Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth

Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) mo...
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Gespeichert in:

Autor*in:	N Helbig [verfasserIn] A van Herwijnen

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography

Übergeordnetes Werk:	Enthalten in: Water resources research - Hoboken, NJ : Wiley, 1965, 53(2017), 2, Seite 1444
Übergeordnetes Werk:	volume:53 ; year:2017 ; number:2 ; pages:1444

Links:	Volltext Link aufrufen

DOI / URN:	10.1002/2016WR019872

Katalog-ID:	OLC199227424X

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520			\|a Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks.
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allfields	10.1002/2016WR019872 doi PQ20170721 (DE-627)OLC199227424X (DE-599)GBVOLC199227424X (PRQ)proquest_journals_18950778060 (KEY)0046260820170000053000201444subgridparameterizationforsnowdepthovermountainous DE-627 ger DE-627 rakwb eng 550 DE-600 38.85 bkl N Helbig verfasserin aut Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography A van Herwijnen oth Enthalten in Water resources research Hoboken, NJ : Wiley, 1965 53(2017), 2, Seite 1444 (DE-627)129088285 (DE-600)5564-5 (DE-576)014422980 0043-1397 nnns volume:53 year:2017 number:2 pages:1444 http://dx.doi.org/10.1002/2016WR019872 Volltext https://search.proquest.com/docview/1895077806 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_4219 38.85 AVZ AR 53 2017 2 1444
spelling	10.1002/2016WR019872 doi PQ20170721 (DE-627)OLC199227424X (DE-599)GBVOLC199227424X (PRQ)proquest_journals_18950778060 (KEY)0046260820170000053000201444subgridparameterizationforsnowdepthovermountainous DE-627 ger DE-627 rakwb eng 550 DE-600 38.85 bkl N Helbig verfasserin aut Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography A van Herwijnen oth Enthalten in Water resources research Hoboken, NJ : Wiley, 1965 53(2017), 2, Seite 1444 (DE-627)129088285 (DE-600)5564-5 (DE-576)014422980 0043-1397 nnns volume:53 year:2017 number:2 pages:1444 http://dx.doi.org/10.1002/2016WR019872 Volltext https://search.proquest.com/docview/1895077806 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_4219 38.85 AVZ AR 53 2017 2 1444
allfields_unstemmed	10.1002/2016WR019872 doi PQ20170721 (DE-627)OLC199227424X (DE-599)GBVOLC199227424X (PRQ)proquest_journals_18950778060 (KEY)0046260820170000053000201444subgridparameterizationforsnowdepthovermountainous DE-627 ger DE-627 rakwb eng 550 DE-600 38.85 bkl N Helbig verfasserin aut Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography A van Herwijnen oth Enthalten in Water resources research Hoboken, NJ : Wiley, 1965 53(2017), 2, Seite 1444 (DE-627)129088285 (DE-600)5564-5 (DE-576)014422980 0043-1397 nnns volume:53 year:2017 number:2 pages:1444 http://dx.doi.org/10.1002/2016WR019872 Volltext https://search.proquest.com/docview/1895077806 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_4219 38.85 AVZ AR 53 2017 2 1444
allfieldsGer	10.1002/2016WR019872 doi PQ20170721 (DE-627)OLC199227424X (DE-599)GBVOLC199227424X (PRQ)proquest_journals_18950778060 (KEY)0046260820170000053000201444subgridparameterizationforsnowdepthovermountainous DE-627 ger DE-627 rakwb eng 550 DE-600 38.85 bkl N Helbig verfasserin aut Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography A van Herwijnen oth Enthalten in Water resources research Hoboken, NJ : Wiley, 1965 53(2017), 2, Seite 1444 (DE-627)129088285 (DE-600)5564-5 (DE-576)014422980 0043-1397 nnns volume:53 year:2017 number:2 pages:1444 http://dx.doi.org/10.1002/2016WR019872 Volltext https://search.proquest.com/docview/1895077806 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_4219 38.85 AVZ AR 53 2017 2 1444
allfieldsSound	10.1002/2016WR019872 doi PQ20170721 (DE-627)OLC199227424X (DE-599)GBVOLC199227424X (PRQ)proquest_journals_18950778060 (KEY)0046260820170000053000201444subgridparameterizationforsnowdepthovermountainous DE-627 ger DE-627 rakwb eng 550 DE-600 38.85 bkl N Helbig verfasserin aut Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks. Climate models Snow Snow cover depth Climatic regions Snow depth Lapse rates Elk Topography A van Herwijnen oth Enthalten in Water resources research Hoboken, NJ : Wiley, 1965 53(2017), 2, Seite 1444 (DE-627)129088285 (DE-600)5564-5 (DE-576)014422980 0043-1397 nnns volume:53 year:2017 number:2 pages:1444 http://dx.doi.org/10.1002/2016WR019872 Volltext https://search.proquest.com/docview/1895077806 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO GBV_ILN_4219 38.85 AVZ AR 53 2017 2 1444
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author	N Helbig
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title	Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth
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title_sort	subgrid parameterization for snow depth over mountainous terrain from flat field snow depth
title_auth	Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth
abstract	Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks.
abstractGer	Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks.
abstract_unstemmed	Snow depth is an important variable for a variety of models including land-surface, meteorological, and climate models. Various measurement networks were therefore developed to measure snow depth on the ground. Measurement stations are generally located in gentle terrain (flat field measurements) most often at lower or mid elevation. While these sites have provided a wealth of information, various studies have questioned the representativity of such flat field measurements for the surrounding topography, especially in alpine regions. Using highly resolved snow depth maps at the peak of winter from two distinct climatic regions in Switzerland and in the Spanish Pyrenees, we developed two parameterizations to estimate domain-averaged snow depth in coarse-scale model applications over complex topography using easy to derive topographic parameters. The first parameterization uses a commonly applied linear lapse rate. Removing the dominant elevation gradient in mean snow depth revealed remaining underlying correlations with other topographic parameters, in particular the sky view factor. The second parameterization combines a power law elevation trend scaled with the subgrid parameterized sky view factor. Using a variety of statistic measures showed that the more complex parameterization performs better when using mean high-resolution flat field snow depth. The performances slightly decreased when formulating the parameterizations for a single flat field snow depth measurement. Nevertheless, the more complex parameterization still outperformed the linear lapse rate model. As the parameterization was developed independently of a specific geographic region, we suggest it could be used to assimilate flat field snow depth or snowfall into coarse-scale snow model frameworks.
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title_short	Subgrid parameterization for snow depth over mountainous terrain from flat field snow depth
url	http://dx.doi.org/10.1002/2016WR019872 https://search.proquest.com/docview/1895077806
remote_bool	false
author2	A van Herwijnen
author2Str	A van Herwijnen
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doi_str	10.1002/2016WR019872
up_date	2024-07-04T04:37:50.453Z
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