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www.earth-system-dynamics.net | ||
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gmd.copernicus.org
Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (Lagrangian persistence). In that context, ... |
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www.atmospheric-measurement-techniques.net
[AI summary] The page provides the publisher information, article options, and submission guidelines for the Atmospheric Measurement Techniques journal under the European Geosciences Union. |
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hess.copernicus.org
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gmd.copernicus.org
GMD - Metrics - Deep learning model based on multi-scale feature fusion for precipitation nowcasting 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... |
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