Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Convolutional LSTM (Long-Short Term Memory) network is a type of neural network that is well-suited for sequence-to-sequence prediction tasks. It is an extension of the traditional LSTM network that has been widely used in natural language processing and speech recognition.

The Convolutional LSTM network is particularly useful for precipitation nowcasting, which is the prediction of rainfall for a short period of time (e.g., 1-2 hours) in the future. The network takes as input a sequence of radar images and outputs a sequence of predicted rainfall maps.

The network consists of multiple layers of convolutional and LSTM cells. The convolutional layers extract spatial features from the input radar images, while the LSTM cells capture the temporal dependencies between the input frames. The output of the network is a sequence of predicted rainfall maps that can be used to generate a short-term precipitation forecast.

The Convolutional LSTM network has shown promising results in precipitation nowcasting and has the potential to improve weather forecasting and related applications.