본 연구에서는 미국 해양 대기청(NOAA)의 NOAA-20 위성에 장착된 차세대 고해상도 복사계인 VIIRS로부터 산출된 적외 해수면온도의 자료를 수집하고, 실측 자료와의 일치점을 생산하여 한반도 주변 해역에서의 정확도를 검증 하였다. 2020년 5월부터 2023년 6월까지 최근 3년간의 자료를 사용하였고, 총 75,700개의 일치점을 생산하였다. NOAA-20/VIIRS 해수면온도는 표층 뜰개 부이 관측 해수면온도와 비교해보았을 때 약 0.52K의 평균 제곱근 오차와 – 0.12 K의 평균 편차를 보였고, 이는 전구 해역을 대상으로 한 기존의 정확도 검증 연구 결과값을 상회하는 수치였다. NOAA-20 해수면온도의 오차 특성 분석 결과 겨울과 봄에는 음의 편차가, 여름철에는 양의 편차를 보이는 계절적 특 성이 나타났으며, 15-16시에 최대 평균 제곱근오차, 최대 양의 편차 및 22-24시에 최소 평균제곱근오차, 최소 편차를 가지는 일간 변화를 보였다. 이외에도 NOAA-20 해수면온도의 오차는 풍속, 위성 천정각, 연안으로부터의 거리, 해수면 온도의 공간 구배 크기에 영향을 받아 변동하는 특성이 나타났다. 전반적으로 위성 해수면온도의 편차값은 14ms1 이 상의 풍속 범위에서 풍속이 커질수록 양의 방향으로 증가하는 경향을 보였으며, 5 m s1 이하의 낮은 풍속 범위에서는 풍속이 약해질수록 낮/밤 자료에 따라 각각 양의 방향, 음의 방향으로 편차가 증가하였다. 위성 천정각이 커질수록 해 수면온도의 오차 범위는 급격하게 증가하였으며, 연안에 근접할수록 (<300 km) 위성 해수면온도의 오차가 증가하는 것 을 확인할 수 있었다. 해수면온도의 공간 구배는 그 크기가 커질수록 위성 해수면온도의 평균 제곱근 오차를 증폭시키 는 경향이 나타났다. 국지적인 해역에서의 위성 해수면온도 정확도 및 오차 특성은 전구 해역에서의 전반적인 특성과는 다르게 나타날 수 있다는 점을 고려할 때 본 연구는 향후 한반도 주변해에서 VIIRS 해수면온도를 활용하기 위한 선행 연구로 해수면온도 오차의 변동 특성 및 분포에 대한 깊은 이해가 필요함을 시사한다.

In this study we develop a set of solar proton event (SPE) forecast models with NOAA scales by Multi Layer Perceptron (MLP), one of neural network methods, using GOES solar X-ray flare data from 1976 to 2011. Our MLP models are the first attempt to forecast the SPE scales by the neural network method. The combinations of X-ray flare class, impulsive time, and location are used for input data. For this study we make a number of trials by changing the number of layers and nodes as well as combinations of the input data. To find the best model, we use the summation of F-scores weighted by SPE scales, where F-score is the harmonic mean of PODy (recall) and precision (positive predictive value), in order to minimize both misses and false alarms. We find that the MLP models are much better than the multiple linear regression model and one layer MLP model gives the best result.

The trends of sea surface temperature (SST) variations derived from NOAA satellite data in the Northeast Asian Waters (NAW) were quantified using NOAA satellite data for 19 years (1990~2008). The annual mean SSTs were generally increased in the NAW. However, the SST was decreased in some areas of the East Sea in the NAW. The areas in the East Sea were coincided with the same places which SST was decreased in winter season. The annual amplitudes of SST were increased in the northern parts of the East China Sea, the Korean Straits and the southwestern parts of the East Sea. However, the annual amplitudes of SST were decreased in the other waters. The SST was increased in the southwestern parts of the Yellow Sea in winter but it was decreased in summer season for 19 years (1990~2008). The SST variations in the northwestern parts of the East Sea (NWES) in summer and winter seasons were increased at the same period of time for 19 years (1990~2008). The rates of SST rise in the NWES in winter were higher than those of summer season. Therefore, the annual amplitude in the NWES was decreased.

