Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis
Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/4/590 |
| _version_ | 1819002696698429440 |
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| author | Korak Saha Prasanjit Dash Xuepeng Zhao Huai-min Zhang |
| author_facet | Korak Saha Prasanjit Dash Xuepeng Zhao Huai-min Zhang |
| author_sort | Korak Saha |
| collection | DOAJ |
| description | Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values. |
| first_indexed | 2024-12-20T23:09:13Z |
| format | Article |
| id | doaj.art-7fb7c4cee04648b0b53d33f43c63bb6d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:09:13Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7fb7c4cee04648b0b53d33f43c63bb6d2022-12-21T19:23:47ZengMDPI AGRemote Sensing2072-42922020-02-0112459010.3390/rs12040590rs12040590Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation AnalysisKorak Saha0Prasanjit Dash1Xuepeng Zhao2Huai-min Zhang3Cooperative Institute for Satellite Earth System Studies (CISESS)-Maryland, University of Maryland, College Park, MD 20740, USACooperative Institute for Research in Atmosphere (CIRA), Colorado State University, Fort Collins, CO 80523, USANational Centers for Environmental Information (NCEI), NOAA/NESDIS, Silver Spring, MD 20910, USANational Centers for Environmental Information (NCEI), NOAA/NESDIS, Asheville, NC 28801, USASea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values.https://www.mdpi.com/2072-4292/12/4/590sea surface temperaturepathfinder ssttriple collocationerror characterizationroot-mean-square error |
| spellingShingle | Korak Saha Prasanjit Dash Xuepeng Zhao Huai-min Zhang Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis Remote Sensing sea surface temperature pathfinder sst triple collocation error characterization root-mean-square error |
| title | Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis |
| title_full | Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis |
| title_fullStr | Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis |
| title_full_unstemmed | Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis |
| title_short | Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis |
| title_sort | error estimation of pathfinder version 5 3 level 3c sst using extended triple collocation analysis |
| topic | sea surface temperature pathfinder sst triple collocation error characterization root-mean-square error |
| url | https://www.mdpi.com/2072-4292/12/4/590 |
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