首页> 外文会议>Remote sensing of the coastal ocean, land, and atmosphere environment >Temporal and spatial Varibility of SST and LST concentrations in the Korea sea using empirical orthogonal function (EOF) analysis of remote sensing data.
【24h】

Temporal and spatial Varibility of SST and LST concentrations in the Korea sea using empirical orthogonal function (EOF) analysis of remote sensing data.

机译:使用遥感数据的经验正交函数(EOF)分析,韩国海中SST和LST浓度的时空变化。

获取原文
获取原文并翻译 | 示例

摘要

Global warming has significant effect on the sea surface temperature. Sea surface temperature is an important parameter for the quantitative studies of monitoring the Earth's environment changes. Determination and analysis of sea surface temperature from satellite data has been the main focus in oceanographic research and thus needs quantitative analysis in its retrievals. We used EOF method applying SST. Seasonal and interannual variability of Sea surface temperature (SST) and Land surface temperature (LST) concentration in the korea Sea was examined using Empirical Orthogonal Function (EOF) analysis of data obtained by the NOAA from 1999 to 2009.rnIn the result of SST, The first EOF mode explains 55.7% of the variability, the second EOF mode explains 21.5%, and the third EOF mode explains 21.5%. As a result of LST, The first EOF mode explains 99.7% of the variability, the second EOF mode explains 2.5%, and the third EOF mode explains 0.9. It shows commom tendency of interannual variability with the period of 3-4 years at most of the locations. SST was higher in the 2004's and early 2006's and lower in the 2003. The pattern of the interannual variability of SST was similar to that of air temperature. Increasing trend of SST was obvious that it was larger eastern more than western. In the Future, we expect to analyse, collect with a various satellite data and in situ data for long time.
机译:全球变暖对海面温度有重大影响。海面温度是监测地球环境变化的定量研究的重要参数。从卫星数据确定和分析海面温度一直是海洋学研究的重点,因此在其检索中需要进行定量分析。我们使用了SST的EOF方法。使用经验正交函数(EOF)分析NOAA从1999年至2009年获得的数据,检查了韩国海域海表温度(SST)和陆表温度(LST)浓度的季节性和年际变化。第一个EOF模式解释了55.7%的可变性,第二个EOF模式解释了21.5%,第三个EOF模式解释了21.5%。 LST的结果是,第一个EOF模式解释了99.7%的变异性,第二个EOF模式解释了2.5%的差异,第三个EOF模式解释了0.9%的变异性。在大多数位置,它显示了年际变化的共同趋势,为3-4年。 SST在2004年和2006年初较高,而在2003年较低。SST年际变化的模式与气温相似。 SST的增长趋势显而易见,即东部比西部更大。在未来,我们希望能够长期分析,收集各种卫星数据和原位数据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号