• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.263-265
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• 2005

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.80-80
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• 2010

본 연구에서는 식물의 개화시기가 기온에 민감하게 반응한다는 것에 근거하여 54개의 기상관측지점을 선택하고 1988년부터 2008년까지 21년간 벚꽃 개화일의 시계열적 변화를 분석하였고 이들과 3월 평균기온과의 상관관계를 조사하였다. 그리고 엘니뇨와 라니냐 시기에 기온과 벚꽃개화일의 변화 경향과 도시화에 의한 기온변화가 벚꽃 개화일에 미치는 영향을 대도시와 교외도시와의 비교를 통해 알아보았다. 또한 경험직교함수(EOF) 분석을 통하여 벚꽃개화일의 시 공간 분포를 알아보았다. 그 결과는 다음과 같다. 첫 번째, 3월 평균기온 분석을 통해 1988-2008년 사이에 한반도의 기온은 평균적으로 $0.13^{\circ}C$ 증가하였음을 확인할 수 있다. 그리고 평균적으로 남해안이 동해안과 서해안보다 평균기온이 높게 나타났으며, 서해안이 가장 낮은 평균기온을 보였다. 그리고 내륙지역의 경우 위도에 따라 평균기온이 달랐으며, 위도가 낮아질수록 평균기온이 크게 나타났다. 하지만 모든 지점에서 유의하지는 않았다. 두 번째, 벚꽃개화일의 변화를 보면, 개화일은 시간이 지날수록 빨라지는 경향을 보였다. 그리고 벚꽃개화일의 분포를 보면 위도가 낮은 지역이 개화일이 빨랐으며, 해안지역 중에서는 남해안, 동해안, 서해안 순이었다. 세 번째, 2월, 3월, 4월의 평균기온과 벚꽃 개화일의 상관관계 분석결과 3월 평균기온이 벚꽃개화일과 상관관계가 가장 높았다. 네 번째, 엘니뇨가 발생한 해에는 한반도의 기온이 상승하는 경향을 보였으며, 동시에 벚꽃개화일도 빨라졌다. 다섯 번째, 대도시가 교외도시에 비해서 기온상승이 높았으며, 벚꽃개화일도 빠르게 나타났다. 마지막으로, 경험직교함수(EOF) 분석 결과 3월 평균기온과 벚꽃 개화일의 1mode 시계열 패턴이 비슷하다는 것을 확인하였다. 또한, 1996년을 기준으로 이전에는 개화일이 느려지고, 이후에는 빨라지는 패턴을 보였다.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.469-482
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• 2005

China Coastal Waters (CCW) usually appears in the seas surrounding Jeju Island annually(June to October) and is very pronounced in August. Generally, low-salinity water appears to the western seas of Jeju Island from June through October and gradually propagates to the eastern seas, where CCW meets the Tsushima Current. Empirical orthogonal function (EOF) analysis of SLAs and SSTs indicated that the variance in SLAs and SSTs was $95.01\%$(the first mode to third mode) and $98.09\%$(the first mode), respectively. The PSD of the western waters for the first mode of EOF analysis of SLAs was stronger than that of the eastern waters because of the influence of CCW. The PSD for the EOF analysis of SSTs was similar in all areas (the Yangtze Estuary and the seas to the west and east of Jeju Island), with a period of approximately 260 days.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.119-122
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• 2005

정규식생지수는 일반적으로 식생의 활력도를 나타나는 지표로서 널리 사용되고 있다. 최근에는 정규식생지수가 특정지역의 강우량과 온도의 계절 및 경년변화와 어떤 상관관계를 가지며 기후변화는 식생지수에 어떠한 영향을 미치는지 등에 관한 연구가 활발히 수행되고 있다. 본 연구에서는 1981년부터 2001년까지의 NOAA/AVHRR 영상으로부터 계산된 남한지역 정규식생지수의 주성분 분석을 통해 자료의 공간변화패턴을 분석하고 경험적 직교함수를 이용하여 시간적 변화 양상을 파악하였다. 분석결과 정규식생지수의 공간변화도는 첫 주성분에 의하여 약 $60\%$ 정도 설명되어지며 첫 주성분은 남한지역의 지형 자료 패턴을 따르고 두 번째 주성분은 전체 변화도의 약 $17\%$를 나타내며 강한 남북기울기를 보여주는 것은 계절변화와 상관한 위도변화에 따른 정규식생지수의 변화를 나타낸다. 그리고 소양강댐 및 안동댐 유역의 정규식생지수, 강우량 및 유입량 상관관계 분석 결과 정규식생지수의 계절변화와 경년변화는 강우량의 변화에 그리 민감하지 않은 것으로 나타났다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.69-77
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• 2003

Measurements of backscattered intensity were made over a shallow water using 300 ㎑and 1200 ㎑ bottom mounted ADCP (Acoustic Doppler Current Profiler) to determine the temporal variability of vertical distribution of high-frequency volume scattering strength (Sv). The variability of Sv in relatively deep water column(85 m and 113 m was due to the daily vertical migration, probably of larger zooplankton. However it was not found with 1200㎑ data at shallow water column. From the empirical orthogonal function (EOF) analysis using 1200㎑ data, the vertical distribution of the first mode eigenvectors of Sv is characterized by the presence of the maximum values near the bottom of the water.

