A New Approach to Generate Dynamical Cone of Uncertainty for Cyclone Track Forecast over North Indian Ocean

Abstract

Abstract The North Indian Ocean (NIO) tropical cyclones (TCs) are devastating multi-hazard disasters with associated gale wind, torrential rainfall and storm-surge which pose severe threat to humankind and responsible for huge economic loss and livelihood. Predictions of track, intensity and structure along with the track uncertainty are very important to produce forecast and warning for the populace and disaster managers. India Meteorological Department (IMD) issues operational track forecasts and associated uncertainty as a Cone of Uncertainty (CoU) based on the climatological track errors computed from past operational forecasts. Due to large variation between different TCs, instead of static, dynamic CoU (DYN-CoU) based on dynamical Ensemble Prediction Systems (EPS) is more sophisticated way to provide track uncertainty. In this study, the utility of different CoU including (i) Climatological CoU (CLM-CoU) (ii) CoU from dynamical EPS using different methods and (iii) Hybrid CoU (utilizing CLM-CoU & DYN-CoU) are analyzed with respect to various characteristics of 13 TCs formed during 2019–2020. The result suggests that the CLM-CoU is more skillful during the first 36 hours of forecast whereas DYN-CoU based on weights assigned to different members of EPS performs better during longer lead time. Basin-wise, CLM-CoU performs better over the Arabian Sea for all the forecast hours whereas in case of Bay of Bengal during first 18 hours and DYN-CoU shows better skill with lead time longer than 18 hours. For recurving TCs, CLM-CoU performs better up to 96 hour forecast lead. However, skill of DYN-CoU in case of recurving TCs is dependent on the operational and EPS track forecast performance.