Competition

Weather4cast 2023 – Data Fusion for Quantitative Hi-Res Rain Movie Prediction under Spatio-temporal Shifts

Aleksandra Gruca · Pilar Rípodas · Xavier Calbet · Llorenç Lliso Valverde · Federico Serva · Bertrand Le Saux · Michael Kopp · David Kreil · Sepp Hochreiter

Virtual
[ ] [ Project Page ]
Fri 15 Dec 7 a.m. PST — 11 a.m. PST

Abstract:

The competition will advance modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hi-res rain radar movies from multi-band satellite sensors requires data fusion of complementary signal sources, multi-channel video frame prediction, as well as super-resolution techniques. To reward models that extract relevant mechanistic patterns reflecting the underlying complex weather systems our evaluation incorporates spatio-temporal shifts: Specifically, algorithms need to forecast 8h of ground-based hi-res precipitation radar from lo-res satellite spectral images in a unique cross-sensor prediction challenge. Models are evaluated within and across regions on Earth with diverse climate and different distributions of heavy precipitation events. Conversely, robustness over time is achieved by testing predictions on data one year after the training period.Now, in its third edition, weather4cast 2023 moves to improve rain forecasts world-wide on an expansive data set and novel quantitative prediction challenges. Accurate rain predictions are becoming ever more critical for everyone, with climate change increasing the frequency of extreme precipitation events. Notably, the new models and insights will have a particular impact for the many regions on Earth where costly weather radar data are not available.

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