Ensemble-based retrospective analysis of the seasonal snowpack

Abstract

This thesis presents a satellite-based modeling framework that can estimate how much snow was stored in the terrain. These estimates can help guide climate analysis and prediction. Accurate quantification of Earth’s snow mass is a long-standing problem to which a solution is direly needed with ongoing climate change. Snow plays an essential role in the climate system and snowmelt is a vital source of freshwater for a quarter of the world’s population.

The framework combines satellite imagery and historic weather data to remotely estimate snow mass by leveraging enhanced ensemble-based data assimilation algorithms. The result is a retrospective analysis (reanalysis) of the snow mass that can be obtained for any location on Earth. So far, this framework has been successfully implemented in three different environments: Svalbard, the Californian Sierra Nevada, and the Swiss Alps. In the future, snow reanalyses could be used to train algorithms to predict snow mass in near real time. They may also help validate and subsequently improve climate models. Ultimately this would allow us to make even more informed future projections of the possible fate of the environment that sustains us.