Preprints
https://doi.org/10.5194/egusphere-2024-3213
https://doi.org/10.5194/egusphere-2024-3213
14 Nov 2024
 | 14 Nov 2024
Status: this preprint is open for discussion.

Evaluation of the Snow CCI Snow Covered Area Product within a Mountain Snow Water Equivalent Reanalysis

Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen

Abstract. An accurate characterization of global snow water equivalent (SWE) is essential in the study of climate and water resources. The current global SWE dataset from the European Space Agency Snow Climate Change Initiative is derived from the assimilation of passive microwave satellite data and in situ snow depth measurements. However, gaps exist in the current Snow CCI SWE dataset in complex terrain due to difficulties in characterizing mountain SWE via the passive microwave sensing approach and limitations of the in situ snow depth measurements. This study applies a Bayesian snow reanalysis approach with the existing Snow CCI snow cover fraction (SCF) dataset (1 km resolution) to develop a SWE dataset over four mountainous domains in Western North America for WYs 2001–2019. The reanalysis SWE estimates are evaluated through comparisons with independent SWE datasets, and a parallel SWE reanalysis generated using snow extent retrieved from Landsat imagery (30 m resolution). Biases in Snow CCI reanalysis SWE were diagnosed by comparing Snow CCI snow cover with the Landsat reference. Both the number of SCF images and their characteristics (such as zenith angle) significantly affect the accuracy of SWE estimation. Overall, the Snow CCI SCF inputs produce reanalysis SWE of sufficient quality to fill the mountain SWE gap in the current Snow CCI SWE climate data record. A better characterization of the SCF uncertainty and a bias correction could further improve the accuracy of the reanalysis SWE estimates.

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Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen

Status: open (until 12 Jan 2025)

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Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen

Data sets

Snow CCI and Landsat reanalysis outputs Haorui Sun https://doi.org/10.5281/zenodo.13930080

Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen

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Short summary
The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) Climate Data Record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in-situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI Snow Cover Fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.