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 en- vironments. However, estimating the snow water equiva- lent (SWE) is challenging i...
Published in: | The Cryosphere |
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Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
National Snow and Ice Data Center
2018
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Subjects: | |
Online Access: | http://hdl.handle.net/10852/71167 http://urn.nb.no/URN:NBN:no-74283 https://doi.org/10.5194/tc-12-247-2018 |
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ftoslouniv:oai:www.duo.uio.no:10852/71167 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
Universitet i Oslo: Digitale utgivelser ved UiO (DUO) |
op_collection_id |
ftoslouniv |
language |
English |
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 en- vironments. However, estimating the snow water equiva- lent (SWE) is challenging in remote-sensing applications al- ready at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satel- lite 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 log- normally 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 previ- ous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimi- lation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Reso- lution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79◦ N, Svalbard, Norway) where field measure- ments 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%, re- spectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother 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. Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
format |
Article in Journal/Newspaper |
author |
Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent |
spellingShingle |
Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites |
author_facet |
Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent |
author_sort |
Aalstad, Kristoffer |
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 |
National Snow and Ice Data Center |
publishDate |
2018 |
url |
http://hdl.handle.net/10852/71167 http://urn.nb.no/URN:NBN:no-74283 https://doi.org/10.5194/tc-12-247-2018 |
long_lat |
ENVELOPE(-66.783,-66.783,-66.867,-66.867) |
geographic |
Arctic Lent Norway Svalbard |
geographic_facet |
Arctic Lent Norway Svalbard |
genre |
albedo Arctic Arctic Svalbard The Cryosphere |
genre_facet |
albedo Arctic Arctic Svalbard The Cryosphere |
op_source |
1994-0416 |
op_relation |
Aalstad, Kristoffer (2019) Ensemble-based retrospective analysis of the seasonal snowpack. Doctoral thesis http://urn.nb.no/URN:NBN:no-74865 http://urn.nb.no/URN:NBN:no-74865 NFR/239918 NORDFORSK/56801 http://urn.nb.no/URN:NBN:no-74283 Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent . Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere. 2018, 12(1), 247-270 http://hdl.handle.net/10852/71167 1549731 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=The Cryosphere&rft.volume=12&rft.spage=247&rft.date=2018 The Cryosphere 12 1 247 270 https://doi.org/10.5194/tc-12-247-2018 URN:NBN:no-74283 Fulltext https://www.duo.uio.no/bitstream/handle/10852/71167/2/tc-12-247-2018.pdf |
op_rights |
Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
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 |
_version_ |
1766249080571297792 |
spelling |
ftoslouniv:oai:www.duo.uio.no:10852/71167 2023-05-15T13:11:49+02:00 Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent 2018-01-23T10:54:27Z http://hdl.handle.net/10852/71167 http://urn.nb.no/URN:NBN:no-74283 https://doi.org/10.5194/tc-12-247-2018 EN eng National Snow and Ice Data Center Aalstad, Kristoffer (2019) Ensemble-based retrospective analysis of the seasonal snowpack. Doctoral thesis http://urn.nb.no/URN:NBN:no-74865 http://urn.nb.no/URN:NBN:no-74865 NFR/239918 NORDFORSK/56801 http://urn.nb.no/URN:NBN:no-74283 Aalstad, Kristoffer Westermann, Sebastian Schuler, Thomas Boike, Julia Bertino, Laurent . Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites. The Cryosphere. 2018, 12(1), 247-270 http://hdl.handle.net/10852/71167 1549731 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=The Cryosphere&rft.volume=12&rft.spage=247&rft.date=2018 The Cryosphere 12 1 247 270 https://doi.org/10.5194/tc-12-247-2018 URN:NBN:no-74283 Fulltext https://www.duo.uio.no/bitstream/handle/10852/71167/2/tc-12-247-2018.pdf Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ CC-BY 1994-0416 Journal article Tidsskriftartikkel Peer reviewed PublishedVersion 2018 ftoslouniv https://doi.org/10.5194/tc-12-247-2018 2020-06-21T08:52:52Z 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 en- vironments. However, estimating the snow water equiva- lent (SWE) is challenging in remote-sensing applications al- ready at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satel- lite 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 log- normally 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 previ- ous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimi- lation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Reso- lution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79◦ N, Svalbard, Norway) where field measure- ments 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%, re- spectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother 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. Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites Article in Journal/Newspaper albedo Arctic Arctic Svalbard The Cryosphere Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Arctic Lent ENVELOPE(-66.783,-66.783,-66.867,-66.867) Norway Svalbard The Cryosphere 12 1 247 270 |