一種預測西北太平洋熱帶氣旋逐月演變的新統(tǒng)計模型

doi:10.12306/2022jms.0034

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一種預測西北太平洋熱帶氣旋逐月演變的新統(tǒng)計模型
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                        10.12306/2022jms.0034
                    
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中圖分類號:P466
基金項目:國家自然科學基金資助項目(41675077;41975090;41605072);國家重點研發(fā)計劃資助項目(2018YFC1505803)

A new statistical model for predicting month-to-month evolution of tropical cyclones in the western North Pacific

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本文提出一種月尺度西北太平洋熱帶氣旋生成頻數(shù)(Western North Pacific Tropical Cyclone Frequency, WNPTCF)預測的新方法。該方法利用全球次表層海溫(Subsurface Sea Temperature Anomaly, SubSSTA)和中國氣象局國家氣候中心發(fā)布的130項監(jiān)測指數(shù),構(gòu)建了既考慮熱力強迫因子、又考慮大氣動力因子,既考慮同期海洋強迫,又考慮前期海洋和大氣影響的集成預測模型。利用該預測模型對2011-2020年6-10月逐月WNPTCF進行獨立樣本檢驗預測,準確率達70%以上,說明該預測模型對WNPTCF的逐月演變預測的效果良好。該預測模型對ENSO信號較強年份的WNPTCF預測效果要好于ENSO信號不強的年份,原因在于在ENSO信號不強的年份,SubSSTA可預報性較低,非線性變率大,海洋對WNPTC的強迫作用弱。

Abstract:

In this paper,a new method for predicting the monthly Western North Pacific Tropical Cyclone Frequency (WNPTCF) was proposed.Based on the global Subsurface Sea Temperature Anomaly (SubSSTA) and 130 monitoring indexes released by the National Climate Center of China Meteorological Administration,this method constructs an integrated statistical prediction model that considers both thermal forcing factor and atmospheric dynamic factor,and both simultaneous oceanic forcing factor and the influence of previous ocean and atmosphere.The independent sample test prediction of monthly WNPTCF from June to October from 2011 to 2020 was carried out by using this prediction model,and the accuracy rate reached more than 70%,indicating that this prediction model has good prediction effect on the monthly evolution of WNPTCF.The prediction effect of this prediction model for WNPTCF in years with strong ENSO signal is better than that in years with weak ENSO signal.The reason is that in years with weak ENSO signal,the predictability of SubSSTA is low,the nonlinear variability is large,and the forcing effect of ocean on WNPTC is weak.

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呂碩,胡軼佳,孫源,鐘中.一種預測西北太平洋熱帶氣旋逐月演變的新統(tǒng)計模型.氣象科學,2024,44(1):59-72 Lü Shuo, HU Yijia, SUN Yuan, ZHONG Zhong. A new statistical model for predicting month-to-month evolution of tropical cyclones in the western North Pacific. Journal of the Meteorological Sciences,2024,44(1):59-72

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收稿日期:2022-02-22
最后修改日期:2022-03-24
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在線發(fā)布日期: 2024-03-13
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