位作者 Xun Li Wen Feng Yu Zhang 《Tropical Cyclone Research and Review》 2025年第3期317-322,共6页
Yagi(2024)is another super typhoon that made landfall in 2024 in Wenchang,Hainan,following Rammasun(2014),with its destructive power setting a new historical record for typhoon disasters in Hainan.Based on multi-sourc... Yagi(2024)is another super typhoon that made landfall in 2024 in Wenchang,Hainan,following Rammasun(2014),with its destructive power setting a new historical record for typhoon disasters in Hainan.Based on multi-source observational data and numerical model forecast results,we present the distinctive characteristics of Supertyphoon Yagi and discuss the huge challenges in the operational forecasting of its track and intensity.Yagi underwent explosive intensification over the northeastern South China Sea,with wind speeds increasing by 28 m s−1 within 24 h—meeting the criteria for extreme rapid intensification(ERI).It maintained supertyphoon intensity for 67 h,with hurricane-force winds(>32.7 m s−1)persistently affecting Hainan's land area for approximately 10 h.These characteristics exceeded those of Supertyphoon Rammasun and pose great challenges in operational forecasting.Operational numerical weather prediction(NWP)models show marked disagreements in predicting the track of Yagi in the early stage.In the later period,while the ECMWF and Pangu model predictions suggest landfall over northeastern Hainan,those of the NCEP-GFS and three CMA models maintain a landfall over the Leizhou Peninsula.Given the fact that historically,the landfall probability in Guangdong is much higher than that in Hainan,such discrepancies considerably increased the difficulty in determining the landfall location.The forecasting of Yagi’s intensity also poses a substantial challenge because of the rare occurrence of supertyphoon landfall cases in Hainan and the underpredicted intensities from the NWP models. 展开更多
关键词 Super typhoon Yagi Extreme rapid intensification(ERI) Forecasting challenges Model forecast bias
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