Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites
With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in re...
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ftdoajarticles:oai:doaj.org/article:070f448d2152441d8986cd23660ed7ed 2023-05-15T13:11:52+02:00 Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites K. Aalstad S. Westermann T. V. Schuler J. Boike L. Bertino 2018-01-01T00:00:00Z https://doi.org/10.5194/tc-12-247-2018 https://doaj.org/article/070f448d2152441d8986cd23660ed7ed EN eng Copernicus Publications https://www.the-cryosphere.net/12/247/2018/tc-12-247-2018.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-12-247-2018 1994-0416 1994-0424 https://doaj.org/article/070f448d2152441d8986cd23660ed7ed The Cryosphere, Vol 12, Pp 247-270 (2018) Environmental sciences GE1-350 Geology QE1-996.5 article 2018 ftdoajarticles https://doi.org/10.5194/tc-12-247-2018 2022-12-30T21:03:14Z With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak 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. The ES-MDA either outperforms or at least nearly matches the performance of other ... Article in Journal/Newspaper albedo Arctic Ny Ålesund Ny-Ålesund Svalbard The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic Svalbard Ny-Ålesund Norway The Cryosphere 12 1 247 270 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 K. Aalstad S. Westermann T. V. Schuler J. Boike L. Bertino Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak 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. The ES-MDA either outperforms or at least nearly matches the performance of other ... |
format |
Article in Journal/Newspaper |
author |
K. Aalstad S. Westermann T. V. Schuler J. Boike L. Bertino |
author_facet |
K. Aalstad S. Westermann T. V. Schuler J. Boike L. Bertino |
author_sort |
K. Aalstad |
title |
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
title_short |
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
title_full |
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
title_fullStr |
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
title_full_unstemmed |
Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
title_sort |
ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at arctic sites |
publisher |
Copernicus Publications |
publishDate |
2018 |
url |
https://doi.org/10.5194/tc-12-247-2018 https://doaj.org/article/070f448d2152441d8986cd23660ed7ed |
geographic |
Arctic Svalbard Ny-Ålesund Norway |
geographic_facet |
Arctic Svalbard Ny-Ålesund Norway |
genre |
albedo Arctic Ny Ålesund Ny-Ålesund Svalbard The Cryosphere |
genre_facet |
albedo Arctic Ny Ålesund Ny-Ålesund Svalbard The Cryosphere |
op_source |
The Cryosphere, Vol 12, Pp 247-270 (2018) |
op_relation |
https://www.the-cryosphere.net/12/247/2018/tc-12-247-2018.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-12-247-2018 1994-0416 1994-0424 https://doaj.org/article/070f448d2152441d8986cd23660ed7ed |
op_doi |
https://doi.org/10.5194/tc-12-247-2018 |
container_title |
The Cryosphere |
container_volume |
12 |
container_issue |
1 |
container_start_page |
247 |
op_container_end_page |
270 |
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1766249275664105472 |