Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds
Direct remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water...
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description | Direct remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.
The delay‐Doppler map (DDM) is a lower‐level observation of Global Navigation Satellite System Reflectometry (GNSS‐R) compared with the retrieved wind speed. This study demonstrates the assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Observation bias correction, quality control, and error characterization are desc |
doi_str_mv | 10.1002/qj.4034 |
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The delay‐Doppler map (DDM) is a lower‐level observation of Global Navigation Satellite System Reflectometry (GNSS‐R) compared with the retrieved wind speed. This study demonstrates the assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Observation bias correction, quality control, and error characterization are described.</description><identifier>ISSN: 0035-9009</identifier><identifier>EISSN: 1477-870X</identifier><identifier>DOI: 10.1002/qj.4034</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Atmospheric water ; Atmospheric water vapor ; Bias ; Brightness temperature ; Control methods ; Cyclones ; Data assimilation ; Data collection ; Deformation ; Doppler sonar ; GNSS‐R ; Meteorological satellites ; Navigation ; Navigation satellites ; Navigation systems ; Navigational satellites ; Oceans ; Radar ; Radar backscatter ; Radiometers ; Scatterometers ; Soil moisture ; Statistical analysis ; Statistical methods ; Surface radiation temperature ; Surface wind ; Water vapor ; Water vapour ; Weather forecasting ; Wind ; Wind speed ; Winds</subject><ispartof>Quarterly journal of the Royal Meteorological Society, 2021-04, Vol.147 (737), p.2469-2489</ispartof><rights>2021 Royal Meteorological Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2894-e27a2588be4da4c87c2549dbacf0234c52cd3e991b31d9ed2b7ca7553f8429963</citedby><cites>FETCH-LOGICAL-c2894-e27a2588be4da4c87c2549dbacf0234c52cd3e991b31d9ed2b7ca7553f8429963</cites><orcidid>0000-0002-0269-9834 ; 0000-0002-4962-9438 ; 0000-0002-6888-280X</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%2Fqj.4034$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fqj.4034$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Huang, Feixiong</creatorcontrib><creatorcontrib>Garrison, James L.</creatorcontrib><creatorcontrib>Mark Leidner, S.</creatorcontrib><creatorcontrib>Grieco, Giuseppe</creatorcontrib><creatorcontrib>Stoffelen, Ad</creatorcontrib><creatorcontrib>Annane, Bachir</creatorcontrib><creatorcontrib>Hoffman, Ross N.</creatorcontrib><title>Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds</title><title>Quarterly journal of the Royal Meteorological Society</title><description>Direct remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.
The delay‐Doppler map (DDM) is a lower‐level observation of Global Navigation Satellite System Reflectometry (GNSS‐R) compared with the retrieved wind speed. This study demonstrates the assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Observation bias correction, quality control, and error characterization are described.</description><subject>Atmospheric water</subject><subject>Atmospheric water vapor</subject><subject>Bias</subject><subject>Brightness temperature</subject><subject>Control methods</subject><subject>Cyclones</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Deformation</subject><subject>Doppler sonar</subject><subject>GNSS‐R</subject><subject>Meteorological satellites</subject><subject>Navigation</subject><subject>Navigation satellites</subject><subject>Navigation systems</subject><subject>Navigational satellites</subject><subject>Oceans</subject><subject>Radar</subject><subject>Radar backscatter</subject><subject>Radiometers</subject><subject>Scatterometers</subject><subject>Soil moisture</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Surface radiation temperature</subject><subject>Surface wind</subject><subject>Water vapor</subject><subject>Water vapour</subject><subject>Weather forecasting</subject><subject>Wind</subject><subject>Wind speed</subject><subject>Winds</subject><issn>0035-9009</issn><issn>1477-870X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kN9KwzAUh4MoOKf4CgEvvJDONH-W5nJMncpQZArelTRNNaVtuqRz9E7wBXxGn8Rs89abcw7nfHwcfgCcxmgUI4Qvl-WIIkL3wCCmnEcJR6_7YIAQYZFASByCI-9LhBDjmA_A18R7U5tKdsY20BZw9rBYQKeLSqvO1rpzPcx1Jfufz-8r27aVdrCWrYdr071DCbu1DZfc1LrxwSAr-CGd2drCLEPpvfEb8Vtls7CySssG-pUrpNLB0uT-GBwUsvL65K8PwcvN9fP0Npo_zu6mk3mkcCJopDGXmCVJpmkuqUq4woyKPJOqQJhQxbDKiRYizkicC53jjCvJGSNFQrEQYzIEZztv6-xypX2Xlnblwos-xYyMWUxCSoE631HKWe9DEmnrTC1dn8Yo3SScLst0k3AgL3bk2lS6_w9Ln-639C8v7n_I</recordid><startdate>202104</startdate><enddate>202104</enddate><creator>Huang, Feixiong</creator><creator>Garrison, James L.