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dc.contributor.authorBroxton, Patrick D.
dc.contributor.authorZeng, Xubin
dc.contributor.authorDawson, Nicholas
dc.date.accessioned2017-02-08T19:46:49Z
dc.date.available2017-02-08T19:46:49Z
dc.date.issued2016-11
dc.identifier.citationWhy Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent? 2016, 17 (11):2743 Journal of Hydrometeorologyen
dc.identifier.issn1525-755X
dc.identifier.issn1525-7541
dc.identifier.doi10.1175/JHM-D-16-0056.1
dc.identifier.urihttp://hdl.handle.net/10150/622474
dc.description.abstractThere is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.
dc.description.sponsorshipNASA [NNX14AM02G]; NOAA [NA13NES4400003]; NSF [AGS-0944101]en
dc.language.isoenen
dc.publisherAMER METEOROLOGICAL SOCen
dc.relation.urlhttp://journals.ametsoc.org/doi/10.1175/JHM-D-16-0056.1en
dc.rights© 2016 American Meteorological Societyen
dc.titleWhy Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?en
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalJournal of Hydrometeorologyen
dc.description.notePublished Online: 7 November 2016; 6 Month Embargo.en
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
refterms.dateFOA2017-11-09T00:00:00Z
html.description.abstractThere is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.


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