Review for NeurIPS paper: SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology

Weaknesses: - It is not really clear what types of event are available, and what is the difference between events and episodes. This goes with the point above, it would be nice to have a more in-depth description of the events, their frequency and the possibility of predicting them given the input. Is it simply a label attached to the 384 2 km inputs or is it localized within each image, for each time? It seem that the values of weather radars are very skewed towards 0s, and large values very rare. Also, I wonder if maybe there are some more domain specific loss functions to be optimized, eg taking into account spatial smoothness of signals, rarity of levels, level sets of precipitation, etc.