Local-scale wind forecasting for tropical cyclone early warnings

Abstract

Tropical Cyclones (TCs) damage assets and threaten populations globally, multiple times a year. Forecasting products from meteorological agencies across the world can help anticipate their likely path, intensity and broad regional impact. They are critical in informing safety warnings and potential evacuation measures at the county scale. These products are not designed to represent experience on the ground at a scale characteristic of individual neighborhoods (i.e. the local scale, ~10 3 m). This limits their usability for granular decision making. Local-scale simulations are achievable using full physics numerical weather prediction models, but the associated computational requirements typically allow for only a handful of deterministic simulations to be performed in real-time. This restricts their use in applications requiring probabilistic information. Recent developments from AI based weather forecasting models provide vastly more efficient TC forecasting solutions that can run simulation ensembles to provide probabilistic information in real-time. Yet these are fundamentally limited by the resolution of the data they train on, which currently fails to represent the local scale. We here introduce LiveCyc, a machine learning approach that can augment any TC track and intensity forecast with a probabilistic local-scale wind forecast. After an overview of the algorithms forming LiveCyc, we introduce an extensive dataset of historical back tests. Using this dataset, we show the value of LiveCyc in informing local-scale decision making in the days before a TC makes landfall. In particular, we demonstrate how objective cost-saving optimization can calibrate and automate the triggering of protective actions ahead of a TC event.