An Objective Track Similarity Index and Its Preliminary Application to Predicting Precipitation of Landfalling Tropical Cyclones

Abstract

Abstract Combining dynamical model output and statistical information in historical observations is an innovative approach to predicting severe or extreme weather. In this study, in order to examine a dynamical–statistical method for precipitation forecasting of landfalling tropical cyclones (TC), an objective TC track similarity area index (TSAI) is developed. TSAI represents an area of the enclosed scope surrounded by two TC tracks and two line segments connecting the initiating and ending points of the two tracks. The smaller the TSAI value, the greater the similarity of the two TC tracks, where a value of 0 indicates that the two tracks overlap completely. The TSAI is then preliminarily applied to a precipitation forecast test of landfalling TCs over South China. Given the considerable progress made in TC track forecasting over past few decades, TC track forecast products are also used. Through this test, a track-similarity-based landfalling TC precipitation dynamical–statistical ensemble forecast (LTP_DSEF) model is established, which consists of four steps: adopting the predicted TC track, determining the TC track similarity, checking the seasonal similarity, and making an ensemble prediction. Its application to the precipitation forecasts of landfalling TCs over South China reveals that the LTP_DSEF model is superior to three numerical weather prediction models (i.e., ECMWF, GFS, and T639/China), especially for intense precipitation at large thresholds (i.e., 100 or 250 mm) in both the training (2012–14) and independent (2015–16) samples.