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Very few\nprevious studies have examined this crucial and challenging weather forecasting\nproblem from the machine learning perspective. In this paper, we formulate\nprecipitation nowcasting as a spatiotemporal sequence forecasting problem in\nwhich both the input and the prediction target are spatiotemporal sequences. By\nextending the fully connected LSTM (FC-LSTM) to have convolutional structures\nin both the input-to-state and state-to-state transitions, we propose the\nconvolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model\nfor the precipitation nowcasting problem. Experiments show that our ConvLSTM\nnetwork captures spatiotemporal correlations better and consistently\noutperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for\nprecipitation nowcasting.","url_abs":"http://arxiv.org/abs/1506.04214v2","url_pdf":"http://arxiv.org/pdf/1506.04214v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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