Seasonal prediction of summertime tropical cyclone activity over the East China Sea using the least absolute deviation regression and the Poisson regression

Abstract

AbstractIn the present study, we have employed two statistical models to predict summertime (July–September) tropical cyclone (TC) activity over the East China Sea using the least absolute deviation (LAD) regression and the Poisson regression method. Through a lagged correlation analysis of the relationship between the seasonal TC frequency in the target region and several pre‐season environmental parameters for the period 1979–2003, physically interpretable and statistically significant large‐scale environmental parameters were identified as potential predictors. After applying the predictor screening method based on the stepwise regression, three predictors, i.e. sea surface temperature, outgoing long‐wave radiation and 850‐hPa relative vorticity were finally chosen. They are related to the phase transition of El Niño/Southern Oscillation and the strength of the western North Pacific summer monsoon. The correlation coefficient between the predicted and the observed frequency is 0.75 for the LAD model and 0.78 for the Poisson model. The predictions using the two models have a skill improvement of about 60% compared to the reference forecasts. The present study suggests that both models are skillful in predicting summertime TC frequency over the East China Sea with the Poisson model being slightly more skillful than the LAD model. Copyright © 2009 Royal Meteorological Society