Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Examining the Predictability of Tropical Cyclogenesis through the Ensemble-Based Data Assimilation System

Version 1 : Received: 16 October 2023 / Approved: 16 October 2023 / Online: 19 October 2023 (06:19:43 CEST)

A peer-reviewed article of this Preprint also exists.

Hoa, D.N.-Q.; Tien, T.-T.; Nhu, N.-Y.; Dao, T.L. Examining the Predictability of Tropical Cyclogenesis over the East Sea of Vietnam through the Ensemble-Based Data Assimilation System. Atmosphere 2023, 14, 1671. Hoa, D.N.-Q.; Tien, T.-T.; Nhu, N.-Y.; Dao, T.L. Examining the Predictability of Tropical Cyclogenesis over the East Sea of Vietnam through the Ensemble-Based Data Assimilation System. Atmosphere 2023, 14, 1671.

Abstract

In this study, we conducted experiments to assess the forecasting capabilities for the tropical cyclone (TC) genesis over the South China Sea using the ensemble-based data assimilation system (EPS-DA) by WRF-LETKF. These experiments covered forecast lead times of up to 5 days and spanned a period from 2012 to 2019, involving a total of 45 TC formation events. The evaluation involved forecast probability assessments and positional and timing error analysis. Results indicated that successful forecasting depends on lead time and initial condition quality. For TC formation from an embryo vortex to tropical depression intensity, the EPS-DA system demonstrated improved accuracy as the forecast cycle approached the actual formation time. TC centers converged toward observed locations, highlighting the potential of assimilation up to 5 days before formation. We examined statistical variations in dynamic and thermodynamic variables relevant to TC processes, offering an objective system assessment. Our study emphasized early warnings of TC development appear linked to formation-time environmental conditions, particularly strong vorticity and enhanced moisture processes.

Keywords

tropical cyclones; tropical cyclogenesis, ensemble prediction system; data assimilation

Subject

Environmental and Earth Sciences, Atmospheric Science and Meteorology

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