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The accurate prediction and estimation of annual snow accumulation has grownin importance as we deal with the effects of climate change and the increase ofglobal atmospheric temperatures. Airborne radar sensors, such as the SnowRadar, are able to measure accumulation rate patterns at a large-scale andmonitor the effects of ongoing climate change on Greenland's precipitation andrun-off. The Snow Radar's use of an ultra-wide bandwidth enables a finevertical resolution that helps in capturing internal ice layers. Given theamount of snow accumulation in previous years using the radar data, in thispaper, we propose a machine learning model based on recurrent graphconvolutional networks to predict the snow accumulation in recent consecutiveyears at a certain location. We found that the model performs better and withmore consistency than equivalent nongeometric and nontemporal models.

Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar