Estimation of Winds from GPS Radio Occultations
GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is...
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description | GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is pressure. To estimate the precision with which GPS radio occultation data can map winds, dry pressure profiles are simulated from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) at the actual locations of the Challenging Minisatellite Payload (CHAMP) and the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings for the year 2007. Monthly wind maps were created by using Bayesian interpolation on subsampled ERA-Interim data in 3–5-day bins and subsequent averaging over a month. Mapping winds in this approach requires that 1) geostrophy approximates winds; 2) dry pressure approximates pressure in the UTLS; and 3) geopotential height can be mapped accurately given sparse, nonuniform distributions of data. This study found that, under these conditions, it is possible to map monthly winds near the tropopause with an accuracy of 5.6 m s
−1
with CHAMP alone and 4.5 m s
−1
with COSMIC alone. The dominant contributors to uncertainty are undersampling of the atmosphere and ageostrophy, particularly at the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible to capture the general wind pattern and to determine the position of the subtropical jet despite the uncertainty in its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online. |
doi_str_mv | 10.1175/JTECH-D-14-00061.1 |
format | Article |
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−1
with CHAMP alone and 4.5 m s
−1
with COSMIC alone. The dominant contributors to uncertainty are undersampling of the atmosphere and ageostrophy, particularly at the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible to capture the general wind pattern and to determine the position of the subtropical jet despite the uncertainty in its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online.</description><identifier>ISSN: 0739-0572</identifier><identifier>EISSN: 1520-0426</identifier><identifier>DOI: 10.1175/JTECH-D-14-00061.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Atmospheric models ; Global positioning systems ; GPS ; Ionosphere ; Meteorology ; Stratosphere ; Tropopause ; Troposphere ; Weather forecasting ; Wind</subject><ispartof>Journal of atmospheric and oceanic technology, 2014-11, Vol.31 (11), p.2451-2461</ispartof><rights>Copyright American Meteorological Society Nov 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-62a9adbaefe7fea52001d3ec325c2cf9ad5022e6ccf6279241fd6098acb9035d3</citedby><cites>FETCH-LOGICAL-c385t-62a9adbaefe7fea52001d3ec325c2cf9ad5022e6ccf6279241fd6098acb9035d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,3668,27905,27906</link.rule.ids></links><search><creatorcontrib>Verkhoglyadova, Olga P.</creatorcontrib><creatorcontrib>Leroy, Stephen S.</creatorcontrib><creatorcontrib>Ao, Chi O.</creatorcontrib><title>Estimation of Winds from GPS Radio Occultations</title><title>Journal of atmospheric and oceanic technology</title><description>GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is pressure. To estimate the precision with which GPS radio occultation data can map winds, dry pressure profiles are simulated from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) at the actual locations of the Challenging Minisatellite Payload (CHAMP) and the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings for the year 2007. Monthly wind maps were created by using Bayesian interpolation on subsampled ERA-Interim data in 3–5-day bins and subsequent averaging over a month. Mapping winds in this approach requires that 1) geostrophy approximates winds; 2) dry pressure approximates pressure in the UTLS; and 3) geopotential height can be mapped accurately given sparse, nonuniform distributions of data. This study found that, under these conditions, it is possible to map monthly winds near the tropopause with an accuracy of 5.6 m s
−1
with CHAMP alone and 4.5 m s
−1
