A deep learning based approach with adversarial regularization for Doppler weather radar ECHO prediction

Precipitation nowcasting is an important component for accurate weather modeling and Doppler radar data acts as an important input for nowcasting models. In this work, we propose a deep learning based approach for radar echo states prediction. Our approach uses a hybrid structure of convolutions within Long Short Term Memory recurrent network structure and a discriminator network is added in the loss objective to refine the predictions acting as a regularizer. This models the spatio-temporal nature of the problem explicitly in the neural network. We show that this model can be applied for fine grained short term precipitation prediction with improvement in evaluation metrics as compared to strong baselines. The proposed model improves recall score by 11% compared to without adversarial regularization. Results are presented using usual train, test strategy for the task of echo state prediction and derived precipitation based skill scores on the data from Seattle, WA, USA.