A Physically-Motivated Stochastic Model of Tropical Cyclone Dynamic Wind Fields
Abstract
Tropical cyclones (TCs) pose significant risk to infrastructure and human safety, and the frequency and intensity of large TCs are expected to increase in the future. Quantifying such risk to coastal and inland regions is challenging, largely due to the rare nature of TCs which limits historical data availability. We introduce a new stochastic model of TC wind fields conditional on a small number of physical variables commonly found in synthetic and observed storm track databases. The model consists of a physically-motivated mean trend and an expansion of the residual field into a wavelet frame. We demonstrate the approach on a dataset consisting of historical data for storms impacting the northwest Gulf of Mexico. The model adequately captures asymmetry and nonstationary space-time correlation structures of observed wind fields.
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BibTeX
@misc{leduc2026physicallymotivated,
title = {{A Physically-Motivated Stochastic Model of Tropical Cyclone Dynamic Wind Fields}},
author = {Matthew LeDuc and William Kleiber},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31349},
eprint = {2610.31349},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31349},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Physically-Motivated Stochastic Model of Tropical Cyclone Dynamic Wind Fields
AU - Matthew LeDuc
AU - William Kleiber
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.31349
UR - https://arxiv.org/abs/2610.31349
AB - Tropical cyclones (TCs) pose significant risk to infrastructure and human safety, and the frequency and intensity of large TCs are expected to increase in the future. Quantifying such risk to coastal and inland regions is challenging, largely due to the rare nature of TCs which limits historical data availability. We introduce a new stochastic model of TC wind fields conditional on a small number of physical variables commonly found in synthetic and observed storm track databases. The model consists of a physically-motivated mean trend and an expansion of the residual field into a wavelet frame. We demonstrate the approach on a dataset consisting of historical data for storms impacting the northwest Gulf of Mexico. The model adequately captures asymmetry and nonstationary space-time correlation structures of observed wind fields.
ER -