2010Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

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

Suk Yoon, Joo-Hyung Ryu, Yu-Hwan Ahn, Hong Sik Min, Joong‐Sun Won

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Abstract

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. In 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.

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What this paper is about

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. In 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.

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Available abstract

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. In 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.

Key concepts: Empirical orthogonal functions, Sea surface temperature, Environmental science, Climatology, Mode (computer interface), Satellite, Spatial variability, Remote sensing

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