Pedro Herruzo, Aleksandra Gruca, Llorenç Lliso, Xavier Calbet, Pilar Rípodas, Sepp Hochreiter, Michael Kopp, and David P Kreil

Competition challeges

The core and transfer learning challenges of the Weather4cast 2021 competition.

Weather forecasting is both a high impact application as well as a complex Big Data modelling challenge. Recent advances in machine learning have already demonstrated the power of non-physical modelling approaches for the prediction of rainfall. The Weather4cast competitions now provide a unique multi-channel benchmark for the prediction of up to 8 hours of weather with high temporal and spatial resolutions (15 min, 4 km) for a diverse set of large regions across Earth. This diversity, for the first time, also permits a meaningful spatial transfer learning challenge in weather forecasting.Weather4cast introduces multi-channel weather ‘movies’ that encode temperature, rainfall, cloud properties, and turbulence as derived from the meteorological satellites by the EUMETSAT NWC SAF. Inspired by the Traffic4cast competitions at the NeurIPS conferences in 2019 and 2020, weather forecasting is thus presented as a video frame prediction task. As then, the U-Net based models developed for photographic image analysis intriguingly did well on these artificial videos. In contrast, however, the winning submission did not employ a U-Net but a recurrent convolutional network with residual units.Weather4cast introduces the first spatial transfer learning challenge in weather forecasting: only one-hour short snippets from spatial regions never seen before were provided as input to models. We can thus now present first insights from submissions to this spatial transfer learning challenge. Notably, models with better core prediction performance also generalized better. Moreover, the two top-ranked models – one RCN based, one U-Net based – were further ahead of the remaining top-ranked submissions for spatial transfer learning (+6%) than in the core prediction challenge (+1%).While submissions tested varying strategies for input data selection and training, there remains a wide range of additional complementary approaches to be explored in future analyses. The competition and its leaderboards remain available and open for new submissions on the weather4cast.ai website.

2021 IEEE International Conference on Big Data (Big Data), 5750-5757, 2021-12-15.

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IARAI Authors
Pedro Herruzo, Dr Aleksandra Gruca, Dr Sepp Hochreiter, Dr Michael Kopp​, Dr David Kreil
Research
Weather and Climate
Keywords
Benchmark Dataset, Competition, Machine Learning, Transfer Learning, Weather Prediction

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