Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model
Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE)...
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2012
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Online Access: | https://doi.org/10.5194/tc-6-1323-2012 http://www.the-cryosphere.net/6/1323/2012/tc-6-1323-2012.pdf https://doaj.org/article/06eacc5cafe94a0db254c2cb3bbe777e |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:06eacc5cafe94a0db254c2cb3bbe777e 2023-05-15T18:32:22+02:00 Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model T. M. Saloranta 2012-11-01 https://doi.org/10.5194/tc-6-1323-2012 http://www.the-cryosphere.net/6/1323/2012/tc-6-1323-2012.pdf https://doaj.org/article/06eacc5cafe94a0db254c2cb3bbe777e en eng Copernicus Publications doi:10.5194/tc-6-1323-2012 1994-0416 1994-0424 http://www.the-cryosphere.net/6/1323/2012/tc-6-1323-2012.pdf https://doaj.org/article/06eacc5cafe94a0db254c2cb3bbe777e undefined The Cryosphere, Vol 6, Iss 6, Pp 1323-1337 (2012) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2012 fttriple https://doi.org/10.5194/tc-6-1323-2012 2023-01-22T19:15:34Z Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway. Article in Journal/Newspaper The Cryosphere Unknown Norway The Cryosphere 6 6 1323 1337 |
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geo envir T. M. Saloranta Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
topic_facet |
geo envir |
description |
Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway. |
format |
Article in Journal/Newspaper |
author |
T. M. Saloranta |
author_facet |
T. M. Saloranta |
author_sort |
T. M. Saloranta |
title |
Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
title_short |
Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
title_full |
Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
title_fullStr |
Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
title_full_unstemmed |
Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model |
title_sort |
simulating snow maps for norway: description and statistical evaluation of the senorge snow model |
publisher |
Copernicus Publications |
publishDate |
2012 |
url |
https://doi.org/10.5194/tc-6-1323-2012 http://www.the-cryosphere.net/6/1323/2012/tc-6-1323-2012.pdf https://doaj.org/article/06eacc5cafe94a0db254c2cb3bbe777e |
geographic |
Norway |
geographic_facet |
Norway |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 6, Iss 6, Pp 1323-1337 (2012) |
op_relation |
doi:10.5194/tc-6-1323-2012 1994-0416 1994-0424 http://www.the-cryosphere.net/6/1323/2012/tc-6-1323-2012.pdf https://doaj.org/article/06eacc5cafe94a0db254c2cb3bbe777e |
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undefined |
op_doi |
https://doi.org/10.5194/tc-6-1323-2012 |
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The Cryosphere |
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6 |
container_issue |
6 |
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1323 |
op_container_end_page |
1337 |
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