  The accurate prediction and estimation of annual snow accumulation has grown
in importance as we deal with the effects of climate change and the increase of
global atmospheric temperatures. Airborne radar sensors, such as the Snow
Radar, are able to measure accumulation rate patterns at a large-scale and
monitor the effects of ongoing climate change on Greenland's precipitation and
run-off. The Snow Radar's use of an ultra-wide bandwidth enables a fine
vertical resolution that helps in capturing internal ice layers. Given the
amount of snow accumulation in previous years using the radar data, in this
paper, we propose a machine learning model based on recurrent graph
convolutional networks to predict the snow accumulation in recent consecutive
years at a certain location. We found that the model performs better and with
more consistency than equivalent nongeometric and nontemporal models.
