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A Statistical Analysis of Daily Snow Depth Trends in North America

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dc.contributor.author Woody, J.
dc.contributor.author Xu, Y.
dc.contributor.author Dyer, J.
dc.contributor.author Lund, R
dc.contributor.author Hewaarachchi, A.P.
dc.date.accessioned 2021-08-05T05:06:14Z
dc.date.available 2021-08-05T05:06:14Z
dc.date.issued 2021
dc.identifier.citation Woody, J.; Xu, Y.; Dyer, J.; Lund, R.; Hewaarachchi, A.P. A Statistical Analysis of Daily Snow Depth Trends in North America. Atmosphere 2021, 12, 820. https:// doi.org/10.3390/atmos12070820 en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/23148
dc.description.abstract Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America. en_US
dc.publisher Atmosphere en_US
dc.subject changepoints, genetic algorithms, snow trends, storage model, time series en_US
dc.title A Statistical Analysis of Daily Snow Depth Trends in North America en_US


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