A hybrid dynamic-statistical prediction model for tropical cyclone frequency over the western North Pacific and its evaluation
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    Abstract:

    Prediction of tropical cyclone (TC) genesis at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions,which is a challenge for TC forecast.This study presents an extended-range hybrid dynamical-statistical prediction model and a statistical prediction model for TC frequency over the western North Pacific.The models are based on tropical intraseasonal oscillation signals and the TC clustering method.The fuzzy c-mean clustering method categorizes TCs over the western North Pacific into seven track patterns.Predicting anomalous TC counts in each week involves adding the observed climatological mean of weekly TC counts to obtain total genesis counts for each cluster.The probability of TC track distributions each week is derived by involving the climatology of each track probability.This model could not only predict TC number for each cluster but also the TC track distribution pattern each week.The hybrid dynamical-statistical model relies on contemporaneous statistical relationships between low-frequency variabilities and the output of the ECMWF dynamical model from the S2S dataset.The predictand is the TC genesis number over the western North Pacific during each week.Evaluation of prediction results indicates that the forecast skill of the hybrid dynamic-statistical forecast surpasses that of the statistical forecast model.The precursor signals associated with sub-seasonal TC changes dissipate rapidly,making stable forecasts challenging.In contrast,the dynamic model simulates the low-frequency background field (predictors) effectively,enhancing the hybrid model's forecast skill.While,the current forecast skill of the hybrid dynamic-statistical forecast model extends to six weeks,further improvement is possible.Evaluation of prediction skills and error analysis of different TC clusters reveal that interannual and interdecadal variabilities of background fields on the modulations of intraseasonal oscillations on TC activity cannot be ignored.Statistical relationships between TC counts and low-frequency variabilities differ in distinct ENSO phases,suggesting potential improvement by developing forecast models based on different ENSO phases.Additionally,extratropical intraseasonal signals (e.g.,Rossby wave breaking and westerly jet intensity) significantly impact TC frequency and trajectory,which may provide more source of predictability for TC extended-range prediction.

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徐邦琪,魏澎,钱伊恬,游立军,2024.西北太平洋热带气旋频次的延伸期动力-统计预报方法和评估[J].大气科学学报,47(1):65-79. HSU Pangchi, WEI Peng, QIAN Yitian, YOU Lijun,2024. A hybrid dynamic-statistical prediction model for tropical cyclone frequency over the western North Pacific and its evaluation[J]. Trans Atmos Sci,47(1):65-79.

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History
  • Received:September 22,2023
  • Revised:November 20,2023
  • Adopted:
  • Online: March 19,2024
  • Published: January 28,2024

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