Improvement of tropical cyclone prediction with vortex initialization in cyclic data assimilation using WRF model

Meenakshi Shenoy, V. S. Prasad, D. Srinivas, K. B. R. R. Hari Prasad, Suryakanti Dutta and P. V. S. Raju ()
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Meenakshi Shenoy: Amity University Rajasthan, Centre for Ocean Atmospheric Science and Technology
V. S. Prasad: Ministry of Earth Sciences, National Centre for Medium Range Weather Forecasting (NCMRWF)
D. Srinivas: Ministry of Earth Sciences, National Centre for Medium Range Weather Forecasting (NCMRWF)
K. B. R. R. Hari Prasad: Ministry of Earth Sciences, National Centre for Medium Range Weather Forecasting (NCMRWF)
Suryakanti Dutta: Ministry of Earth Sciences, National Centre for Medium Range Weather Forecasting (NCMRWF)
P. V. S. Raju: Amity University Rajasthan, Centre for Ocean Atmospheric Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 19, No 40, 23349-23378

Abstract: Abstract Vortex initialization (VI) has demonstrated improvements in tropical cyclone forecasting, yet the sustained, stage-wise benefits of VI combined with cyclic data assimilation (DA) remain unquantified. This study addresses that gap by evaluating the sustained impact of 6-hourly VI and three-dimensional (3DVAR) DA across all cyclone stages using the WRF model. Results indicate that VI + DA corrects inner-core moisture and thermal fields at initialization, boosting latent heat release and balanced ascent, which leads to improved representation of wind and temperature structures from cyclonic storm (CS) stage onward. These enhancements persist through intensification, maintaining a coherent core dynamic structure throughout CS, Severe Cyclonic Storm (SCS), Very Severe Cyclonic Storm (VSCS), and Extremely Severe Cyclonic Storm (ESCS) phases. Compared to cyclic DA, VI + DA reduces track errors by 20–30% and intensity errors by 10–15%. At landfall, the combined captures the opterved 90 kt peak winds and wind structure, reducing landfall location errors by up to 80% and intensity errors by up to 50%. Overall, VI with cyclic DA not only refines the initial vortex dynamics but also sustains and amplifies forecast skill, including track, intensity, and structural accuracy, throughout the cyclone’s evolution.

Keywords: Tropical cyclones; WRF; Vortex initialization; GSI; Data assimilation; Bay of Bengal (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07721-2

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