Influence of spatial information resolution on the relation between elevation and temperature
The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central Fran...
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| Veröffentlicht in: | International journal of climatology 2018-12, Vol.38 (15), p.5677-5688 |
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| description | The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R2, is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations.
Influence of DEM spatial resolution on the altidunal temperature lapse rate. |
| doi_str_mv | 10.1002/joc.5771 |
| format | Article |
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Influence of DEM spatial resolution on the altidunal temperature lapse rate.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.5771</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Climate models ; Climatology ; Correlation ; Correlation analysis ; Daily temperatures ; digital elevation model ; Earth Sciences ; Elevation ; Environmental Sciences ; France ; Geography ; Global Changes ; Humanities and Social Sciences ; Lapse rate ; Regional climate models ; Regional climates ; Regression analysis ; Resolution ; Sciences of the Universe ; Spatial data ; spatial resolution ; Temperature ; Temperature effects ; Weather stations</subject><ispartof>International journal of climatology, 2018-12, Vol.38 (15), p.5677-5688</ispartof><rights>2018 Royal Meteorological Society</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3271-4f408bd27229ca6a9c1ef039596566a51dc9f723f8213993e2c94a5f2b8530ef3</citedby><cites>FETCH-LOGICAL-c3271-4f408bd27229ca6a9c1ef039596566a51dc9f723f8213993e2c94a5f2b8530ef3</cites><orcidid>0000-0002-4817-1779 ; 0000-0002-9339-797X ; 0000-0002-9203-0922 ; 0000-0001-9650-9474</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.5771$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.5771$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,780,784,885,1416,27922,27923,45572,45573</link.rule.ids><backlink>$$Uhttps://hal.science/hal-01863415$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Joly, Daniel</creatorcontrib><creatorcontrib>Castel, Thierry</creatorcontrib><creatorcontrib>Pohl, Benjamin</creatorcontrib><creatorcontrib>Richard, Yves</creatorcontrib><title>Influence of spatial information resolution on the relation between elevation and temperature</title><title>International journal of climatology</title><description>The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R2, is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations.
Influence of DEM spatial resolution on the altidunal temperature lapse rate.</description><subject>Climate models</subject><subject>Climatology</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Daily temperatures</subject><subject>digital elevation model</subject><subject>Earth Sciences</subject><subject>Elevation</subject><subject>Environmental Sciences</subject><subject>France</subject><subject>Geography</subject><subject>Global Changes</subject><subject>Humanities and Social Sciences</subject><subject>Lapse rate</subject><subject>Regional climate models</subject><subject>Regional climates</subject><subject>Regression analysis</subject><subject>Resolution</subject><subject>Sciences of the Universe</subject><subject>Spatial data</subject><subject>spatial resolution</subject><subject>Temperature</subject><subject>Temperature effects</subject><subject>Weather stations</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kE1LAzEQhoMoWKvgT1jwooet-djsJsdS1FYKvehRQppO6JZ0U5Pdlv570654EwZmeOeZd4ZB6J7gEcGYPm-8GfGqIhdoQLCscoyFuEQDLKTMRUHENbqJcYMxlpKUA_Q1a6zroDGQeZvFnW5r7bK6sT5sU-2bLED0rjuXKdo1JMX1rSW0B4AmAwf7XtHNKmthu4Og2y7ALbqy2kW4-81D9Pn68jGZ5vPF22wynueG0YrkhS2wWK5oRak0utTSELCYSS5LXpaak5WRtqLMCkqYlAyokYXmli4FZxgsG6Kn3netndqFeqvDUXldq-l4rk4aJqJkBeF7ktiHnt0F_91BbNXGd6FJ5ylK0h2U47RqiB57ygQfYwD7Z0uwOj06TRl1enRC8x491A6O_3LqfTE58z_VOH58</recordid><startdate>201812</startdate><enddate>201812</enddate><creator>Joly, Daniel</creator><creator>Castel, Thierry</creator><creator>Pohl, Benjamin</creator><creator>Richard, Yves</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><general>Wiley</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>1XC</scope><scope>BXJBU</scope><orcidid>https://orcid.org/0000-0002-4817-1779</orcidid><orcidid>https://orcid.org/0000-0002-9339-797X</orcidid><orcidid>https://orcid.org/0000-0002-9203-0922</orcidid><orcidid>https://orcid.org/0000-0001-9650-9474</orcidid></search><sort><creationdate>201812</creationdate><title>Influence of spatial information resolution on the relation between elevation and temperature</title><author>Joly, Daniel ; Castel, Thierry ; Pohl, Benjamin ; Richard, Yves</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3271-4f408bd27229ca6a9c1ef039596566a51dc9f723f8213993e2c94a5f2b8530ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Climate models</topic><topic>Climatology</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>Daily temperatures</topic><topic>digital elevation model</topic><topic>Earth Sciences</topic><topic>Elevation</topic><topic>Environmental Sciences</topic><topic>France</topic><topic>Geography</topic><topic>Global Changes</topic><topic>Humanities and Social Sciences</topic><topic>Lapse rate</topic><topic>Regional climate models</topic><topic>Regional climates</topic><topic>Regression analysis</topic><topic>Resolution</topic><topic>Sciences of the Universe</topic><topic>Spatial data</topic><topic>spatial resolution</topic><topic>Temperature</topic><topic>Temperature effects</topic><topic>Weather stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Joly, Daniel</creatorcontrib><creatorcontrib>Castel, Thierry</creatorcontrib><creatorcontrib>Pohl, Benjamin</creatorcontrib><creatorcontrib>Richard, Yves</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Joly, Daniel</au><au>Castel, Thierry</au><au>Pohl, Benjamin</au><au>Richard, Yves</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Influence of spatial information resolution on the relation between elevation and temperature</atitle><jtitle>International journal of climatology</jtitle><date>2018-12</date><risdate>2018</risdate><volume>38</volume><issue>15</issue><spage>5677</spage><epage>5688</epage><pages>5677-5688</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R2, is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations.
Influence of DEM spatial resolution on the altidunal temperature lapse rate.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.5771</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4817-1779</orcidid><orcidid>https://orcid.org/0000-0002-9339-797X</orcidid><orcidid>https://orcid.org/0000-0002-9203-0922</orcidid><orcidid>https://orcid.org/0000-0001-9650-9474</orcidid></addata></record> |
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| subjects | Climate models Climatology Correlation Correlation analysis Daily temperatures digital elevation model Earth Sciences Elevation Environmental Sciences France Geography Global Changes Humanities and Social Sciences Lapse rate Regional climate models Regional climates Regression analysis Resolution Sciences of the Universe Spatial data spatial resolution Temperature Temperature effects Weather stations |
| title | Influence of spatial information resolution on the relation between elevation and temperature |
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