Developing high-resolution multidecadal satellite remote sensing-based snow lifecycle reanalysis products over the Northern Hemisphere

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A deep learning model is being trained using a subset of meteorological forcings and remote sensing observations of snow cover to reconstruct seasonal snow water content globally. The relatively small quantity and location-independent model inputs mean that the model is transferrable globally with little computational cost.