A technique for mapping global illuminance from satellite data
A technique for mapping global illuminance from satellite data was developed. A five-year (1998–2002) climatology of global solar illuminance obtained from this technique is presented for Thailand using hourly GMS-5 satellite data. The technique is based on a radiation budget model which traces sola...
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description | A technique for mapping global illuminance from satellite data was developed. A five-year (1998–2002) climatology of global solar illuminance obtained from this technique is presented for Thailand using hourly GMS-5 satellite data. The technique is based on a radiation budget model which traces solar radiation as it is scattered, absorbed and reflected back to space. The model produces an earth-atmospheric albedo in the satellite spectral window as well as global illuminance at the earth’s surface. The model is tuned using surface illuminance measurements at four stations in Thailand: Chiang Mai (18.78°N, 98.98°E), Ubon Ratchatani (15.25°N, 104.87°E), Songkhla (7.20°N, 100.60 °E) and Nakhon Pathom (13.82°N, 100.04°E). In the mapping process, a satellite earth-atmospheric albedo at any locations yields a cloud-atmospheric albedo in the satellite band, which is then transformed into a cloud-atmospheric albedo in the photopic band. Having obtained the photopic cloud-atmospheric albedo, the model calculates surface illuminance. The model gives a root mean square difference of 8.1% and a mean bias difference of −2.6% when tested against an independent data set. Monthly average maps are presented covering Thailand for local times of 10:30, 12:30 and 14:30. |
doi_str_mv | 10.1016/j.solener.2007.11.003 |
format | Article |
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A five-year (1998–2002) climatology of global solar illuminance obtained from this technique is presented for Thailand using hourly GMS-5 satellite data. The technique is based on a radiation budget model which traces solar radiation as it is scattered, absorbed and reflected back to space. The model produces an earth-atmospheric albedo in the satellite spectral window as well as global illuminance at the earth’s surface. The model is tuned using surface illuminance measurements at four stations in Thailand: Chiang Mai (18.78°N, 98.98°E), Ubon Ratchatani (15.25°N, 104.87°E), Songkhla (7.20°N, 100.60 °E) and Nakhon Pathom (13.82°N, 100.04°E). In the mapping process, a satellite earth-atmospheric albedo at any locations yields a cloud-atmospheric albedo in the satellite band, which is then transformed into a cloud-atmospheric albedo in the photopic band. Having obtained the photopic cloud-atmospheric albedo, the model calculates surface illuminance. The model gives a root mean square difference of 8.1% and a mean bias difference of −2.6% when tested against an independent data set. Monthly average maps are presented covering Thailand for local times of 10:30, 12:30 and 14:30.</description><identifier>ISSN: 0038-092X</identifier><identifier>EISSN: 1471-1257</identifier><identifier>DOI: 10.1016/j.solener.2007.11.003</identifier><identifier>CODEN: SRENA4</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Daylight ; Earth, ocean, space ; Exact sciences and technology ; External geophysics ; Illuminance ; Light ; Mapping ; Meteorology ; Radiation ; Radiative transfer. Solar radiation ; Satellite data ; Solar energy ; Solar radiation ; Temperature</subject><ispartof>Solar energy, 2008-01, Vol.82 (6), p.543-555</ispartof><rights>2007 Elsevier Ltd</rights><rights>2008 INIST-CNRS</rights><rights>Copyright Pergamon Press Inc. Jun 2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-273aaf14d15eb4b83b340eb96f087cead3caf2100e2c09604d33f4f3d5c0f8543</citedby><cites>FETCH-LOGICAL-c438t-273aaf14d15eb4b83b340eb96f087cead3caf2100e2c09604d33f4f3d5c0f8543</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0038092X07002290$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20323876$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Vignola, F</contributor><creatorcontrib>Janjai, S.</creatorcontrib><creatorcontrib>Tohsing, K.</creatorcontrib><creatorcontrib>Nunez, M.</creatorcontrib><creatorcontrib>Laksanaboonsong, J.</creatorcontrib><title>A technique for mapping global illuminance from satellite data</title><title>Solar energy</title><description>A technique for mapping global illuminance from satellite data was developed. A five-year (1998–2002) climatology of global solar illuminance obtained from this technique is presented for Thailand using hourly GMS-5 satellite data. The technique is based on a radiation budget model which traces solar radiation as it is scattered, absorbed and reflected back to space. The model produces an earth-atmospheric albedo in the satellite spectral window as well as global illuminance at the earth’s surface. The model is tuned using surface illuminance measurements at four stations in Thailand: Chiang Mai (18.78°N, 98.98°E), Ubon Ratchatani (15.25°N, 104.87°E), Songkhla (7.20°N, 100.60 °E) and Nakhon Pathom (13.82°N, 100.04°E). In the mapping process, a satellite earth-atmospheric albedo at any locations yields a cloud-atmospheric albedo in the satellite band, which is then transformed into a cloud-atmospheric albedo in the photopic band. Having obtained the photopic cloud-atmospheric albedo, the model calculates surface illuminance. The model gives a root mean square difference of 8.1% and a mean bias difference of −2.6% when tested against an independent data set. Monthly average maps are presented covering Thailand for local times of 10:30, 12:30 and 14:30.