</creator><creator>Mark Leidner, S.</creator><creator>Grieco, Giuseppe</creator><creator>Stoffelen, Ad</creator><creator>Annane, Bachir</creator><creator>Hoffman, Ross N.</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</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><orcidid>https://orcid.org/0000-0002-0269-9834</orcidid><orcidid>https://orcid.org/0000-0002-4962-9438</orcidid><orcidid>https://orcid.org/0000-0002-6888-280X</orcidid></search><sort><creationdate>202104</creationdate><title>Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds</title><author>Huang, Feixiong ; Garrison, James L. ; Mark Leidner, S. ; Grieco, Giuseppe ; Stoffelen, Ad ; Annane, Bachir ; Hoffman, Ross N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2894-e27a2588be4da4c87c2549dbacf0234c52cd3e991b31d9ed2b7ca7553f8429963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Atmospheric water</topic><topic>Atmospheric water vapor</topic><topic>Bias</topic><topic>Brightness temperature</topic><topic>Control methods</topic><topic>Cyclones</topic><topic>Data assimilation</topic><topic>Data collection</topic><topic>Deformation</topic><topic>Doppler sonar</topic><topic>GNSS‐R</topic><topic>Meteorological satellites</topic><topic>Navigation</topic><topic>Navigation satellites</topic><topic>Navigation systems</topic><topic>Navigational satellites</topic><topic>Oceans</topic><topic>Radar</topic><topic>Radar backscatter</topic><topic>Radiometers</topic><topic>Scatterometers</topic><topic>Soil moisture</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Surface radiation temperature</topic><topic>Surface wind</topic><topic>Water vapor</topic><topic>Water vapour</topic><topic>Weather forecasting</topic><topic>Wind</topic><topic>Wind speed</topic><topic>Winds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Feixiong</creatorcontrib><creatorcontrib>Garrison, James L.</creatorcontrib><creatorcontrib>Mark Leidner, S.</creatorcontrib><creatorcontrib>Grieco, Giuseppe</creatorcontrib><creatorcontrib>Stoffelen, Ad</creatorcontrib><creatorcontrib>Annane, Bachir</creatorcontrib><creatorcontrib>Hoffman, Ross N.</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><jtitle>Quarterly journal of the Royal Meteorological Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Feixiong</au><au>Garrison, James L.</au><au>Mark Leidner, S.</au><au>Grieco, Giuseppe</au><au>Stoffelen, Ad</au><au>Annane, Bachir</au><au>Hoffman, Ross N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds</atitle><jtitle>Quarterly journal of the Royal Meteorological Society</jtitle><date>2021-04</date><risdate>2021</risdate><volume>147</volume><issue>737</issue><spage>2469</spage><epage>2489</epage><pages>2469-2489</pages><issn>0035-9009</issn><eissn>1477-870X</eissn><abstract>Direct remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.
The delay‐Doppler map (DDM) is a lower‐level observation of Global Navigation Satellite System Reflectometry (GNSS‐R) compared with the retrieved wind speed. This study demonstrates the assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Observation bias correction, quality control, and error characterization are described.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/qj.4034</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-0269-9834</orcidid><orcidid>https://orcid.org/0000-0002-4962-9438</orcidid><orcidid>https://orcid.org/0000-0002-6888-280X</orcidid></addata></record> |
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subjects | Atmospheric water Atmospheric water vapor Bias Brightness temperature Control methods Cyclones Data assimilation Data collection Deformation Doppler sonar GNSS‐R Meteorological satellites Navigation Navigation satellites Navigation systems Navigational satellites Oceans Radar Radar backscatter Radiometers Scatterometers Soil moisture Statistical analysis Statistical methods Surface radiation temperature Surface wind Water vapor Water vapour Weather forecasting Wind Wind speed Winds |
title | Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds |
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