Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data
The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product w...
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ftdoajarticles:oai:doaj.org/article:96b73c4c062845878369f33dba087ae1 2024-09-15T18:35:39+00:00 Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data Quanjun He Peng Cui Yanwei Chen 2024-07-01T00:00:00Z https://doi.org/10.3390/rs16152769 https://doaj.org/article/96b73c4c062845878369f33dba087ae1 EN eng MDPI AG https://www.mdpi.com/2072-4292/16/15/2769 https://doaj.org/toc/2072-4292 doi:10.3390/rs16152769 2072-4292 https://doaj.org/article/96b73c4c062845878369f33dba087ae1 Remote Sensing, Vol 16, Iss 15, p 2769 (2024) quality assessment error variation comparison sea surface temperature FY-4B/AGRI Science Q article 2024 ftdoajarticles https://doi.org/10.3390/rs16152769 2024-08-12T15:24:03Z The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of 15 min. This paper conducts the first data quality assessment of operational SST products from the FY-4B/AGRI using quality-controlled measured SSTs from the in situ SST quality monitor dataset and foundation SSTs produced by the operational sea surface temperature and sea ice analysis (OSTIA) system from July 2023 to January 2024. The FY-4B/AGRI SST product provides a data quality level flag on a pixel-by-pixel basis. Accuracy evaluations are conducted on the FY-4B/AGRI SST product with different data quality levels. The results indicate that the FY-4B/AGRI operational SST generally has a negative mean bias compared to in situ SST and OSTIA SST, and that the accuracy of the FY-4B/AGRI SST, with an excellent quality level, can meet the needs of practical applications. The FY-4B/AGRI SST with an excellent quality level demonstrates a strong correlation with in situ SST and OSTIA SST, with a correlation coefficient R exceeding 0.99. Compared with in situ SST, the bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of the FY-4B/AGRI SST with an excellent quality level are −0.19, 0.66, and 0.63 °C in daytime, and −0.15, 0.70, and 0.68 °C at night, respectively. Compared with OSTIA SST, the bias, RMSE, and ubRMSE of the FY-4B/AGRI SST with an excellent data quality level are −0.10, 0.64, and 0.63 °C in daytime, and −0.13, 0.68, and 0.67 °C at night. The FY-4B/AGRI SST tends to underestimate the sea water temperature in mid–low-latitude regions, while it tends to overestimate sea water temperature in high-latitude regions and near the edges of the full disk. The time-varying validation of FY-4B/AGRI SST accuracy shows weak fluctuations with a period of 3–4 months. Hourly ... Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Remote Sensing 16 15 2769 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
quality assessment error variation comparison sea surface temperature FY-4B/AGRI Science Q |
spellingShingle |
quality assessment error variation comparison sea surface temperature FY-4B/AGRI Science Q Quanjun He Peng Cui Yanwei Chen Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
topic_facet |
quality assessment error variation comparison sea surface temperature FY-4B/AGRI Science Q |
description |
The Fengyun-4B (FY-4B) satellite is currently the primary operational geostationary meteorological satellite in China, replacing the previous FY-4A satellite. The advanced geostationary radiation imager (AGRI) aboard the FY-4B satellite provides an operational sea surface temperature (SST) product with a high observation frequency of 15 min. This paper conducts the first data quality assessment of operational SST products from the FY-4B/AGRI using quality-controlled measured SSTs from the in situ SST quality monitor dataset and foundation SSTs produced by the operational sea surface temperature and sea ice analysis (OSTIA) system from July 2023 to January 2024. The FY-4B/AGRI SST product provides a data quality level flag on a pixel-by-pixel basis. Accuracy evaluations are conducted on the FY-4B/AGRI SST product with different data quality levels. The results indicate that the FY-4B/AGRI operational SST generally has a negative mean bias compared to in situ SST and OSTIA SST, and that the accuracy of the FY-4B/AGRI SST, with an excellent quality level, can meet the needs of practical applications. The FY-4B/AGRI SST with an excellent quality level demonstrates a strong correlation with in situ SST and OSTIA SST, with a correlation coefficient R exceeding 0.99. Compared with in situ SST, the bias, root mean square error (RMSE), and unbiased RMSE (ubRMSE) of the FY-4B/AGRI SST with an excellent quality level are −0.19, 0.66, and 0.63 °C in daytime, and −0.15, 0.70, and 0.68 °C at night, respectively. Compared with OSTIA SST, the bias, RMSE, and ubRMSE of the FY-4B/AGRI SST with an excellent data quality level are −0.10, 0.64, and 0.63 °C in daytime, and −0.13, 0.68, and 0.67 °C at night. The FY-4B/AGRI SST tends to underestimate the sea water temperature in mid–low-latitude regions, while it tends to overestimate sea water temperature in high-latitude regions and near the edges of the full disk. The time-varying validation of FY-4B/AGRI SST accuracy shows weak fluctuations with a period of 3–4 months. Hourly ... |
format |
Article in Journal/Newspaper |
author |
Quanjun He Peng Cui Yanwei Chen |
author_facet |
Quanjun He Peng Cui Yanwei Chen |
author_sort |
Quanjun He |
title |
Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
title_short |
Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
title_full |
Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
title_fullStr |
Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
title_full_unstemmed |
Quality Assessment of Operational Sea Surface Temperature Product from FY-4B/AGRI with In Situ and OSTIA Data |
title_sort |
quality assessment of operational sea surface temperature product from fy-4b/agri with in situ and ostia data |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/rs16152769 https://doaj.org/article/96b73c4c062845878369f33dba087ae1 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing, Vol 16, Iss 15, p 2769 (2024) |
op_relation |
https://www.mdpi.com/2072-4292/16/15/2769 https://doaj.org/toc/2072-4292 doi:10.3390/rs16152769 2072-4292 https://doaj.org/article/96b73c4c062845878369f33dba087ae1 |
op_doi |
https://doi.org/10.3390/rs16152769 |
container_title |
Remote Sensing |
container_volume |
16 |
container_issue |
15 |
container_start_page |
2769 |
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1810478855121534976 |