A statistical-dynamical approach to intraseasonal prediction of tropical cyclogenesis in the western North Pacific

Abstract

We have developed a combined statistical-dynamical prediction scheme to predict the probability of tropical cyclone (TC) formation at daily, 2.5° horizontal resolution across the western North Pacific at intraseasonal lead times. Through examination of previous research and our own analysis, we chose five variables to represent the favorability of the climate system to support tropical cyclogenesis. These so-called large-scale environmental factors (LSEFs) include: low-level relative vorticity, sea surface temperature, vertical wind shear, Coriolis, and upper-level divergence. Logistic regression was employed to generate a statistical model representing the probability of TC formation at every grid point based on these LSEFs. Thorough verification of zero-lead hindcasts reveals this model displays skill and potential value for risk adverse customers. In particular, these hindcasts had a positive Brier skill score of 0.03 and a skillful relative operating characteristic skill score of 0.68. The fully coupled, one-tier NCEP Climate Forecast System was used as the dynamical model with which to forecast the LSEFs and, in turn, force the regression model. A series of individual TC case studies were conducted to demonstrate the predictive potential, at intraseasonal leads, of our statistical-dynamical method. Lastly, we investigated the applicability of intraseasonal forecasts to military planning.