| 10 Dec 2025
Assimilation of synthetic radar backscatters at Ku-band improves SWE estimates
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 m−2 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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In this paper, the authors conducted an important research to explore the data assimilation potential of the TSMM mission of Canada, which provides dual-Ku backscatter measurements in weekly intervals.
However, firstly, the authors didn't clearly present whether all weekly backscatter coefficients were input together to constrain the entire snow season, or the snow process was updated at weekly steps.
If it is the first case, previous studies would not recommend perturbing the seasonal pattern of snowfalls. Instead, an adjustable constant multiplication factor will be applied to the entire snow season.
The current assumption cannot enumerate all possibilities in the meteorological forcing errors for the entire snow season. Therefore, instead, it adds great noise into the SWE and backscatter ensembles (e.g., Fig.2h), which have made DA really difficult.
Due to the reasons mentioned above, the presented ensembles alone in Fig.2 fail to convince me that dual-Ku backscatter can work well to constrain SWE uncertainty, although it indeed could.
The simulations also have other problems: usually, if the snowpack is deeper, the snow will tend to melt more slowly under the same energy input (air temperature+radiation). It is unrealistic that the spread of snow-off dates is so narrow in Rogers Pass, which is even narrower than the very shallow snow at TVC in Fig.2(k). It should be noted that the largest and the smallest peak SWEs for Rogers Pass differ by over 300 mm, whereas that for TVC is only 20 mm. Therefore, I guess there is also some problem in the snow process modeling. If the snow-off dates are correctly simulated, even fractional snow cover from optical sensors can be used for DA; backscatter should be more powerful.
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