Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Convolutional LSTM (Long Short-Term Memory) network is a machine learning approach used for precipitation nowcasting. It combines the strengths of convolutional neural networks (CNNs) and LSTMs to make accurate predictions of precipitation in the near future.

CNNs are good at understanding spatial features in images, while LSTMs are good at understanding temporal dependencies in time-series data. By combining these two networks, the Convolutional LSTM network is able to learn both spatial and temporal features of precipitation data.

The network takes in a sequence of radar images and predicts the precipitation intensity for the next few minutes. The output of the network is a sequence of precipitation maps that show the predicted intensity of precipitation over time.

This approach has shown promising results in accurately predicting precipitation in the near future, which can have significant applications in weather forecasting and disaster management.