Retrieval of SWE from dual-frequency radar measurements: Usingtimeseries to overcome the need for accurate a priori information

Abstract

Abstract. Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, microstructure and to surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem, and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of the prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than than 20 %, and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally.

Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

“…With the saturation effect of the modelled background properties within the Ku-Band range, the volume scattering will only become more dominant compared to the background surface scattering. These results show that a distributed, statistical approach, for all the retrieved parameters, is more suited to forward modelling of the Ku-Band signal (Pan et al, 2023), even though this approach is less preferable for satellite observation SWE retrievals (Durand et al, 2023). To improve efficiency in forward modelling computation time, the snowpacks of January were reduced to two layers, i.e.…”

“…The synergistic radiometer measurements were effective in providing first-guess information on effective snow grain size which was used within the SWE retrieval approach developed and evaluated by Lemmetyinen et al (2018). These tower measurements have also been used to support other algorithm development experiments (Merkouriadi et al, 2021;Zhu et al, 2021;Durand et al, 2023;Pan et al, 2023), with the daily temporal resolution proving to be particularly insightful. However, a major limitation was the lack of spatial sampling, so efforts in the community shifted to the acquisition of airborne Ku-band radar data.…”