Identification and interpretation of nonnormality in atmospheric time series
Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time serie...
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| Veröffentlicht in: | Geophysical research letters 2016-05, Vol.43 (10), p.5425-5434 |
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| container_title | Geophysical research letters |
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| creator | Proistosescu, Cristian Rhines, Andrew Huybers, Peter |
| description | Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.
Key Points
Atmospheric temperature variability is nonnormal on daily time scales
Apparent normal variability on synoptic scales is an artifact of filtering
Generating mechanism for nonnormality does not necessarily act at frequency where greatest nonnormality is observed |
| doi_str_mv | 10.1002/2016GL068880 |
| format | Article |
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Key Points
Atmospheric temperature variability is nonnormal on daily time scales
Apparent normal variability on synoptic scales is an artifact of filtering
Generating mechanism for nonnormality does not necessarily act at frequency where greatest nonnormality is observed</description><identifier>ISSN: 0094-8276</identifier><identifier>EISSN: 1944-8007</identifier><identifier>DOI: 10.1002/2016GL068880</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Annual variations ; Atmospherics ; Autoregressive models ; Autoregressive processes ; Determinants ; Dynamical systems ; Dynamics ; Filtering ; Filtration ; Geophysics ; High frequencies ; Identification ; Kurtosis ; Noise prediction ; nonnormality ; non‐Gaussianity ; Radiosonde temperature ; Radiosondes ; Skewness ; spectral analysis ; Statistical analysis ; Temperature ; temperature distributions ; Temperature effects ; Temperature variations ; Time series ; Variability ; Winter ; Winter temperatures</subject><ispartof>Geophysical research letters, 2016-05, Vol.43 (10), p.5425-5434</ispartof><rights>2016. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4389-a288f1a7d18bee194fcc80320b4bba44eb141a53ad0b672f8b91e7d300d8ed853</citedby><cites>FETCH-LOGICAL-c4389-a288f1a7d18bee194fcc80320b4bba44eb141a53ad0b672f8b91e7d300d8ed853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2016GL068880$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2016GL068880$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,11514,27924,27925,45574,45575,46409,46468,46833,46892</link.rule.ids></links><search><creatorcontrib>Proistosescu, Cristian</creatorcontrib><creatorcontrib>Rhines, Andrew</creatorcontrib><creatorcontrib>Huybers, Peter</creatorcontrib><title>Identification and interpretation of nonnormality in atmospheric time series</title><title>Geophysical research letters</title><description>Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.
Key Points
Atmospheric temperature variability is nonnormal on daily time scales
Apparent normal variability on synoptic scales is an artifact of filtering
Generating mechanism for nonnormality does not necessarily act at frequency where greatest nonnormality is observed</description><subject>Annual variations</subject><subject>Atmospherics</subject><subject>Autoregressive models</subject><subject>Autoregressive processes</subject><subject>Determinants</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>Filtering</subject><subject>Filtration</subject><subject>Geophysics</subject><subject>High frequencies</subject><subject>Identification</subject><subject>Kurtosis</subject><subject>Noise prediction</subject><subject>nonnormality</subject><subject>non‐Gaussianity</subject><subject>Radiosonde temperature</subject><subject>Radiosondes</subject><subject>Skewness</subject><subject>spectral analysis</subject><subject>Statistical analysis</subject><subject>Temperature</subject><subject>temperature distributions</subject><subject>Temperature effects</subject><subject>Temperature variations</subject><subject>Time series</subject><subject>Variability</subject><subject>Winter</subject><subject>Winter temperatures</subject><issn>0094-8276</issn><issn>1944-8007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqF0cFq3DAQBmBRUuhmm1sewJBLD9l2RpLt0bEsyTZgKJT2bGR7TLXY0kbyEvbto7I5hBzS0wyjDzE_I8Q1wlcEkN8kYLVroCIi-CBWaLTeEEB9IVYAJveyrj6Jy5T2AKBA4Uo0DwP7xY2ut4sLvrB-KJxfOB4iL-dRGAsfvA9xtpNbTvm5sMsc0uEvR9cXi5u5SLnl9Fl8HO2U-OqlrsWf-7vf2x-b5ufuYfu92fRakdlYSTSirQekjjlvOfY9gZLQ6a6zWnOHGm2p7ABdVcuROoNcDwpgIB6oVGvx5fzvIYbHI6elnV3qeZqs53BMLZIsyxw4h_w_BapBU6kzvXlD9-EYfQ7SokGUqAy-r2pT65LIyKxuz6qPIaXIY3uIbrbx1CK0_27Vvr5V5vLMn9zEp3dtu_vV5GUro54BJwWTrQ</recordid><startdate>20160528</startdate><enddate>20160528</enddate><creator>Proistosescu, Cristian</creator><creator>Rhines, Andrew</creator><creator>Huybers, Peter</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope></search><sort><creationdate>20160528</creationdate><title>Identification