Deep Learning for High-Resolution Weather and Weather Prediction

Le résumé fourni par la source

Recent advances in deep learning have opened new avenues in weather forecasting by providing novel methods to enhance prediction accuracy, increase resolution, and capture complex spatiotemporal dependencies inherent in atmospheric phenomena. Traditional numerical weather prediction (NWP) methods, while robust, are computationally expensive and struggle to capture localized extreme events such as heavy rainfall or convective storms. In contrast, deep learning techniques—ranging from convolutional neural networks (CNNs) and recurrent neural networks (RNNs) (including LSTM and ConvLSTM variants) to emerging capsule network architectures—offer a data-driven complement to physics-based models. This paper reviews recent progress in the use of deep learning for weather forecasting, discusses methodologies for generating high-resolution forecasts from coarse data (i.e., downscaling), and evaluates approaches for predicting heavy rainfall and extreme weather events. We critically analyze works such as “DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast” [1] and “Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall” [2], as well as the survey “Survey on the Application of Deep Learning in Extreme Weather Prediction” [3]. We also integrate additional findings from the broader literature [4–20] to present a comprehensive overview. Challenges and opportunities in model interpretability, data assimilation, and computational efficiency are examined, and future research directions are proposed.

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