Abstract
Producing high-quality match-ups coupling the Japanese geostationary satellite, Himawari-6 (H6), and buoy SST observations, we have developed the new SST retrieval method. Kawamura et al. (2010) developed the previous version of SST product called MTSAT SST, which left several scientific/technical questions. For solving them, 6,711 algorithm tuning match-ups with precise navigation and 240,476 validation match-ups are generated for covering all seasons and wide ocean coverage. For discriminating the previous MTSAT SST, we call the new version of SST H6 SST. It is found that the SZA dependences of MTSAT SST algorithm are different from area to area of SZA > 40–50° N/S. The regionally different SZA dependences are treated by dividing the H6 disk coverage into five areas by the latitude lines of 40° N/S first and the longitude lines of 100° K and 180° K. Using the algorithm tuning match-ups, Nonlinear SST (NLSST) equations are derived for all of the five areas. Though the sun zenith angle dependency correction term is also examined, there is no significant regional difference. Therefore, this term is used in the H6 SST algorithm again. The new H6 SST equation is formed by the areal NLSST and the sun zenith angle dependency term for each area. The statistical evaluation of H6 SST using the validation match-ups show the small negative biases and the RMS errors of about 0.74° K for each area. For the full H6 disk, the bias is −0.1° K and the RMS error 0.74° K. The histogram of H6 SST minus the in situ SST for each area has a similar Gaussian shape with small negative skewness, and the monthly validation of H6 SST for each area is consistent with those for the whole period and the histograms
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Brisson, A., P. Le Borgne and A. Marsouin (2002): Results of one year of preoperational production of sea surface temperatures from GOES-8. J. Atmos. Oceanic Technol., 19, 1638–1652.
Evans, R. and G. Podestá (1998): Surface Temperature Algorithm Version 4.0 (February 6, 1998). http://www.rsmas.miami.edu/groups/rrsl/pathfinder/Algorithm/algo_index.html
Hosoda, K. (2010): Validation of Aqua/AMSR-E SST by comparison with in situ and satellite infrared measurements for long-term period: an EOF analysis to detect the errors. Rem. Sens. Environ. (submitted).
Kawai, Y. and H. Kawamura (2002): Evaluation of the diurnal warming of sea surface temperature using satellite-derived meteorological data. J. Oceanogr., 58, 805–814.
Kawai, Y. and H. Kawamura (2004): Spatial and temporal variations of model-derived diurnal amplitude of sea surface temperature in the western Pacific Ocean. J. Geophys. Res., 110(C8), Art. No. C08012.
Kawai, Y. and H. Kawamura (2005): Validation and improvement of satellite-derived surface solar radiation over the northwestern Pacific Ocean. J. Oceanogr., 61, 79–89.
Kawamura, H., S. Tanahashi and T. Takahashi (1998): Estimation of solar insolation over the Pacific Ocean off the Sanriku coast. J. Oceanogr., 54, 457–464.
Kawamura, H., H. Qin, F. Sakaida and R. Y. Setiawan (2010): Hourly sea surface temperature retrieval using the Japanese geostationary satellite, Multi-functional Transport Satellite (MTSAT). J. Oceanogr., 66, 61–70.
Kilpatric, K. A., G. P. Podesta and R. Evans (2001): Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res., 101, 9179–9197.
Kizu, S. (1995): A study on thermal response of ocean surface layer to solar radiation using satellite sensing. Doctoral Thesis, Tohoku University, 100 pp.
Kuroda, Y. (2001): TRITON: Present status and future plan. Japan Marine Science and Technology Center, report, 31 pp. On-line document available at: http://www.jamstec.go.jp/jamstec/TRITON/future/pdf/Status.pdf
Merchant, C. J., L. A. Horrocks, J. Eyre and A. G. O’Carroll (2006): Retrievals of sea surface temperature from infrared imagery: origin and form of systematic errors. Quart. J. Roy. Met. Soc., 132, 1205–1223.
Qin, H., H. Kawamura, F. Sakaida and K. Ando (2008): A case study of the tropical Hot Event in November 2006 (HE0611) using a geostationary meteorological satellite and the TAO/TRITON mooring array. J. Geophys. Res., 113, C08045, doi:10.1029/2007JC004640.
Shibata, A. (2004): AMSR/AMSR-E SST algorithm developments-removal of ocean wind effect. Italian J. Rem. Sens., 30/31, 131–142.
Tanahashi, S., H. Kawamura, T. Matsuura, T. Takahashi and H. Yusa (2000): Improved estimates of wide-ranging sea surface temperature from GMS S-VISSR data. J. Oceanogr., 56, 345–358.
Tanahashi, S., H. Kawamura, T. Takahashi and H. Yusa (2003): Diurnal variations of sea surface temperature over the wideranging ocean using VISSR on board GMS. J. Geophys. Res., 108(C7), 3216, doi:10.1029/2003JC001313.
Wick, G., J. J. Bates and D. J. Scott (2002): Satellite and skinlayer effects on the accuracy of sea surface temperature measurements from the GOES satellites. J. Atmos. Oceanic Technol., 19, 1834–1848.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kawamura, H., Qin, H., Hosoda, K. et al. Advanced sea surface temperature retrieval using the Japanese geostationary satellite, Himawari-6. J Oceanogr 66, 855–864 (2010). https://doi.org/10.1007/s10872-010-0069-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10872-010-0069-x