Predicting weather radar data using an ensemble of neural network models

The topic of predicting weather radar echo is of major interest in both operational and research meteorology. Weather radar measurements are an important data source used by operational meteorologists for weather analysis, radar reflectivity having a significant influence on short-term heavy rainfall prediction. To manage the prediction of multiple time-steps ahead we proposed a novel approach: combining the results of multiple models that each predict at a different time step in the future - an ensemble model we named SepConv-ens. SepConv-ens uses three separable convolution-based deep learning models trained on real radar data from the Romanian National Meteorological Administration (NMA). Experiments reveal a good performance of the model in predicting radar data up to more than 40 minutes ahead and a good correlation between the radar measurements and the predictions in terms of spatial and intensity evolution of the radar echoes. SepConv-ens is integrated in the operational visualisation software utilised by the Romanian NMA and is the first attempt, at the national level, to offer an artificial intelligence-based automated assistance for operational meteorologists.