With its high albedo, low thermal conductivity and large waterstoring capacity, snow strongly modulates the surface energy and waterbalance, which makes it a critical factor in mid- to high-latitude and mountainenvironments. However, estimating the snow water equivalent (SWE) ischallenging in remote-sensing applications already at medium spatialresolutions of 1 km. We present an ensemble-based data assimilationframework that estimates the peak subgrid SWE distribution (SSD) at the1 km scale by assimilating fractional snow-covered area (fSCA)satellite retrievals in a simple snow model forced by downscaled reanalysisdata. The basic idea is to relate the timing of the snow cover depletion(accessible from satellite products) to the peak SSD. Peak subgrid SWE isassumed to be lognormally distributed, which can be translated to a modeledtime series of fSCA through the snow model. Assimilation of satellite-derivedfSCA facilitates the estimation of the peak SSD, while taking into accountuncertainties in both the model and the assimilated data sets. As anextension to previous studies, our method makes use of the novel (to snowdata assimilation) ensemble smoother with multiple data assimilation (ES-MDA)scheme combined with analytical Gaussian anamorphosis to assimilate timeseries of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied toArctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where fieldmeasurements of fSCA and SWE distributions are available. The method is ableto successfully recover accurate estimates of peak SSD on most of theoccasions considered. Through the ES-MDA assimilation, the root-mean-squareerror (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient ofvariation is improved by around 75, 60 and 20 %, respectively, whencompared to the prior, yielding RMSEs of 0.01, 0.09 m waterequivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or atleast nearly matches the performance of other ensemble-based batch smootherschemes with regards to various evaluation metrics. Given the modularity ofthe method, it could prove valuable for a range of satellite-erahydrometeorological reanalyses.
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