基于地球静止卫星影像的热带气旋风场与强度临近预报
Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery
Simon Donike, Pritthijit Nath, Cristina Radin, Arthur Avenas, Ruben Cartuyvels, Emiliano Diaz, William K. Jones, Anna Jungbluth
AI 速读 基于摘要
作者提出用简单神经网络从地球静止卫星影像预测二维风场和风暴尺度强度指标，并通过消融实验发现联合预测可改善最大风速及其半径的估计。
cs.LG
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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.