ConvLSTM architectures for meteorological nowcasting based on satellite imagery скачать в хорошем качестве

ConvLSTM architectures for meteorological nowcasting based on satellite imagery

The satellite imagery repositories in the custody of MET offer one of the richest collections of meteorological data in Europe, therefore capitalizing on this informational golden ore was only natural in the context of our purposes of developing deep learning models for meteorological nowcasting. As it promised to be a model that can pick up on dynamics similar to the motion of meteorological fronts in the atmosphere, the ConvLSTM model was chosen as the basis for several deep learning architectures employed with the task of predicting meteorological products with respect to prior consecutive temporal satellite readings. The trained models show the promise of adaptation to the temporal progression of meteorological products, granted the model is trained over a considerable number of iterations on significantly computationally demanding data sources.