The paper "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting" by Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo addresses the challenge of precipitation nowcasting, which involves predicting future rainfall intensity in a local region over a short period. The authors formulate this problem as a spatiotemporal sequence forecasting task, where both input and output sequences are spatiotemporal. They propose the Convolutional LSTM (ConvLSTM) network, an extension of the fully connected LSTM (FC-LSTM) with convolutional structures in both input-to-state and state-to-state transitions. This network is designed to better capture spatiotemporal correlations and is evaluated on both synthetic and real radar echo datasets. The results show that the ConvLSTM network outperforms the FC-LSTM and the state-of-the-art ROVER algorithm in precipitation nowcasting, demonstrating its effectiveness in handling complex spatiotemporal data and boundary conditions. The paper also discusses the advantages of larger state-to-state kernels in capturing spatiotemporal motion patterns and the benefits of deeper models with fewer parameters.The paper "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting" by Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo addresses the challenge of precipitation nowcasting, which involves predicting future rainfall intensity in a local region over a short period. The authors formulate this problem as a spatiotemporal sequence forecasting task, where both input and output sequences are spatiotemporal. They propose the Convolutional LSTM (ConvLSTM) network, an extension of the fully connected LSTM (FC-LSTM) with convolutional structures in both input-to-state and state-to-state transitions. This network is designed to better capture spatiotemporal correlations and is evaluated on both synthetic and real radar echo datasets. The results show that the ConvLSTM network outperforms the FC-LSTM and the state-of-the-art ROVER algorithm in precipitation nowcasting, demonstrating its effectiveness in handling complex spatiotemporal data and boundary conditions. The paper also discusses the advantages of larger state-to-state kernels in capturing spatiotemporal motion patterns and the benefits of deeper models with fewer parameters.