The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS
A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise \sigma ^{o} bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a se...
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| description | A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise \sigma ^{o} bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise \sigma ^{o} bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term \sigma ^{o} downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected \sigma ^{o} measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes. |
| doi_str_mv | 10.1109/TGRS.2021.3087426 |
| format | Article |
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This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.]]></description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2021.3087426</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Atmospheric precipitations ; Bias ; Calibration ; Cyclones ; Geophysical measurements ; Global navigation satellite system ; Global Positioning System ; Global positioning systems ; GPS ; Hurricanes ; Meteorological satellites ; Microwave radiometers ; microwave reflectometry ; Navigation ; Navigational satellites ; Numerical prediction ; Numerical weather forecasting ; Prediction models ; Predictive models ; radar measurements ; Radiometers ; Rain ; remote sensing ; Retrieval ; Satellite navigation systems ; scattering ; Scatterometers ; Sea surface ; Soil moisture ; Space vehicles ; Tropical climate ; Tropical cyclones ; Weather ; Weather forecasting ; Wind ; Wind forecasting ; Wind measurement ; Wind speed ; Winds</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-24</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-aa15541f75cb48de1e6ef2bcd5c72ecc3e9d6bbd3ddfb56ea2f99a8bd1d41923</citedby><cites>FETCH-LOGICAL-c336t-aa15541f75cb48de1e6ef2bcd5c72ecc3e9d6bbd3ddfb56ea2f99a8bd1d41923</cites><orcidid>0000-0001-6231-8522 ; 0000-0002-4641-188X ; 0000-0001-5113-0938</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9481894$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids></links><search><creatorcontrib>Said, Faozi</creatorcontrib><creatorcontrib>Jelenak, Zorana</creatorcontrib><creatorcontrib>Park, Jeonghwan</creatorcontrib><creatorcontrib>Chang, Paul S.</creatorcontrib><title>The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description><![CDATA[A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.]]></description><subject>Algorithms</subject><subject>Atmospheric precipitations</subject><subject>Bias</subject><subject>Calibration</subject><subject>Cyclones</subject><subject>Geophysical measurements</subject><subject>Global navigation satellite system</subject><subject>Global Positioning System</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Hurricanes</subject><subject>Meteorological satellites</subject><subject>Microwave radiometers</subject><subject>microwave reflectometry</subject><subject>Navigation</subject><subject>Navigational satellites</subject><subject>Numerical prediction</subject><subject>Numerical weather forecasting</subject><subject>Prediction models</subject><subject>Predictive models</subject><subject>radar measurements</subject><subject>Radiometers</subject><subject>Rain</subject><subject>remote sensing</subject><subject>Retrieval</subject><subject>Satellite navigation systems</subject><subject>scattering</subject><subject>Scatterometers</subject><subject>Sea surface</subject><subject>Soil moisture</subject><subject>Space vehicles</subject><subject>Tropical climate</subject><subject>Tropical cyclones</subject><subject>Weather</subject><subject>Weather forecasting</subject><subject>Wind</subject><subject>Wind forecasting</subject><subject>Wind measurement</subject><subject>Wind speed</subject><subject>Winds</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAUhoMoOD9-gHgT8LozJ03T5LIMnYJsshV2GdLk1HVu60w6Yf_ejg2vDhye9z2Hh5AHYEMApp_L8Ww-5IzDMGUqF1xekAFkmUqYFOKSDBhomXCl-TW5iXHFGIgM8gGZlEukk2lR0DJY950smoh00Ww9nWEXGvy1a1qsv9rQdMsNtf3-M7R-7zpaxIgxbnDb0boNdHQYT-bzO3JV23XE-_O8JeXrSzl6Sz6m4_dR8ZG4NJVdYm3_m4A6z1wllEdAiTWvnM9cztG5FLWXVeVT7-sqk2h5rbVVlQcvQPP0ljydaneh_dlj7Myq3Ydtf9FwCTpTOpd5T8GJcqGNMWBtdqHZ2HAwwMzRmjlaM0dr5mytzzyeMg0i_vNaKFBapH9IuWjb</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Said, Faozi</creator><creator>Jelenak, Zorana</creator><creator>Park, Jeonghwan</creator><creator>Chang, Paul S.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula> <tex-math notation="LaTeX">\sigma ^{o} </tex-math></inline-formula> measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2021.3087426</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0001-6231-8522</orcidid><orcidid>https://orcid.org/0000-0002-4641-188X</orcidid><orcidid>https://orcid.org/0000-0001-5113-0938</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Atmospheric precipitations Bias Calibration Cyclones Geophysical measurements Global navigation satellite system Global Positioning System Global positioning systems GPS Hurricanes Meteorological satellites Microwave radiometers microwave reflectometry Navigation Navigational satellites Numerical prediction Numerical weather forecasting Prediction models Predictive models radar measurements Radiometers Rain remote sensing Retrieval Satellite navigation systems scattering Scatterometers Sea surface Soil moisture Space vehicles Tropical climate Tropical cyclones Weather Weather forecasting Wind Wind forecasting Wind measurement Wind speed Winds |
| title | The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS |
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