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Accurate rainfall forecasting is critical because it has a great impact onpeople's social and economic activities. Recent trends on various literaturesshow that Deep Learning (Neural Network) is a promising methodology to tacklemany challenging tasks. In this study, we introduce a brand-new data-drivenprecipitation prediction model called DeepRain. This model predicts the amountof rainfall from weather radar data, which is three-dimensional andfour-channel data, using convolutional LSTM (ConvLSTM). ConvLSTM is a variantof LSTM (Long Short-Term Memory) containing a convolution operation inside theLSTM cell. For the experiment, we used radar reflectivity data for a two-yearperiod whose input is in a time series format in units of 6 min divided into 15records. The output is the predicted rainfall information for the input data.Experimental results show that two-stacked ConvLSTM reduced RMSE by 23.0%compared to linear regression.

DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data