Development and evaluation of regional SST regression algorithms for FY-3C/VIRR data in the western north pacific

ABSTRACT

Satellite remote sensing has been one of the most main methods to acquire the sea surface temperature (SST). In this study, the new regional algorithms to estimate SST in the western north Pacific are developed using the data from the visible and infrared radiometer (VIRR) aboard the Chinese FengYun-3 C (FY-3 C) meteorological satellite. The new regional algorithms are based on the nonlinear SST (NLSST) and triple window NLSST (TNLSST), and improve the accuracy of SST by adding atmospheric correction terms to the original forms of NLSST and TNLSST. For discriminating the original algorithms, the new regional algorithms are named as corrected NLSST (CNLSST) and corrected TNLSST (CTNLSST), respectively. The regional SST algorithms are evaluated with in-situ data in the western north Pacific. Compared with NLSST, CNLSST reduces the bias and root-mean-square error (RMSE) from 0.0259°C and 0.6376°C to 0.0162°C and 0.6185°C at daytime, and from −0.0348°C and 0.6365°C to −0.0291°C and 0.6036°C at night. Also, with comparison to TNLSST, CTNLSST reduces the bias and RMSE from −0.0822°C and 0.5691°C to −0.0550°C and 0.5424°C.

Acknowledgments

The authors thank NSMC/CMA for providing FY-3C/VIRR data, also thank STAR/NESDIS/NOAA and NCEI/NOAA for providing iQuam dataset and OISST data.

Additional information

Funding

This work was supported by the Science and Technology Program of Guangzhou, China [grant number 201707020031].