Articles | Volume 18, issue 6
https://doi.org/10.5194/tc-18-2765-2024
https://doi.org/10.5194/tc-18-2765-2024
Research article
 | 
19 Jun 2024
Research article |  | 19 Jun 2024

Subgridding high-resolution numerical weather forecast in the Canadian Selkirk mountain range for local snow modeling in a remote sensing perspective

Paul Billecocq, Alexandre Langlois, and Benoit Montpetit

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Short summary
Snow covers a vast part of the globe, making snow water equivalent (SWE) crucial for climate science and hydrology. SWE can be inversed from satellite data, but the snow's complex structure highly affects the signal, and thus an educated first guess is mandatory. In this study, a subgridding framework was developed to model snow at the local scale from model weather data. The framework enhanced snow parameter modeling, paving the way for SWE inversion algorithms from satellite data.