A Statistical Analysis of Daily Snow Depth Trends in North America

Woody, J.; Xu, Y.; Dyer, J.; Lund, R; Hewaarachchi, A.P.

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