Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units

Abstract

Nowcasting of clouds is a challenging spatiotemporal task due to the dynamic nature of the atmosphere. In this study, the use of convolutional gated recurrent unit networks (ConvGRUs) to produce short-term cloudiness forecasts for the next 3 h over Europe is proposed, along with an optimisation criterion able to preserve image structure across the predicted sequences. This approach is compared against state-of-the-art optical flow algorithms using over two and a half years of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation satellite. We show that the ConvGRU trained using our structure-preserving loss function significantly outperforms the optical flow algorithms with an average change in R2, mean absolute error and structural similarity of 12.43%, −8.75% and 9.68%, respectively, across all time steps. We also confirm that merging multiple optical flow algorithms into an ensemble yields significant short-term performance increases (<1 h), and that nowcast skill can vary significantly across different European regions. Furthermore, our results show that blurry images resulting from using globally oriented loss functions can be avoided by optimising for structural similarity when producing nowcasts. We thus showcase that deep-learning-based models using locally oriented loss functions present a powerful new way to produce accurate cloud nowcasts, with important applications to be found in solar power forecasting.