with COSMIC alone. The dominant contributors to uncertainty are undersampling of the atmosphere and ageostrophy, particularly at the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible to capture the general wind pattern and to determine the position of the subtropical jet despite the uncertainty in its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online.</description><subject>Atmospheric models</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Ionosphere</subject><subject>Meteorology</subject><subject>Stratosphere</subject><subject>Tropopause</subject><subject>Troposphere</subject><subject>Weather forecasting</subject><subject>Wind</subject><issn>0739-0572</issn><issn>1520-0426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNotkLFOwzAQhi0EEqHwAkyRmN3e2bGTjKgtBVSpCIoYLdexpVRtXOxk4O0xKdMN9-n_7z5C7hGmiKWYvW6X82e6oFhQAJA4xQuSoWBAoWDykmRQ8pqCKNk1uYlxnyDkKDMyW8a-Peq-9V3uXf7Vdk3MXfDHfPX2kb_rpvX5xpjh0I9MvCVXTh-ivfufE_L5tNym7vVm9TJ_XFPDK9FTyXStm522zpbO6nQIYMOt4UwYZlzaCWDMSmOcZGXNCnSNhLrSZlcDFw2fkIdz7in478HGXu39ELpUqVAyiYktqkSxM2WCjzFYp04hfRN-FIL6E6NGMWqhsFCjGIX8F_EnVZM</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Verkhoglyadova, Olga P.</creator><creator>Leroy, Stephen S.</creator><creator>Ao, Chi O.</creator><general>American Meteorological Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8AF</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M1Q</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20141101</creationdate><title>Estimation of Winds from GPS Radio Occultations</title><author>Verkhoglyadova, Olga P. ; Leroy, Stephen S. ; Ao, Chi O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-62a9adbaefe7fea52001d3ec325c2cf9ad5022e6ccf6279241fd6098acb9035d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Atmospheric models</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Ionosphere</topic><topic>Meteorology</topic><topic>Stratosphere</topic><topic>Tropopause</topic><topic>Troposphere</topic><topic>Weather forecasting</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verkhoglyadova, Olga P.</creatorcontrib><creatorcontrib>Leroy, Stephen S.</creatorcontrib><creatorcontrib>Ao, Chi O.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Military Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of atmospheric and oceanic technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verkhoglyadova, Olga P.</au><au>Leroy, Stephen S.</au><au>Ao, Chi O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Winds from GPS Radio Occultations</atitle><jtitle>Journal of atmospheric and oceanic technology</jtitle><date>2014-11-01</date><risdate>2014</risdate><volume>31</volume><issue>11</issue><spage>2451</spage><epage>2461</epage><pages>2451-2461</pages><issn>0739-0572</issn><eissn>1520-0426</eissn><abstract>GPS radio occultations (RO) offer the possibility to map winds in the upper troposphere and lower stratosphere (UTLS) region because geopotential height is the independent coordinate of retrieval. Most other sounders do not offer this possibility because their independent coordinate of retrieval is pressure. To estimate the precision with which GPS radio occultation data can map winds, dry pressure profiles are simulated from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) at the actual locations of the Challenging Minisatellite Payload (CHAMP) and the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings for the year 2007. Monthly wind maps were created by using Bayesian interpolation on subsampled ERA-Interim data in 3–5-day bins and subsequent averaging over a month. Mapping winds in this approach requires that 1) geostrophy approximates winds; 2) dry pressure approximates pressure in the UTLS; and 3) geopotential height can be mapped accurately given sparse, nonuniform distributions of data. This study found that, under these conditions, it is possible to map monthly winds near the tropopause with an accuracy of 5.6 m s
−1
with CHAMP alone and 4.5 m s
−1
with COSMIC alone. The dominant contributors to uncertainty are undersampling of the atmosphere and ageostrophy, particularly at the leading and trailing edges of the subtropical jet. The former is reduced with increased density of GPS RO soundings. The latter cannot be reduced even after iteration for balanced winds. Nevertheless, it is still possible to capture the general wind pattern and to determine the position of the subtropical jet despite the uncertainty in its magnitude. COSMIC radio occultation measurements from 2006 through 2011 were used to estimate monthly geostrophic winds maps in UTLS. The resultant wind dataset is posted online.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/JTECH-D-14-00061.1</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Atmospheric models Global positioning systems GPS Ionosphere Meteorology Stratosphere Tropopause Troposphere Weather forecasting Wind |
title | Estimation of Winds from GPS Radio Occultations |
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