Video prediction, which aims to synthesize new consecutive frames subsequent to an existing video. However, its performance suffers from uncertainty of the future. As a potential weather application for video prediction, short time precipitation nowcasting is a more challenging task than other ones as its uncertainty is highly influenced by temperature, atmospheric, wind, humidity and such like. To address this issue, we propose a star-bridge neural network (StarBriNet). Specifically, we first construct a simple yet effective star-shape information bridge for RNN to transfer features across time-steps. We also propose a novel loss function designed for precipitaion nowcasting task. Furthermore, we utilize group normalization to refine the predictive performance of our network. Experiments in a Moving-Digital dataset and a weather predicting dataset demonstrate that our model outperforms the state-of-the-art algorithms for video prediction and precipitation nowcasting, achieving satisfied weather forecasting performance.
@cite_4: The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.
@cite_7: The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal variations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial appearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures.
The task of video prediction is of great value to precipitation nowcasting problem. Previous works such as @cite_4 and @cite_7 have studied in video prediction and applied their models to realistic precipitation forecasting, like Figure .