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WANG Xiaochun, LI Chao, YING Ming, et al. 2026. Subseasonal to Seasonal Typhoon Forecast in the Western North Pacific in 2024 J. Climatic and Environmental Research (in Chinese), 31 (X): 1−13. DOI: 10.3878/j.issn.1006-9585.2026.26087
Citation: WANG Xiaochun, LI Chao, YING Ming, et al. 2026. Subseasonal to Seasonal Typhoon Forecast in the Western North Pacific in 2024 J. Climatic and Environmental Research (in Chinese), 31 (X): 1−13. DOI: 10.3878/j.issn.1006-9585.2026.26087

Subseasonal to Seasonal Typhoon Forecast in the Western North Pacific in 2024

  • To mitigate the losses caused by typhoons, accurate typhoon forecasts are indispensable. Currently, synoptical and seasonal time scale typhoon forecasts have matured in term of operational practice, but there is no universal standard in the subseasonal to seasonal (S2S) time scale typhoon forecasts. For the typhoons in the Western North Pacific in 2024, this study uses Daily Tropical Cyclone Probability (DTCP) as the forecasting variable, Debiased Brier Skill Score (DBSS), missing and false-alarm error as evaluation metrics to assess the S2S forecast. We assess the forecasting skill based on the best track dataset from the China Meteorological Administration and the real-time typhoon forecasting database of the World Meteorological Organization, specifically the products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP). The study also analyzes the characteristics of the Western North Pacific typhoons in 2024. The results indicate that the total number of typhoons between May and November of 2024 has increased by 2.3 compared to1991-2020 average. The averaged DTCP of 2024 is much higher than the climatological DTCP east of the Philippine islands. The forecast skill of ECMWF model and IAP model as measured by DBSS decreases rapidly within 10 days, and remains constant after that. Both models are more skillful than the seasonal reference climate forecast within 30 day lead time. The missing error of both model increases within 10 days and keeps unchanged after that. The false-alarm error keeps at a low level. However, the area with false alarm increases greatly by a factor of 6 to 8, associated with the dispersion of forecast DTCP area and the reducing of its value.
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