Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ...
This is chapter 8 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). We document differences and similarities between three satellite-based and three model-based snow cover datasets, showing the geographical distribution and amount of snow a...
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Svalbard Integrated Arctic Earth Observing System
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ftdatacite:10.5281/zenodo.4294071 2024-01-28T10:09:27+01:00 Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... Malnes, Eirik Vickers, Hannah Karlsen, Stein Rune Saloranta, Tuomo Killie, Mari Anne Van Pelt, Ward Zhang, Jie Stendardi, Laura Notarnicola, Claudia 2021 https://dx.doi.org/10.5281/zenodo.4294071 https://zenodo.org/doi/10.5281/zenodo.4294071 en eng Svalbard Integrated Arctic Earth Observing System https://dx.doi.org/10.5281/zenodo.4294072 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Snow remote sensing modelling snow cover snow water equivalent Report report 2021 ftdatacite https://doi.org/10.5281/zenodo.429407110.5281/zenodo.4294072 2024-01-04T15:12:18Z This is chapter 8 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). We document differences and similarities between three satellite-based and three model-based snow cover datasets, showing the geographical distribution and amount of snow across Svalbard for several periods from 1957 to 2020. The study shows that the datasets have many differences and that work needs to be done to accurately represent the snow cover in Svalbard. Low resolution datasets tend to predict longer winters than higher resolution datasets. We studied differences between the datasets and suggest methods to improve each dataset. Satellite data have been available since 1978, but early sensors had low resolution, and can only provide correct information over larger areas. Current sensors, available since 2016, have high resolution. Older low-resolution data may be improved by utilising overlapping time-series of high- and low-resolution data since local snow distribution ... Report Svalbard DataCite Metadata Store (German National Library of Science and Technology) Svalbard |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Snow remote sensing modelling snow cover snow water equivalent |
spellingShingle |
Snow remote sensing modelling snow cover snow water equivalent Malnes, Eirik Vickers, Hannah Karlsen, Stein Rune Saloranta, Tuomo Killie, Mari Anne Van Pelt, Ward Zhang, Jie Stendardi, Laura Notarnicola, Claudia Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
topic_facet |
Snow remote sensing modelling snow cover snow water equivalent |
description |
This is chapter 8 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). We document differences and similarities between three satellite-based and three model-based snow cover datasets, showing the geographical distribution and amount of snow across Svalbard for several periods from 1957 to 2020. The study shows that the datasets have many differences and that work needs to be done to accurately represent the snow cover in Svalbard. Low resolution datasets tend to predict longer winters than higher resolution datasets. We studied differences between the datasets and suggest methods to improve each dataset. Satellite data have been available since 1978, but early sensors had low resolution, and can only provide correct information over larger areas. Current sensors, available since 2016, have high resolution. Older low-resolution data may be improved by utilising overlapping time-series of high- and low-resolution data since local snow distribution ... |
format |
Report |
author |
Malnes, Eirik Vickers, Hannah Karlsen, Stein Rune Saloranta, Tuomo Killie, Mari Anne Van Pelt, Ward Zhang, Jie Stendardi, Laura Notarnicola, Claudia |
author_facet |
Malnes, Eirik Vickers, Hannah Karlsen, Stein Rune Saloranta, Tuomo Killie, Mari Anne Van Pelt, Ward Zhang, Jie Stendardi, Laura Notarnicola, Claudia |
author_sort |
Malnes, Eirik |
title |
Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
title_short |
Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
title_full |
Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
title_fullStr |
Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
title_full_unstemmed |
Satellite and modelling based snow season time series for Svalbard: Inter-comparisons and assessment of accuracy (SATMODSNOW) ... |
title_sort |
satellite and modelling based snow season time series for svalbard: inter-comparisons and assessment of accuracy (satmodsnow) ... |
publisher |
Svalbard Integrated Arctic Earth Observing System |
publishDate |
2021 |
url |
https://dx.doi.org/10.5281/zenodo.4294071 https://zenodo.org/doi/10.5281/zenodo.4294071 |
geographic |
Svalbard |
geographic_facet |
Svalbard |
genre |
Svalbard |
genre_facet |
Svalbard |
op_relation |
https://dx.doi.org/10.5281/zenodo.4294072 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.429407110.5281/zenodo.4294072 |
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1789339382336978944 |