Machine learning for remote sensing and modelling of mountain snow patches

用于山雪斑块遥感和建模的机器学习

• 批准号: 2738186
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2738186
• 关键词: Machine; learning; remote; sensing; modelling

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.

山上的雪提供了许多服务，如储存水，习惯和游乐场。它还构成洪水和雪崩风险的威胁，雪对气候变异和变化高度敏感。与卫星传感器和模型的分辨率相比，山区积雪的空间变异性很大，因此，在最需要测量和预测积雪变化的环境中，测量和预测积雪变化特别困难。即使在苏格兰的海洋性气候，雪补丁可以持续整个夏天在有利的山区位置，在冬季和夏季的庇护优惠雪沉积之间的良好平衡，预测这些雪补丁的分布是一个严峻的考验，在气候预测和影响研究中使用的物理雪模型。该项目的动机是使用新的高分辨率遥感和气象建模与机器学习，以提高对山区积雪气候敏感性的理解。