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hess.copernicus.org | ||
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www.earth-system-dynamics.net
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esd.copernicus.org
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www.atmospheric-measurement-techniques.net
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gmd.copernicus.org
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| | | Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called multi-scale feature fusion (MFF) that can forecast precipitation with a lead time of up to 3?h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the... | ||
