Machine learning for remote sensing and modelling of mountain snow patches

Snow in the mountains provides many services as a store of water, a habit and a playground. It also poses threats as a flood and avalanche risk, and snow is highly sensitive to climate variability and change.

The high spatial variability of snow cover in mountains, compared with the resolutions of satellite sensors and models, makes measurement and prediction of changes in snow cover particularly difficult in the very environments for which they are most needed.

Even in the maritime climate of Scotland, snow patches can persist throughout the summer in favourable mountain locations; the fine balance between preferential snow deposition in winter and sheltering in summer would make predicting the distribution of these snow patches a stern test for the kind of physically based snow models used in climate projection and impacts studies.

The motivation for this project is to use newly available high resolution remote sensing and meteorological modelling with machine learning to improve understanding of the climate sensitivity of mountain snow.