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dc.contributor.authorBroxton, Patrick D.
dc.contributor.authorDawson, Nicholas
dc.contributor.authorZeng, Xubin
dc.date.accessioned2016-11-22T22:43:07Z
dc.date.available2016-11-22T22:43:07Z
dc.date.issued2016-06
dc.identifier.citationLinking snowfall and snow accumulation to generate spatial maps of SWE and snow depth 2016, 3 (6):246 Earth and Space Scienceen
dc.identifier.issn23335084
dc.identifier.doi10.1002/2016EA000174
dc.identifier.urihttp://hdl.handle.net/10150/621410
dc.description.abstractIt is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.
dc.language.isoenen
dc.publisherAMER GEOPHYSICAL UNIONen
dc.relation.urlhttp://doi.wiley.com/10.1002/2016EA000174en
dc.rights© 2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleLinking snowfall and snow accumulation to generate spatial maps of SWE and snow depthen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Atmospher Scien
dc.identifier.journalEarth and Space Scienceen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionDepartment of Atmospheric Sciences; University of Arizona; Tucson Arizona USA
dc.contributor.institutionDepartment of Atmospheric Sciences; University of Arizona; Tucson Arizona USA
dc.contributor.institutionDepartment of Atmospheric Sciences; University of Arizona; Tucson Arizona USA
refterms.dateFOA2018-06-11T13:35:45Z
html.description.abstractIt is critically important but challenging to estimate the amount of snow on the ground over large areas due to its strong spatial variability. Point snow data are used to generate or improve (i.e., blend with) gridded estimates of snow water equivalent (SWE) by using various forms of interpolation; however, the interpolation methodologies often overlook the physical mechanisms for the snow being there in the first place. Using data from the Snow Telemetry and Cooperative Observer networks in the western United States, we show that four methods for the spatial interpolation of peak of winter snow water equivalent (SWE) and snow depth based on distance and elevation can result in large errors. These errors are reduced substantially by our new method, i.e., the spatial interpolation of these quantities normalized by accumulated snowfall from the current or previous water years. Our method results in significant improvement in SWE estimates over interpolation techniques that do not consider snowfall, regardless of the number of stations used for the interpolation. Furthermore, it can be used along with gridded precipitation and temperature data to produce daily maps of SWE over the western United States that are comparable to existing estimates (which are based on the assimilation of much more data). Our results also show that not honoring the constraint between SWE and snowfall when blending in situ data with gridded data can lead to the development and propagation of unrealistic errors.


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© 2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.
Except where otherwise noted, this item's license is described as © 2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License.