Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System
The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwa...
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| Veröffentlicht in: | Monthly weather review 2021-07, Vol.149 (7), p.2171 |
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| creator | Lien, Guo-Yuan Lin, Chung-Han Huang, Zih-Mao Teng, Wen-Hsin Chen, Jen-Her Lin, Ching-Chieh Ho, Hsu-Hui Huang, Jyun-Ying Hong, Jing-Shan Cheng, Chia-Ping Huang, Ching-Yuang |
| description | The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB. |
| doi_str_mv | 10.1175/MWR-D-20-0267.1 |
| format | Article |
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The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.</description><identifier>ISSN: 0027-0644</identifier><identifier>EISSN: 1520-0493</identifier><identifier>DOI: 10.1175/MWR-D-20-0267.1</identifier><language>eng</language><publisher>Washington: American Meteorological Society</publisher><subject>Algorithms ; Assimilation ; Cyclones ; Data assimilation ; Data collection ; Deformation ; Ensemble forecasting ; Global navigation satellite system ; Global positioning systems ; Global weather ; GPS ; Interpolation ; Navigation ; Navigation satellites ; Navigation systems ; Navigational satellites ; Numerical prediction ; Numerical weather forecasting ; Orbits ; Quality control ; Radio ; Radio occultation ; Rain ; Real time ; Satellite constellations ; Satellite observation ; Satellites ; Sensitivity ; Tropical climate ; Tropical environment ; Tropical environments ; Weather forecasting</subject><ispartof>Monthly weather review, 2021-07, Vol.149 (7), p.2171</ispartof><rights>Copyright American Meteorological Society Jul 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c240t-bd4d00e37836c8f88ceb37750fad4dee65c2807e7293c92a9bc8f96eb0720f8b3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3668,27901,27902</link.rule.ids></links><search><creatorcontrib>Lien, Guo-Yuan</creatorcontrib><creatorcontrib>Lin, Chung-Han</creatorcontrib><creatorcontrib>Huang, Zih-Mao</creatorcontrib><creatorcontrib>Teng, Wen-Hsin</creatorcontrib><creatorcontrib>Chen, Jen-Her</creatorcontrib><creatorcontrib>Lin, Ching-Chieh</creatorcontrib><creatorcontrib>Ho, Hsu-Hui</creatorcontrib><creatorcontrib>Huang, Jyun-Ying</creatorcontrib><creatorcontrib>Hong, Jing-Shan</creatorcontrib><creatorcontrib>Cheng, Chia-Ping</creatorcontrib><creatorcontrib>Huang, Ching-Yuang</creatorcontrib><title>Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System</title><title>Monthly weather review</title><description>The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.</description><subject>Algorithms</subject><subject>Assimilation</subject><subject>Cyclones</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Deformation</subject><subject>Ensemble forecasting</subject><subject>Global navigation satellite system</subject><subject>Global positioning systems</subject><subject>Global weather</subject><subject>GPS</subject><subject>Interpolation</subject><subject>Navigation</subject><subject>Navigation satellites</subject><subject>Navigation systems</subject><subject>Navigational satellites</subject><subject>Numerical prediction</subject><subject>Numerical weather forecasting</subject><subject>Orbits</subject><subject>Quality control</subject><subject>Radio</subject><subject>Radio occultation</subject><subject>Rain</subject><subject>Real time</subject><subject>Satellite constellations</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Sensitivity</subject><subject>Tropical climate</subject><subject>Tropical environment</subject><subject>Tropical environments</subject><subject>Weather forecasting</subject><issn>0027-0644</issn><issn>1520-0493</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNotkF1rwjAUhsPYYM7tereBXUdP0zZJL139mKArWIeXIY0pq7TWJRHxbn9jf2-_ZBV3dTic57wvPAg9BzAIAh4Pl5sVGRMKBCjjg-AG9YL4skVJeIt6AJQTYFF0jx6c2wEAYxHtodPIuaqpauWrdo_nzUFpj9sST5Stz3iarZZZPloTPkyzfDlPCcWz9zzHK7WtWpxpfaz99XWsvMKnyn_itapOav_7_eNwunnFs7otVI2nrTVaOY_zs_OmeUR3paqdefqfffQxnazTN7LIZvN0tCCaRuBJsY22ACbkImRalEJoU4Scx1Cq7mIMizUVwA2nSagTqpKioxJmCuAUSlGEffRyzT3Y9utonJe79mj3XaXsNLGAJkLQjhpeKW1b56wp5cFWjbJnGYC82JWdXTmWFOTFrgzCP7DYbII</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Lien, Guo-Yuan</creator><creator>Lin, Chung-Han</creator><creator>Huang, Zih-Mao</creator><creator>Teng, Wen-Hsin</creator><creator>Chen, Jen-Her</creator><creator>Lin, 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satellites</topic><topic>Navigation systems</topic><topic>Navigational satellites</topic><topic>Numerical prediction</topic><topic>Numerical weather forecasting</topic><topic>Orbits</topic><topic>Quality control</topic><topic>Radio</topic><topic>Radio occultation</topic><topic>Rain</topic><topic>Real time</topic><topic>Satellite constellations</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Sensitivity</topic><topic>Tropical climate</topic><topic>Tropical environment</topic><topic>Tropical environments</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lien, Guo-Yuan</creatorcontrib><creatorcontrib>Lin, Chung-Han</creatorcontrib><creatorcontrib>Huang, Zih-Mao</creatorcontrib><creatorcontrib>Teng, Wen-Hsin</creatorcontrib><creatorcontrib>Chen, Jen-Her</creatorcontrib><creatorcontrib>Lin, Ching-Chieh</creatorcontrib><creatorcontrib>Ho, Hsu-Hui</creatorcontrib><creatorcontrib>Huang, 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review</jtitle><date>2021-07-01</date><risdate>2021</risdate><volume>149</volume><issue>7</issue><spage>2171</spage><pages>2171-</pages><issn>0027-0644</issn><eissn>1520-0493</eissn><abstract>The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.</abstract><cop>Washington</cop><pub>American Meteorological Society</pub><doi>10.1175/MWR-D-20-0267.1</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Monthly weather review, 2021-07, Vol.149 (7), p.2171 |
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| language | eng |
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| source | Free E-Journal (出版社公開部分のみ）; Alma/SFX Local Collection; AMS Journals (Meteorology) |
| subjects | Algorithms Assimilation Cyclones Data assimilation Data collection Deformation Ensemble forecasting Global navigation satellite system Global positioning systems Global weather GPS Interpolation Navigation Navigation satellites Navigation systems Navigational satellites Numerical prediction Numerical weather forecasting Orbits Quality control Radio Radio occultation Rain Real time Satellite constellations Satellite observation Satellites Sensitivity Tropical climate Tropical environment Tropical environments Weather forecasting |
| title | Assimilation Impact of Early FORMOSAT-7/COSMIC-2 GNSS Radio Occultation Data with Taiwan’s CWB Global Forecast System |
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