and interpretation of nonnormality in atmospheric time series</title><author>Proistosescu, Cristian ; Rhines, Andrew ; Huybers, Peter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4389-a288f1a7d18bee194fcc80320b4bba44eb141a53ad0b672f8b91e7d300d8ed853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Annual variations</topic><topic>Atmospherics</topic><topic>Autoregressive models</topic><topic>Autoregressive processes</topic><topic>Determinants</topic><topic>Dynamical systems</topic><topic>Dynamics</topic><topic>Filtering</topic><topic>Filtration</topic><topic>Geophysics</topic><topic>High frequencies</topic><topic>Identification</topic><topic>Kurtosis</topic><topic>Noise prediction</topic><topic>nonnormality</topic><topic>non‐Gaussianity</topic><topic>Radiosonde temperature</topic><topic>Radiosondes</topic><topic>Skewness</topic><topic>spectral analysis</topic><topic>Statistical analysis</topic><topic>Temperature</topic><topic>temperature distributions</topic><topic>Temperature effects</topic><topic>Temperature variations</topic><topic>Time series</topic><topic>Variability</topic><topic>Winter</topic><topic>Winter temperatures</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Proistosescu, Cristian</creatorcontrib><creatorcontrib>Rhines, Andrew</creatorcontrib><creatorcontrib>Huybers, Peter</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Geophysical research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Proistosescu, Cristian</au><au>Rhines, Andrew</au><au>Huybers, Peter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification and interpretation of nonnormality in atmospheric time series</atitle><jtitle>Geophysical research letters</jtitle><date>2016-05-28</date><risdate>2016</risdate><volume>43</volume><issue>10</issue><spage>5425</spage><epage>5434</epage><pages>5425-5434</pages><issn>0094-8276</issn><eissn>1944-8007</eissn><abstract>Nonnormal characteristics of geophysical time series are important determinants of extreme events and may provide insight into the underlying dynamics of a system. The structure of nonnormality in winter temperature is examined through the use of linear filtering of radiosonde temperature time series. Filtering either low or high frequencies generally suppresses what is otherwise statistically significant nonnormal variability in temperature. The structure of nonnormality is partly attributable to geometric relations between filtering and the appearance of skewness, kurtosis, and higher order moments in time series data, and partly attributable to the presence of nonnormal temperature variations at the highest resolved frequencies in the presence of atmospheric memory. A nonnormal autoregressive model and a multiplicative noise model are both consistent with the observed frequency structure of nonnormality. These results suggest that the generating mechanism for nonnormal variations does not necessarily act at the frequencies at which greatest nonnormality is observed.
Key Points
Atmospheric temperature variability is nonnormal on daily time scales
Apparent normal variability on synoptic scales is an artifact of filtering
Generating mechanism for nonnormality does not necessarily act at frequency where greatest nonnormality is observed</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/2016GL068880</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library Journals; Free E-Journal (出版社公開部分のみ）; Wiley Online Library Free Content; Wiley Online Library AGU Free Content |
| subjects | Annual variations Atmospherics Autoregressive models Autoregressive processes Determinants Dynamical systems Dynamics Filtering Filtration Geophysics High frequencies Identification Kurtosis Noise prediction nonnormality non‐Gaussianity Radiosonde temperature Radiosondes Skewness spectral analysis Statistical analysis Temperature temperature distributions Temperature effects Temperature variations Time series Variability Winter Winter temperatures |
| title | Identification and interpretation of nonnormality in atmospheric time series |
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