Ensemble-based retrospective analysis of the seasonal snowpack
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- Institutt for geofag [2342]
Sammendrag
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.Artikkelliste
Paper I: Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L. (2018). Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere 12: 247-270, doi: 10.5194/tc-12-247-2018. The article is included in the thesis. Also available in DUO: http://urn.nb.no/URN:NBN:no-74283 |
Paper II: Aalstad, K., Westermann, S., and Bertino, L. (2019). Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment, Vol 239, 15 March 2020, 111618. doi: 10.1016/j.rse.2019.111618. The paper is included in the thesis. The published version is available at: https://doi.org/10.1016/j.rse.2019.111618 |
Paper III: Aalstad, K., Westermann, S., Fiddes, J., and Bertino, L. (2019). Ensemble-based snow reanalysis using dense time stacks of multisensor multispectral satellite imagery. Manuscript to be submitted. To be published. The paper is not available in DUO awaiting publishing. |
Paper IV: Fiddes, J., Aalstad, K., and Westermann, S. (2019): Hyper-resolution ensemblebased snow reanalysis in mountain regions using clustering. Hydrology and Earth System Sciences, 23, 4717–4736, 2019. doi:10.5194/hess-23-4717-2019. The paper is included in the thesis. Also available at: http://hdl.handle.net/10852/72147 |