Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis
[1] Weekly cycles in several meteorological parameters have been previously reported. Yet the extent to which these cycles are caused by anthropogenic activity remains unclear. Some of the complications associated with establishing this link are discussed here. Specifically, we highlight and quantif...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Geophysical Union (AGU)
2013
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| Online Access: | http://hdl.handle.net/1721.1/76272 https://orcid.org/0000-0002-2020-7581 |
| _version_ | 1826214177608302592 |
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| author | Daniel, J. S. Portmann, R. W. Solomon, Susan Murphy, Daniel M. |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Daniel, J. S. Portmann, R. W. Solomon, Susan Murphy, Daniel M. |
| author_sort | Daniel, J. S. |
| collection | MIT |
| description | [1] Weekly cycles in several meteorological parameters have been previously reported. Yet the extent to which these cycles are caused by anthropogenic activity remains unclear. Some of the complications associated with establishing this link are discussed here. Specifically, we highlight and quantify some common errors that have been made in the application of statistical techniques to this problem. Some errors, including the inappropriate use of the Student ttest, have been significant enough to affect the conclusions of previous studies. A resampling technique that can properly account for both temporal and spatial correlation is evaluated and is shown to be accurate for determining the statistical significance of weekly cycles at the station level and for evaluating total field significance. We demonstrate that this resampling approach performs comparably to a Fourier analysis that evaluates the significance of the power at a seven-day period. Regardless of the analysis technique used, an understanding of the behavior of and uncertainties associated with the statistical analysis is critical to arriving at a justifiable conclusion regarding a human influence on weekly cycles and for putting results in context with other studies. We also discuss some general errors that can be made in weekly cycle analysis. These include selection of an analysis region after identifying where weekly cycles are significant, acceptance of a physical explanation for the hypothesized link that has not been properly tested given its large number of degrees of freedom, and ignoring the correlation among meteorological parameters. |
| first_indexed | 2024-09-23T16:01:19Z |
| format | Article |
| id | mit-1721.1/76272 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:01:19Z |
| publishDate | 2013 |
| publisher | American Geophysical Union (AGU) |
| record_format | dspace |
| spelling | mit-1721.1/762722022-10-02T05:43:48Z Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis Daniel, J. S. Portmann, R. W. Solomon, Susan Murphy, Daniel M. Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Solomon, Susan Solomon, Susan [1] Weekly cycles in several meteorological parameters have been previously reported. Yet the extent to which these cycles are caused by anthropogenic activity remains unclear. Some of the complications associated with establishing this link are discussed here. Specifically, we highlight and quantify some common errors that have been made in the application of statistical techniques to this problem. Some errors, including the inappropriate use of the Student ttest, have been significant enough to affect the conclusions of previous studies. A resampling technique that can properly account for both temporal and spatial correlation is evaluated and is shown to be accurate for determining the statistical significance of weekly cycles at the station level and for evaluating total field significance. We demonstrate that this resampling approach performs comparably to a Fourier analysis that evaluates the significance of the power at a seven-day period. Regardless of the analysis technique used, an understanding of the behavior of and uncertainties associated with the statistical analysis is critical to arriving at a justifiable conclusion regarding a human influence on weekly cycles and for putting results in context with other studies. We also discuss some general errors that can be made in weekly cycle analysis. These include selection of an analysis region after identifying where weekly cycles are significant, acceptance of a physical explanation for the hypothesized link that has not been properly tested given its large number of degrees of freedom, and ignoring the correlation among meteorological parameters. 2013-01-16T22:01:28Z 2013-01-16T22:01:28Z 2012-07 2012-04 Article http://purl.org/eprint/type/JournalArticle 0148-0227 2156–2202 http://hdl.handle.net/1721.1/76272 Daniel, J. S. et al. “Identifying Weekly Cycles in Meteorological Variables: The Importance of an Appropriate Statistical Analysis.” Journal of Geophysical Research 117.D13 (2012). https://orcid.org/0000-0002-2020-7581 en_US http://dx.doi.org/10.1029/2012jd017574 Journal of Geophysical Research Atmospheres Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Geophysical Union (AGU) Prof. Solomon via Chris Sherratt |
| spellingShingle | Daniel, J. S. Portmann, R. W. Solomon, Susan Murphy, Daniel M. Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title | Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title_full | Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title_fullStr | Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title_full_unstemmed | Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title_short | Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis |
| title_sort | identifying weekly cycles in meteorological variables the importance of an appropriate statistical analysis |
| url | http://hdl.handle.net/1721.1/76272 https://orcid.org/0000-0002-2020-7581 |
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