본 연구에서는 동북아시아 NOAA AVHRR 위성관측 16년간(1990-2005) 해양표면 수온영상을 이용하여 에러 값 제거와 결측 자료 보완을 위하여 마르코프 계수를 결정하였고, 이 값에서 현재 수온평년 값을 더하여 구름 없는 해양표면수온 생성 기법을 제시하였다. 마르코프 연쇄 모델의 결과에 의하면, 마르코프 계수는 해류가 강한 쿠로시오 해역 등이 해류가 약한 동해 북서부의 대부분 해역과 동중국해보다 그 계수가 상대적으로 낮게 나타났다. 평균 수온의 변동은 봄과 가을이 겨울과 여름에 비하여 분산이 크게 나타났고, 계절별 일간 수온 차이도 수온의 계절적 변동이 큰 봄과 가을이 여름과 겨울에 비하여 큰 지역적인 차이를 보였다. 그 지역적인 분포는 봄과 가을의 경우 전 해역의 대륙 인접부에서 대부분 크게 나타났고, 동해 극전선 남부해역과 쿠로시오해역에서는 난류에 의한 열수송으로 일간 수온의 차이가 작았다.

본 연구에서는 NOAA 위성관측 수온영상을 이용하여 여기에 포함된 에러 값 제거와 결측 자료를 보완하기 위하여 16년간 (1990-2005) 자료를 이용하여 마르코프 계수를 결정하였고, 이 값에서 오늘의 수온평년 값을 더하여 구름 없는 해양표면수온 생성 기법을 제시 하였다. 마르코프 모델의 결과에 의하면, 마르코프 계수는 해류가 강한 쿠로시오 해역 등이 해류가 약한 동해 북서부의 대부분 해역과 동중국해 보다 그 계수가 상대적으로 높게 나타났다. 평균 수온의 변동은 봄과 가을이 겨울과 여름에 비하여 분산이 크게 나타났고, 계절별 일간 수온 차 이도 수온의 계절적 변동이 큰 봄과 가을이 여름과 겨울에 비하여 큰 지역적인 차이를 보였다. 그 지역적인 분포는 봄과 가을의 경우 전 해역의 대륙 인접부에서 대부분 크게 나타났고, 동해 극전선 남부해역과 쿠로시오해역에서는 난류에 의한 열수송으로 일간 수온의 차이가 작았다.

Horizontal scale and movement of tidal front zone, front in the western regions of Korea in summer are studied in conjunction with numerical model and NOAH-11 satellite data analysis(AVHRR multi -channel sea surface temperature). In numerical model result, tidal mixing is dominant in the southeast region of Hwanghae, near field of Taean, Kyunggi bay, near field of Jangsan cape, Seoan bay, mid-east Chinese coast. But the results of the NOAH infra-red image analysis show that low surface temperature by tidal mixing is clear in the southeast region of Hwanghae, near field of Taean, near field of Jangsan cape but not in the Kyunggi bay, Seoan bay and mid- east Chinese coast in August and September, temperature gradient of frontal zone in the southwest region of Hwanghae is 0.05°∼0.1℃/㎞ and tidal mixing is dominant in the near field of Maenggal kundo and Hajodo and low surface temperature extends southwesrward. Early in August, west-east front(0.2°∼0.6℃/㎞) on the south region of Jindo moves northward and persists at east half on the joining line of Jindo and Sohuksando late September. The axis of front on the west region of Jindo is northeast~sorthwest early in August and moves westward until late September The tidal mixing in the near field of Jangsan cape is dominant in the region between Jangsan cape and Baengyougdo early in August and between Baengyougdo and Daechungkundo in late September. The axis of front on the west region of Jangsan cape is south-north and its temperature gradient is 0.2°∼0.4℃/㎞.

El Niño is the largest fluctuation in the climate system, and it can lead to effects influencing humans all over the world. An El Niño occurs when sea surface temperatures in the central and eastern tropical Pacific Ocean become substantially higher than average. We investigated the change in sea surface temperature in the Pacific Ocean during the El Niño period of 2015 and 2016 using the advanced very-high-resolution radiometer (AVHRR) of NOAA Satellites. We calculated anomalies of the Pacific equatorial sea surface temperature for the normal period of 1981–2010 to identify the variation of the 2015 El Niño and warm water area. Generally, the warm water in the western tropical Pacific Ocean shifts eastward along the equator toward the coast of South America during an El Niño period. However, we identified an additional warm water region in the Niño 1+2 and Peru coastal area. This indicates that there are other factors that increase the sea surface temperature. In the future, we will study the heat coming from the bottom of the sea to understand the origin of the heat transport of the Pacific Ocean.

In an effort to examine the Regional Atmospheric Modeling System (RAMS ver. 4.3) to the initial meteorological input data, detailed observational data of NOAA satellite SST (Sea Surface Temperature) was employed. The NOAA satellite SST which is currently provided daily as a seven-day mean value with resolution of 0.1 o grid spacing was used instead of the climatologically derived monthly mean SST using in RAMS. In addition, the RAMS SST data must be changed new one because it was constructed in 1993. For more realistic initial meteorological fields, the NOAA satellite SST was incorporated into the RAMS-preprocess package named ISentropic ANalysis package (ISAN). When the NOAA SST data was imposed to the initial condition of prognostic RAMS model, the resultant performance of near surface atmospheric fields was discussed and compared with that of default option of SST. We got the good results that the new SST data was made in a standard RAMS format and showed the detailed variation of SST. As the modeling grid became smaller, the SST differences of the NOAA SST run and the RAMS SST43 (default) run in diurnal variation were very minor but this research can apply to further study for the realistic SST situation and the development in predicting regional atmospheric field which imply the regional circulation due to differential surface heating between sea and land or climatological phenomenon.

