Statistical seasonal forecasting of tropical cyclone landfalls on Taiwan Island

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Taiwan Island (TW) is one of the key regions that is highly exposed to TCs originated from the western North Pacific. Here, the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model -namely, the Sun Yat-sen University (SYSU) Model -for predicting the number of TC landfalls on TW based on the environmental factors in the preseason. The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January, the 300-hPa specific humidity over the open ocean southwest of Australia in January, the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March, and the sea surface temperature in the South Indian Ocean in April, are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.87. The model is validated by the leave-one-out and nine-fold cross-validation methods, and recent 9-yr observations (2014-2022). The Antarctic Oscillation, variabilities of the western Pacific subtropical high, Asian summer monsoon, and oceanic tunnel are the possible physical linkages or mechanisms behind the model result. The SYSU Model exhibits a 98% hit rate in 1979-2022 (43 out of 44), suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.