27 Jun 2023
 | 27 Jun 2023
Status: this preprint is currently under review for the journal TC.

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi

Abstract. Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X- and dual Ku-bands, 10.2, 13.3 and 16.7 GHz), VV polarization obtained at 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assume only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables are iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a). Results show that BASE-AM achieved a RMSE of ~10 cm for snow depth (SD) and less than 30 mm for SWE, compared with the RMSE of ~20 cm SD and ~50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and shows that providing a fully-unbiased snow microstructure prior is not the only promise to obtain accurate SWE retrievals.

Jinmei Pan et al.

Status: open (until 12 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on tc-2023-85', Jinmei Pan, 20 Jul 2023 reply
  • CC1: 'Comment on tc-2023-85', Yurong Cui, 08 Sep 2023 reply

Jinmei Pan et al.

Data sets

Nordic Snow Radar Experiment Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson

Jinmei Pan et al.


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Short summary
We developed an algorithm to estimate snow mass using X- and dual-Ku band radar and tested it in a ground-based experiment. The algorithm, called the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves (AM), achieved an RMSE of 30 mm. These results demonstrate the potential of radar, a highly promising sensor to map snow mass in high spatial resolution.