Preprints
https://doi.org/10.5194/egusphere-2025-5790
https://doi.org/10.5194/egusphere-2025-5790
10 Dec 2025
 | 10 Dec 2025
Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Assimilation of synthetic radar backscatters at Ku-band improves SWE estimates

Nicolas R. Leroux, Vincent Vionnet, Courtney Bayer, Julien Meloche, Arlan Dirkson, Franck Lespinas, Mark Buehner, Marco Carrera, Benoit Montpetit, Bernard Bilodeau, Maria Abrahamowicz, and Chris Derksen

Abstract. In cold regions, snow serves as the primary water source for downstream rivers and lakes. Accurate gridded snow water equivalent (SWE) estimation is hindered by the sparse ground observation network and the low resolution of satellite passive microwave products. To address this, Environment and Climate change Canada (ECCC), the Canadian Space Agency (CSA), and Natural Resources Canada (NRCan) are developing the Terrestrial Snow Mass Mission (TSMM), a dual Ku-band satellite mission designed to measure backscatter at 13.5 GHz and 17.25 GHz across the Northern Hemisphere at a 500-m spatial resolution with a weekly temporal resolution. This study assesses the feasibility of assimilating Ku-band backscatter to enhance SWE estimates in a synthetic experiment. We used the Soil-Vegetation-Snow version 2 (SVS2) land surface model, which incorporates the snowpack model Crocus, coupled with the Snow Microwave Radiative Transfer model (SMRT). Observations extracted at weekly intervals from synthetic truths (SWE and backscatter) were assimilated with a particle filter in point-scale at three sites spanning different Canadian climates (Arctic, humid continental, Alpine) over three winter seasons. Meteorological forcing derived from the high-resolution Canadian meteorological model was perturbed to generate ensembles of snow simulations for assimilation. Results indicate that assimilating backscatter observations reduced the mean continuous ranked probability score (CRPS) of SWE estimates by up to 32 % at the Arctic and humid continental climate sites compared to the open-loop ensemble, performing similarly to the assimilation of SWE with an observation error larger than 20 %. Assimilating backscatter observations at the Alpine site only improved the SWE estimates by 5 % as backscatter signals seemed to lose sensitivity to SWE values greater than ~300 kg m2 in our experimental setup. Assimilating backscatter and SWE observations also improved the estimations of vertical profiles of snow density and specific surface area. These findings demonstrate the potential of direct assimilation of Ku-band backscatter to enhance both estimates of SWE and snowpack properties.

Competing interests: Chris Derksen is chief editor for The Cryosphere

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Nicolas R. Leroux, Vincent Vionnet, Courtney Bayer, Julien Meloche, Arlan Dirkson, Franck Lespinas, Mark Buehner, Marco Carrera, Benoit Montpetit, Bernard Bilodeau, Maria Abrahamowicz, and Chris Derksen

Status: open (until 21 Jan 2026)

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Nicolas R. Leroux, Vincent Vionnet, Courtney Bayer, Julien Meloche, Arlan Dirkson, Franck Lespinas, Mark Buehner, Marco Carrera, Benoit Montpetit, Bernard Bilodeau, Maria Abrahamowicz, and Chris Derksen

Model code and software

Code of the Soil Vegetation and Snow version 2 (SVS2) coupled with the Snow Microwave Radiative Transfer model (SMRT) within the The Multiple Snow Data Assimilation System (MuSA) N. R. Leroux et al. https://doi.org/10.5281/zenodo.17662807

Nicolas R. Leroux, Vincent Vionnet, Courtney Bayer, Julien Meloche, Arlan Dirkson, Franck Lespinas, Mark Buehner, Marco Carrera, Benoit Montpetit, Bernard Bilodeau, Maria Abrahamowicz, and Chris Derksen
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Latest update: 10 Dec 2025
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Short summary
This study evaluates the assimilation of Ku-band radar backscatter into a multilayer snowpack model to support the upcoming Terrestrial Snow Mass Mission. Synthetic experiments were conducted at Arctic, continental, and alpine sites over three winters using a particle filter. Results show that assimilating backscatter improves estimates of snow water equivalent, depth, and vertical snow properties, laying the groundwork for future satellite missions focused on radar-based snow monitoring.
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