24 Jul 2023
 | 24 Jul 2023

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

Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim

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.

Michael Durand et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1653', Anonymous Referee #1, 10 Aug 2023
    • AC1: 'Reply on RC1', Micheal Durand, 16 Aug 2023
      • AC2: 'Reply on AC1', Micheal Durand, 16 Aug 2023
    • AC4: 'Reply on RC1', Micheal Durand, 28 Aug 2023
  • RC2: 'Comment on egusphere-2023-1653', Anonymous Referee #2, 11 Aug 2023
    • AC3: 'Reply on RC2', Micheal Durand, 16 Aug 2023
    • AC5: 'Reply on RC2', Micheal Durand, 28 Aug 2023

Michael Durand et al.

Michael Durand et al.


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Short summary
Seasonal snow accumulates each winter, storing water to release later in the year, modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed, but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.