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Atmospheric processes involve both space and time. This is why human analysisof atmospheric imagery can often extract more information from animated loopsof image sequences than from individual images. Automating such an analysisrequires the ability to identify spatio-temporal patterns in image sequenceswhich is a very challenging task, because of the endless possibilities ofpatterns in both space and time. In this paper we review different concepts andtechniques that are useful to extract spatio-temporal context specifically formeteorological applications. In this survey we first motivate the need forthese approaches in meteorology using two applications, solar forecasting anddetecting convection from satellite imagery. Then we provide an overview ofmany different concepts and techniques that are helpful for the interpretationof meteorological image sequences, such as (1) feature engineering methods tostrengthen the desired signal in the input, using meteorological knowledge,classic image processing, harmonic analysis and topological data analysis (2)explain how different convolution filters (2D/3D/LSTM-convolution) can beutilized strategically in convolutional neural network architectures to findpatterns in both space and time (3) discuss the powerful new concept of'attention' in neural networks and the powerful abilities it brings to theinterpretation of image sequences (4) briefly survey strategies fromunsupervised, self-supervised and transfer learning to reduce the need forlarge labeled datasets. We hope that presenting an overview of these tools -many of which are underutilized - will help accelerate progress in this area.

Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks