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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Main Authors: Malnes, Eirik, Vickers, Hannah, Karlsen, Stein Rune, Saloranta, Tuomo, Killie, Mari Anne, Van Pelt, Ward, Zhang, Jie, Stendardi, Laura, Notarnicola, Claudia
Format: Report
Language:English
Published: Svalbard Integrated Arctic Earth Observing System 2021
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.4294071
https://zenodo.org/doi/10.5281/zenodo.4294071
id ftdatacite:10.5281/zenodo.4294071
record_format openpolar
spelling 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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