• Journal of the Korean earth science society
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• v.26 no.3
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• pp.199-217
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• 2005

Using monthly total ozone data obtained from a Total Ozone Mapping Spectrometer (TOMS) onboard the Nimbus-7 and Earth Probe satellite, this study examined the trend in the total amount of global ozone during two periods: from 1979-1992 [Early period] and 1997-2002 [Latter period]. The Annual average of total ozone during the Early period was globally reduced by about 10 DU compared to the amount during the Latter, except in some areas between the equator and 20 N. Global trends of total ozone showed a decrease of -6.30 DU/decade during 1979-1992, and an increase of 0.12 DU/decade during 1997-2002. Its enhancement during the Latter period was especially noticeable in tropical areas. The EOF analyses of total ozone from this period indicated signs of temporal/spatial variability, associated with the phenomena of Quasi-Biennial Oscillation (QBO), Quasi-Triennial Oscillation (QTO), El Nino Southern Oscillation (ENSO), and volcanic eruption. Seasonal profiles of tropospheric ozone in the tropics obtained from ozonesondes, showed the spatial pattern of zonal wavenumber one. Overall, this study may be useful in analyzing possible causes in the variations of statospheric and tropospheric ozone.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.91-94
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• 2006

China Coastal Waters (CCW) usually appears in the seas surrounding Jeju Island annually(June to October) and is very pronounced in August. Generally, low-salinity water appears to the western seas of Jeiu Island from June through October and gradually propagates to the eastern seas, where CCW meets the Tsushima Current. Empirical orthogonal function (EOF) analysis of SLAs and SSTs indicated that the valiance in SLAs and SSTs was 95.05%(the first mode to third mode) and 98.09%(the first mode), respectively The PSD of the western waters for the first mode of EOF analysis of SLAs was stronger than that of the eastern waters because of the influence of CCW. The PSD for the EOF analysis of SSTs was similar in all areas (the Yangtze Estuary and the seas to the west and east of Jeju Island), with a period of approximately 260 days.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.30 no.2
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• pp.188-202
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• 1997

The seasonal variability of the sea surface winds over the last Sea of Korea (Japan Sea) is investigated by means of empirical orthogonal function (EOF) analysis. The combined representation of fields of three climatic variables by empirical orthogonal functions is discussed. The eigenvectors are derived from daily sea level pressure, wind speed and 10-day mean sea surface temperature (SST) during 15 years $(1978\~1992)$. The spatial patterns of the mean pressure are characterized by the high pressure in the western part and the low pressure in the eastern part. The spatial distribution of the standard deviation (SD) of pressure are characterized by max SD of 6.6 mb near the Vladivostok, and minima along the coast of the Japan. In Vladivostok, the maxima of SD of SST and south-north wind (WV) were also occurred. The representation of fields of individual meteorological variables by EOF shows that the first mode of the west-east wind (WU) explain over $47.3\%$ of the variance and the second mode of WU represents $30\%$. Especially, the first mode of the WV explain $70.9\%$ of the variance and their time series coefficients show 1-cpy, 0.5-cpy frequency spectrum. The spatial distribution of the first mode eigenvectors of SST are characterized by maximum near Vladivostok. The combined representation of fields of several variables (pressure, wind, SST) reveals that the first mode magnitudes of the variance of the combined eigenvectors (WU-PR) are increased. By means of this result, the 1-year peak and the 6-months peak are remarkable. In the three combined patterns (wind, pressure, SST), the second mode of the eigenvector (wind) is affected by the SST. Their time coefficients of the first mode show noticeable 1-year peak. The spectral analysis of the second mode shows broad seasonal signal with the period of 4-months and a significant peak of variability at 3-month period.

• 한국해양학회지
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• v.24 no.1
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• pp.1-14
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• 1989

This study analyzes the interannual periodicity by using the statistical techniques of probability, spectral analysis, empirical orthogonal function analysis (EOF), and coherency analysis. The data base for this study is the time series of 1971-1985 temperature, salinity in the southern waters of the East Sea, 1960-1986 mean sea level at Pusan and Izuhara, and 1960-1986 sea level atmospheric pressure at Pusan. The appearances of anomalous temperatures higher and lower than 15-year mean monthly average with one standard deviation are about 30% of total data. The significant interannual period for temperature, salinity and sea level fluctuation is 36.6, and 23.3 months. The empirical orthogonal function analyses show that the 1st mode of the EOFs is responsible for more than 90% of total variance of the surface temperature variations, while in near-bottom waters, the relative importance of the higher EOF modes is much greater explaining more than 30% of total variance. The coherency between normalized temperatures and salinities is significant at the interannual period of 36.6 and 21.3 months.

• Journal of the Korean earth science society
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• v.32 no.1
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• pp.33-45
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• 2011

The characteristics and vertical structure of the rainfall are examined in terms of rain types using TRMM (Tropical Rainfall Measuring Mission) PR (Precipitation Radar) data during the JJA period of 2002-2006 over three different regions; midlatitude region around the Korean Peninsula (EA1), subtropical East Asia (EA2), and tropical East Asia (EA3). The convective rain fraction in the EA1 region is 12.2%, which is smaller by 6% than those in the EA2 and EA3 regions. EA1 shows less frequent convective rain events, which are about 0.5 times as many as those in EA3. EA1 produces the mean convective rain rate of 10.4 mm/h that is about 40% larger than EA2 and EA3 while all regions have similar mean stratiform rain rate. The relationships between storm height and rain rate indicate that the rain rate is proportional to the storm height. Based on the vertical structure of radar reflectivity, EA1 produces deeper and stronger convective clouds with higher rain rate compared to the other regions. In EA3, radar reflectivity increases distinctly toward the land surface at altitude below 5 km, indicating more dominant coalescence-collision processes than the other regions. Furthermore, the bright band of stratiform rain clouds in EA3 is very distinct. In convective rain clouds, the first EOFs of radar reflectivity profiles are similar among the three regions, while the second EOFs are slightly different. The larger variability exists at upper layers for EA1 while it exits at lower levels for EA3.