</description><subject>Daylight</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Illuminance</subject><subject>Light</subject><subject>Mapping</subject><subject>Meteorology</subject><subject>Radiation</subject><subject>Radiative transfer. Solar radiation</subject><subject>Satellite data</subject><subject>Solar energy</subject><subject>Solar radiation</subject><subject>Temperature</subject><issn>0038-092X</issn><issn>1471-1257</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFkE1rGzEQhkVJoa7Tn1BYAs1tNzP6sORLQwhtEgjkkkBvQquVXBntypHWhfz7ytj00EtOgtEz77w8hHxF6BBwdbXtSopucrmjALJD7ADYB7JALrFFKuQZWdSJamFNf30in0vZAqBEJRfk-00zO_t7Cq971_iUm9HsdmHaNJuYehObEON-DJOZbP3OaWyKmV2MYXbNYGZzTj56E4v7cnqX5OXnj-fb-_bx6e7h9uaxtZypuaWSGeORDyhcz3vFesbB9euVByWtMwOzxlMEcNTCegV8YMxzzwZhwSvB2ZJcHnN3OdWmZdZjKLYWMZNL-6IpKEGVhApe_Adu0z5PtZumDKXgAkWFxBGyOZWSnde7HEaT3zSCPijVW31Sqg9KNaKuAuvet1O4KdZEn6uWUP4tU2CUKbmq3PWRc1XJn1BTig2uKhxCdnbWQwrvXPoL_zSOdw</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Janjai, S.</creator><creator>Tohsing, K.</creator><creator>Nunez, M.</creator><creator>Laksanaboonsong, J.</creator><general>Elsevier Ltd</general><general>Elsevier</general><general>Pergamon Press Inc</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope><scope>7TG</scope><scope>KL.</scope></search><sort><creationdate>20080101</creationdate><title>A technique for mapping global illuminance from satellite data</title><author>Janjai, S. ; Tohsing, K. ; Nunez, M. ; Laksanaboonsong, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-273aaf14d15eb4b83b340eb96f087cead3caf2100e2c09604d33f4f3d5c0f8543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Daylight</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Illuminance</topic><topic>Light</topic><topic>Mapping</topic><topic>Meteorology</topic><topic>Radiation</topic><topic>Radiative transfer. Solar radiation</topic><topic>Satellite data</topic><topic>Solar energy</topic><topic>Solar radiation</topic><topic>Temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Janjai, S.</creatorcontrib><creatorcontrib>Tohsing, K.</creatorcontrib><creatorcontrib>Nunez, M.</creatorcontrib><creatorcontrib>Laksanaboonsong, J.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Solar energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Janjai, S.</au><au>Tohsing, K.</au><au>Nunez, M.</au><au>Laksanaboonsong, J.</au><au>Vignola, F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A technique for mapping global illuminance from satellite data</atitle><jtitle>Solar energy</jtitle><date>2008-01-01</date><risdate>2008</risdate><volume>82</volume><issue>6</issue><spage>543</spage><epage>555</epage><pages>543-555</pages><issn>0038-092X</issn><eissn>1471-1257</eissn><coden>SRENA4</coden><abstract>A technique for mapping global illuminance from satellite data was developed. A five-year (1998–2002) climatology of global solar illuminance obtained from this technique is presented for Thailand using hourly GMS-5 satellite data. The technique is based on a radiation budget model which traces solar radiation as it is scattered, absorbed and reflected back to space. The model produces an earth-atmospheric albedo in the satellite spectral window as well as global illuminance at the earth’s surface. The model is tuned using surface illuminance measurements at four stations in Thailand: Chiang Mai (18.78°N, 98.98°E), Ubon Ratchatani (15.25°N, 104.87°E), Songkhla (7.20°N, 100.60 °E) and Nakhon Pathom (13.82°N, 100.04°E). In the mapping process, a satellite earth-atmospheric albedo at any locations yields a cloud-atmospheric albedo in the satellite band, which is then transformed into a cloud-atmospheric albedo in the photopic band. Having obtained the photopic cloud-atmospheric albedo, the model calculates surface illuminance. The model gives a root mean square difference of 8.1% and a mean bias difference of −2.6% when tested against an independent data set. Monthly average maps are presented covering Thailand for local times of 10:30, 12:30 and 14:30.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.solener.2007.11.003</doi><tpages>13</tpages></addata></record> |
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subjects | Daylight Earth, ocean, space Exact sciences and technology External geophysics Illuminance Light Mapping Meteorology Radiation Radiative transfer. Solar radiation Satellite data Solar energy Solar radiation Temperature |
title | A technique for mapping global illuminance from satellite data |
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