Incorporating Tropical Cyclone (TC) Translation Speed into the Dynamical Statistical Analog Ensemble Forecast Model for Landfalling TC Disasters to Enhance Its Preassessment Capability

In this study, tropical cyclone (TC) translation speed was introduced as a new similarity factor within the generalized initial value (GIV) framework, enhancing the disaster preassessment capability of the dynamical statistical analog ensemble forecast model for landfalling TC disasters (DSAEF_LTD model). Three TC translation speed indicators most relevant to TC precipitation were incorporated: the maximum speed on Day 1 (the first day of TC-induced precipitation and wind occurring on land) and the average and minimum speeds over All Days (all days of TC-induced precipitation and wind occurring on land), all classified using the K-means clustering algorithm. Simulation experiments showed that integrating TC translation speed enhanced the model’s performance. The model provided a better optimal common scheme, with the TS_SUM (sum of threat scores for severe and above and extremely severe and above disasters) increasing by 2.66% (from 0.5117 to 0.5253) compared with the original model. More importantly, its preassessment ability improved significantly, with the average TS_SUM for independent samples increasing by 6.43% (from 0.6488 to 0.6905). The modified model demonstrated greater accuracy in capturing disaster severity and distribution of TCs with significant speed characteristics or with regular tracks. This improvement stemmed from reduced false alarms due to the selection of analogs that are more similar to the target TC. The enhanced preassessment ability can be attributed to the key role of TC translation speed, which significantly influences TC precipitation patterns and improves TC precipitation forecasting. Since precipitation is one of the most crucial disaster-causing factors, better TC precipitation forecasting leads to improved disaster preassessment outcomes. These findings emphasize the promising potential of the DSAEF_LTD model for future TC disaster research and management, contributing to the achievement of the Sustainable Development Goals set by the United Nations 2030 Agenda by strengthening coastal resilience.