A Statistical Analysis of Daily Snow Depth Trends in North America
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 (changepoint...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/7/820 |
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| author | Jonathan Woody Yang Xu Jamie Dyer Robert Lund Anuradha P. Hewaarachchi |
| author_facet | Jonathan Woody Yang Xu Jamie Dyer Robert Lund Anuradha P. Hewaarachchi |
| author_sort | Jonathan Woody |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:00:18Z |
| format | Article |
| id | doaj.art-0f2ccb2608e1412fb90d62ce437e6450 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T10:00:18Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj.art-0f2ccb2608e1412fb90d62ce437e64502023-11-22T01:55:45ZengMDPI AGAtmosphere2073-44332021-06-0112782010.3390/atmos12070820A Statistical Analysis of Daily Snow Depth Trends in North AmericaJonathan Woody0Yang Xu1Jamie Dyer2Robert Lund3Anuradha P. Hewaarachchi4Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762, USADepartment of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762, USADepartment of Geosciences, Mississippi State University, Mississippi State, MS 39762, USADepartment of Statistics, University of California, Santa Cruz, CA 95064, USADepartment of Statistics and Computer Science, University of Kelaniya, Kelaniya 11600, Sri LankaSeveral 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.https://www.mdpi.com/2073-4433/12/7/820changepointsgenetic algorithmssnow trendsstorage modeltime series |
| spellingShingle | Jonathan Woody Yang Xu Jamie Dyer Robert Lund Anuradha P. Hewaarachchi A Statistical Analysis of Daily Snow Depth Trends in North America Atmosphere changepoints genetic algorithms snow trends storage model time series |
| title | A Statistical Analysis of Daily Snow Depth Trends in North America |
| title_full | A Statistical Analysis of Daily Snow Depth Trends in North America |
| title_fullStr | A Statistical Analysis of Daily Snow Depth Trends in North America |
| title_full_unstemmed | A Statistical Analysis of Daily Snow Depth Trends in North America |
| title_short | A Statistical Analysis of Daily Snow Depth Trends in North America |
| title_sort | statistical analysis of daily snow depth trends in north america |
| topic | changepoints genetic algorithms snow trends storage model time series |
| url | https://www.mdpi.com/2073-4433/12/7/820 |
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