谷歌浏览器插件
订阅小程序
在清言上使用

Advanced sea surface temperature retrieval using the Japanese geostationary satellite, Himawari-6

Journal of Oceanography(2011)

引用 6|浏览2
暂无评分
摘要
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
更多
查看译文
关键词
Himawari-6,SST retrieval,advanced algorithm,diurnal SST variation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要