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A new approach to skillful seasonal prediction of Southeast Asia tropical cyclone occurrence

Feng, X. ORCID:, Hodges, K., Hoang, L., Pura, A. G., Yang, G.-Y. ORCID:, Luu, H., David, S. J., Duran, G. A. M. W. and Guo, Y.-p. (2022) A new approach to skillful seasonal prediction of Southeast Asia tropical cyclone occurrence. Journal of Geophysical Research: Atmospheres, 127 (12). e2022JD036439. ISSN 2169-8996

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To link to this item DOI: 10.1029/2022JD036439


Predicting the peak-season (July–September) tropical cyclones (TCs) in Southeast Asia (SEA) several months ahead remains challenging, related to limited understanding and prediction of the dynamics affecting the variability of SEA TC activity. Here, we introduce a new statistical approach to sequentially identify mutually independent predictors for the occurrence frequency of peak-season TCs in the South China Sea (SCS) and east of the Philippines (PHL). These predictors, which are identified from the pre-season (April–June) environmental fields, can capture the interannual variability of different clusters of peak-season TCs, through a cross-season effect on large-scale environment that governs TC genesis and track. The physically oriented approach provides a skillful seasonal prediction in the 41-year period (1979–2019), with r=0.73 and 0.54 for SCS and PHL TC frequency, respectively. The lower performance for PHL TCs is likely related to the non-stationarity of the cross-season TC–environment relationship. We further develop the statistical approach to a hybrid method using the predictors derived from dynamical seasonal forecasts. The hybrid prediction shows a significant skill for both SCS and PHL TCs, for lead-times up to four or five months ahead, related to the good performance of models for the sea surface temperatures and low-level winds in the tropics. The statistical and hybrid predictions outperform the dynamical predictions, showing the potential for operational use.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:105539
Publisher:American Geophysical Union


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