융설 모형을 이용하여 융설 기간 동안의 하천유출량을 모의하기 위해서는 융설 관련 매개변수의 정립이 반드시 필요하다. 우리나라의 경우 관측 자료의 부족으로 인하여 적설분포면적, 적설심, 적설분포면적 감소곡선과 같은 융설 관련 매개변수의 추출이 불가능하였다. 본 연구에서는 1997년부터 2003년까지의 겨울철(11월-4월) NOAA/AVHRR 위성영상을 이용하여 한반도의 적설분포도를 추출하고 기상청의 69개소 유인지상기상관측소의 기상자료 중 최심적설심 자료

Satellite data, with sea surface temperature(SST) by NOAA and sea level(SL) by Topex/poseidon, are used to estimate characteristics on the variations and correlations of SST and SL in the East Asian Seas from January 1993 through May 1998. We found that there are two climatic characteristics in the East Asian seas: the oceanic climate, the eastern sea of Japan, and the continental climate, the eastern sea of China, respectively. In the oceanic climate, the variations of SL have the high values in the main current of Kuroshio and the variations of SST have not the remarkable seasonal variations because of the continuos compensation of warm current by Kuroshio. In the continental climate, SL has high variations in the estuaries(the Yellow River, the Yangtze River) with the mixing the fresh water and the saline water in the coasts of continent and SST has highly the seasonal variations due to the climatic effect of continents. In the steric variations of summer, the eastern sea of Japan, the East China Sea and the western sea of Korea is increased the sea level about 10∼20㎝. But the Bohai bay in China have relatively the high values about 20∼30㎝ due to the continental climate. Generally the trends of SST and SL increased during all periods. That is say, the slopes of SST and SL is presented 0.29℃/year and 0.84㎝/year, respectively. The annual and semi-annual amplitudes have a remarkable variations in the western sea of Korea and the eastern sea of Japan. In the case of the annual peaks, there appeared mainly in the western sea of Korea and the eastern sea of Japan because of the remarkable variations of SL associated with Kuroshio. But in the case of the semi-annual peaks, there appeared in the eastern sea of Japan by the influence of current, and in the western sea of Korea by the influence of seasonal temperature, respectively. From our results, it should be believed that SST and SL gradually increase in the East Asian seas concerning to the global warming. So that, it should be requested to the international co-operation against to the change of the abnormal climate.

The relationship between the distribution of sea surface temperature(SST) and dinoflagellate(Cochlodinium polykrikoides) bloom areas were studied. The SST data were derived from the infrared channels of AVHRR(Advanced Very High Resolution Radiometer) sensor on NOAA(National Oceanic and Atmospheric Administration) 12 and 14 satellites during 1995-1998. The initial water temperature at C. polykrikoides bloom was about 21℃ at the coastal areas of the South Sea and along the shore of the East Sea of Korea during the summer season of 1995. The northern limit of red tides was coincident with that of 21℃ isothermal line in the East Sea. The red tides that initially bloomed at the coast of Pohang on September 21, 1995 moved to the coast of Uljin on September 26, 1995. The skipped appearance of the red tides in the areas between Pohang and Uljin was due to the East Korean Warm Current, which was moving offshore from Pohang to approach to Uljin. The cold water which was formed by tidal front in the western coast of the South Sea and by upwelling water from deep layer in the southeastern coast of the Korean peninsula played a role in blocking the spreading of red tides during summer season in 1997 and 1998. In conclusion, the distribution of red tides appeared to be dependent on the initial water temperature at red tides bloom. The SST at the red tides varied from 21℃ to 25℃ ; 21℃, 23℃, 24℃ and 24-25℃ in 1995, 1996, 1997 and 1998, respectively.

This study is to evaluate the overall NPP(Net Primary Production) distribution in the Korean Peninsula from the satellite data(NOAA/AVHRR). This has been done using the linear relationship between the natural vegetation condition and the NPP. The NPP of natural vegetation increases proportional to the annual net radiation(Rn), where radiative dryness index(RDI) is a proportional constant connecting Rn to NPP. Normalized Difference Vegetation Index(NDVI) is used for monitoring vegetation change, and iNDVI (integrated NDVI) for annual analysis. The iNDVI has a close relation to Rn and NPP, which can be used effectively for estimating NPP distribution of where the meteorological data is unavailable such as North Korea. The NPP distribution of the Korean Peninsula was estimated based on the model.