A new approach to generate dynamical cone of uncertainty for cyclone track forecast over North Indian Ocean

Akhil Srivastava (), M. Mohapatra, Ananda K. Das, Raghu Nadimpalli and Suneet Dwivedi
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Akhil Srivastava: Ministry of Earth Sciences
M. Mohapatra: Ministry of Earth Sciences
Ananda K. Das: Ministry of Earth Sciences
Raghu Nadimpalli: Ministry of Earth Sciences
Suneet Dwivedi: University of Allahabad

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 4, No 15, 3467-3485

Abstract: Abstract The North Indian Ocean 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 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, dynamical CoU (DYN-COU) based on dynamical Ensemble Prediction Systems 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 (utilising CLM-COU and DYN-COU) are analysed with respect to various characteristics of 13 TCs formed during 2019–2020. The result suggests that the CLM-COU is more skilful during the first 36 h 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 h and DYN-COU shows better skill with lead time longer than 18 h. For recurving TCs, CLM-COU provides better uncertainty forecast information up to 96-h forecast lead time as compared to DYN-COU based forecast uncertainty information. The skill of DYN-COU-based forecast uncertainty information is also dependent on the operational track forecast performance, which has more errors for recurving TCs as compared to straight moving TCs.

Keywords: Ensemble prediction system; Tropical cyclone; Dynamical cone of uncertainty (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-023-06339-6

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