Statistical seasonal forecasting of tropical cyclones over the western North Pacific

Abstract

Abstract Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Many studies and existing seasonal forecasting models have examined and predicted the number of TCs (including geneses and landfalls) mainly based on the environmental factors in the peak TC season. However, these predictions can be time-consuming, computationally expensive and uncertain, depending on the efficiency and predictability of the dynamical models. Therefore, here we propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TCs (intensity at tropical storm or above) over the western North Pacific based on the environmental factors in the preseason. The nine categories comprising 103 candidate predictors in 1980–2015 (36 years) are systematically investigated. The best subset selection regression shows that the sea surface temperatures at the tropical North Atlantic and eastern North Pacific in April, the 500 hPa geopotential height difference between April and January at the open ocean southwest of Australia and the 700 hPa geopotential height at the North Pacific in April are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.89. The model is successfully validated by leave-one-out, nine-fold cross-validations, and later 5 year (2016–2020) observations. The prediction of the SYSU Model exhibits a 95% hit rate in 1980–2020 (39 out of 41), suggesting an operational potential in the seasonal forecasting of TCs over the western North Pacific.