← 研踪 PaperTrailarXiv:2611.08255 · v1
Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery
首次提交 2026-11-06 00:31
版本更新 2026-11-06 00:31
雷达观测 2026-11-06 12:09
摘要
High-resolution tropical cyclone surface wind speeds derived from synthetic aperture radar (SAR) observations can support storm structure and intensity modelling, but remain spatially and temporally sparse and unavailable near-real-time. Geostationary satellites capture cloud tops continuously in space and time, but do not directly resolve surface winds. Here, we present simple neural networks to predict 2D wind fields and storm-scale intensity metrics from geostationary imagery. Through a series of ablation studies, we find that joint prediction of 2D fields and scalar metrics from the model latent space improves the retrieval of the (radius of) maximum wind, especially for rapid intensification events.
作者备注：Accepted as a workshop paper at Tackling Climate Change with Machine Learning, NeurIPS 2026
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v1 · 2026-11-06 00:31 · 最新观测版本
Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery
High-resolution tropical cyclone surface wind speeds derived from synthetic aperture radar (SAR) observations can support storm structure and intensity modelling, but remain spatially and temporally sparse and unavailable near-real-time. Geostationary satellites capture cloud tops continuously in space and time, but do not directly resolve surface winds. Here, we present simple neural networks to predict 2D wind fields and storm-scale intensity metrics from geostationary imagery. Through a series of ablation studies, we find that joint prediction of 2D fields and scalar metrics from the model latent space improves the retrieval of the (radius of) maximum wind, especially for rapid intensification events.
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