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Deep learning for downscaling tropical cyclone rainfall
Emily Vosper · Lucy Harris · Andrew McRae · Laurence Aitchison · Peter Watson · Raul Santos-Rodriguez · Dann Mitchell
Event URL: https://www.climatechange.ai/papers/neurips2022/13 »

Flooding is often the leading cause of mortality and damages from tropical cyclones. With rainfall from tropical cyclones set to rise under global warming, better estimates of extreme rainfall are required to better support resilience efforts. While high resolution climate models capture tropical cyclone statistics well, they are computationally expensive leading to a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. Here, we develop and evaluate a set of deep learning models for downscaling tropical cyclone rainfall for more robust risk analysis.

Author Information

Emily Vosper (University of Bristol)
Lucy Harris (University of Oxford)
Andrew McRae (University of Oxford)
Laurence Aitchison (University of Bristol)
Peter Watson (Bristol University)
Raul Santos-Rodriguez (University of Bristol)
Dann Mitchell (University of Bristol)

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