Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability
The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To...
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| Veröffentlicht in: | Theoretical and applied climatology 2019-04, Vol.136 (1-2), p.499-512 |
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| description | The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981–2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R
2
> 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R
2
values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues. |
| doi_str_mv | 10.1007/s00704-018-2504-7 |
| format | Article |
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2
> 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R
2
values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-018-2504-7</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Air pollution ; Air temperature ; Anthropogenic factors ; Aquatic Pollution ; Asymmetry ; Atmosphere ; Atmospheric models ; Atmospheric moisture ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate science ; Climatology ; Cloud cover ; Clouds (Meteorology) ; Daily ; Daily temperatures ; Dew point ; Earth and Environmental Science ; Earth Sciences ; Equity indexed annuities ; Extreme weather ; Forest management ; Greenhouse effect ; Greenhouse gases ; Human influences ; Humidity ; Land cover ; Land development ; Land use ; Methods ; Monthly variations ; Original Paper ; Precipitation ; Precipitation (Meteorology) ; Real estate development ; Regression analysis ; Regression models ; Seasonal variability ; Seasonal variation ; Seasonal variations ; Snow cover ; Soil ; Soil moisture ; Soil temperature ; Specific humidity ; Statistical analysis ; Statistical methods ; Surface temperature ; Surface-air temperature relationships ; Temperature effects ; Temperature measurement ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather stations</subject><ispartof>Theoretical and applied climatology, 2019-04, Vol.136 (1-2), p.499-512</ispartof><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2018</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Theoretical and Applied Climatology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-b8fd5b33d1d1c60c9337fdd1c55d2623e1847a6e1766a13c94c942531e3df2843</citedby><cites>FETCH-LOGICAL-c389t-b8fd5b33d1d1c60c9337fdd1c55d2623e1847a6e1766a13c94c942531e3df2843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-018-2504-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-018-2504-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Bernhardt, Jase</creatorcontrib><creatorcontrib>Carleton, Andrew M.</creatorcontrib><title>Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981–2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R
2
> 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R
2
values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.</description><subject>Air pollution</subject><subject>Air temperature</subject><subject>Anthropogenic factors</subject><subject>Aquatic Pollution</subject><subject>Asymmetry</subject><subject>Atmosphere</subject><subject>Atmospheric models</subject><subject>Atmospheric moisture</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate science</subject><subject>Climatology</subject><subject>Cloud cover</subject><subject>Clouds (Meteorology)</subject><subject>Daily</subject><subject>Daily temperatures</subject><subject>Dew point</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Equity indexed annuities</subject><subject>Extreme weather</subject><subject>Forest management</subject><subject>Greenhouse effect</subject><subject>Greenhouse gases</subject><subject>Human influences</subject><subject>Humidity</subject><subject>Land cover</subject><subject>Land development</subject><subject>Land use</subject><subject>Methods</subject><subject>Monthly variations</subject><subject>Original Paper</subject><subject>Precipitation</subject><subject>Precipitation (Meteorology)</subject><subject>Real estate development</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Seasonal variability</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Snow cover</subject><subject>Soil</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Specific humidity</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Surface temperature</subject><subject>Surface-air temperature relationships</subject><subject>Temperature effects</subject><subject>Temperature measurement</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Weather 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spatial and seasonal variability</title><author>Bernhardt, Jase ; Carleton, Andrew M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-b8fd5b33d1d1c60c9337fdd1c55d2623e1847a6e1766a13c94c942531e3df2843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Air pollution</topic><topic>Air temperature</topic><topic>Anthropogenic factors</topic><topic>Aquatic Pollution</topic><topic>Asymmetry</topic><topic>Atmosphere</topic><topic>Atmospheric models</topic><topic>Atmospheric moisture</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Atmospheric Sciences</topic><topic>Climate science</topic><topic>Climatology</topic><topic>Cloud cover</topic><topic>Clouds (Meteorology)</topic><topic>Daily</topic><topic>Daily temperatures</topic><topic>Dew point</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Equity 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bernhardt, Jase</au><au>Carleton, Andrew M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability</atitle><jtitle>Theoretical and applied climatology</jtitle><stitle>Theor Appl Climatol</stitle><date>2019-04-01</date><risdate>2019</risdate><volume>136</volume><issue>1-2</issue><spage>499</spage><epage>512</epage><pages>499-512</pages><issn>0177-798X</issn><eissn>1434-4483</eissn><abstract>The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981–2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R
2
> 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R
2
values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00704-018-2504-7</doi><tpages>14</tpages></addata></record> |
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| ispartof | Theoretical and applied climatology, 2019-04, Vol.136 (1-2), p.499-512 |
| issn | 0177-798X 1434-4483 |
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| subjects | Air pollution Air temperature Anthropogenic factors Aquatic Pollution Asymmetry Atmosphere Atmospheric models Atmospheric moisture Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate science Climatology Cloud cover Clouds (Meteorology) Daily Daily temperatures Dew point Earth and Environmental Science Earth Sciences Equity indexed annuities Extreme weather Forest management Greenhouse effect Greenhouse gases Human influences Humidity Land cover Land development Land use Methods Monthly variations Original Paper Precipitation Precipitation (Meteorology) Real estate development Regression analysis Regression models Seasonal variability Seasonal variation Seasonal variations Snow cover Soil Soil moisture Soil temperature Specific humidity Statistical analysis Statistical methods Surface temperature Surface-air temperature relationships Temperature effects Temperature measurement Waste Water Technology Water Management Water Pollution Control Weather stations |
| title | Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability |
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