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The Cryosphere An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/tc-2017-109
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
04 Jul 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).
Ensemble-based assimilation of fractional snow covered area satellite retrievals to estimate snow distribution at a high Arctic site
Kristoffer Aalstad1, Sebastian Westermann1, Thomas Vikhamar Schuler1, Julia Boike2, and Laurent Bertino3 1Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
2Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Telegrafenberg A43, 14473 Potsdam, Germany
3Nansen Environmental and Remote Sensing Center, Thormøhlensgate 47, Bergen 5006, Norway
Abstract. Snow, with high albedo, low thermal conductivity and large water holding capacity strongly modulates the surface energy and water balance, thus making it a critical factor in high-latitude and mountain environments. At the same time, already at medium spatial resolutions of 1 km, estimating the average and subgrid variability of the snow water equivalent (SWE) is challenging in remote sensing applications. In this study, we demonstrate an ensemble-based data assimilation scheme to estimate peak SWE distributions at such scales from a simple snow model driven by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (that is accessible from satellite products) to pre-melt SWE, while at the same time obtaining the subgrid scale distribution. Subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fractional snow covered area (fSCA) by means of the snow model. Assimilation of satellite-derived fSCA hence facilitates the constrained estimation of the average SWE and coefficient of variation, while taking into account uncertainties in both the model and assimilated data sets. Our method makes use of the ensemble-smoother with multiple data assimilation (ES-MDA) combined with analytical Gaussian anamorphosis to assimilate time series of MODIS and Sentinel-2 fSCA retrievals. The scheme is applied to high-Arctic sites near Ny Ålesund (79° N, Svalbard, Norway) where in-situ observations of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak subgrid SWE distributions on most of the occasions considered. Through the ES-MDA assimilation, the root mean squared error (RMSE) for the fSCA, peak mean SWE and subgrid coefficient of variation is improved by around 75 %, 60 % and 20 % respectively when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13 respectively. By comparing the performance of the ES-MDA to that of other ensemble-based batch smoother schemes, it was found that the ES-MDA either outperforms or at least nearly matches the performance of the other schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

Citation: Aalstad, K., Westermann, S., Schuler, T. V., Boike, J., and Bertino, L.: Ensemble-based assimilation of fractional snow covered area satellite retrievals to estimate snow distribution at a high Arctic site, The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-109, in review, 2017.
Kristoffer Aalstad et al.
Kristoffer Aalstad et al.
Kristoffer Aalstad et al.

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Short summary
Our work exemplifies the use of snow cover remote sensing data from satellites to constrain estimates of snow distributions at a high Arctic site. In this effort, we make use of ensemble-based data assimilation techniques to ingest the snow cover data into a simple snow model. By comparing our estimates to independently observed snow distributions it was found that our method performed favorably and can match and even exceed the performance of previously published methods at our study site.
Our work exemplifies the use of snow cover remote sensing data from satellites to constrain...
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