• 한국해안·해양공학회논문집
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• 제32권5호
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• pp.351-362
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• 2020

2003년 남해안에 내습한 태풍 매미에 의해 발생한 폭풍해일과 파랑을 JMA-MSM 예보기상자료, NCEP-CFSR 재분석 기상자료, ECMWF-ERA5 재분석 기상자료, JTWC의 최적경로를 이용한 기상자료를 이용하여 수치모의하고, 계산된 해일고를 전국 해안의 항만에서 관측된 폭풍해일 시계열 자료와 비교하였다. 폭풍해일과 동시에 발생하는 파랑에 대해서는 계산된 유의파고를 관측 자료와 비교하였다. 이 비교를 통해 태풍 매미에 대한 각종 기상장의 신뢰도를 평가하였다. 그 결과 JMA-MSM 기상자료가 가장 신뢰도가 높았고, JTWC의 최적경로를 이용한 기상자료도 상당히 우수하게 나타났다. ECMWF-ERA5 기상자료는 전반적으로 해일고나 파고의 크기가 작게 나타났으며, NCEP-CFSR 기상자료는 태풍 매미의 특정 경우에 대해 신뢰도가 가장 낮게 나타났다. 이 연구를 통하여 폭풍해일과 파랑을 추산하기 위해 신뢰도 높은 기상장이 필수적임을 알 수 있었다.

• 대기
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• 제20권3호
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• pp.247-260
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• 2010

A recent dramatic increase of natural hazards in the Korean peninsular (KP) due to typhoons have raised necessities for the accurate typhoon prediction. Ieodo ocean research station (IORS) has been constructed in June 2003 at the open ocean where typhoons pass frequently, aiming to observe typhoons before the landfall to the KP and hence to improve the prediction skill. This paper investigates the importance of measurements at the IORS in the typhoon research and forecast. Analysis of the best track data in the N. W. Pacific shows that about one typhoon passes over the IORS per year on the average and 54% of the KP-landfall typhoons during 59 years (1950-2008) passed by the IORS within the range of the 150-km radius. The data observed during the event of typhoons reveals that the IORS can provide useful information for the typhoon prediction prior to the landfall (mainland: before 8-10 hrs, Jeju Island: before 4-6 hrs), which may contribute to improving the typhoon prediction skill and conducting the disaster prevention during the landfall. Since 2003, nine typhoons have influenced the IORS by strong winds above 17m/s. Among them, the typhoon Maemi (0314) was the strongest and brought the largest damages in Korea. The various oceanic and atmospheric observation data at the IORS suggest that the Maemi (0314) has kept the strong intensity until the landfall as passing over warm ocean currents, while the Ewiniar (0603) has weakened rapidly as passing over the Yellow Sea Bottom Cold Water (YSBCW), mainly due to the storm's self-induced surface cooling. It is revealed that the IORS is located in the best place for monitering the patterns of the warm currents and the YSBCW which varies in time and space.

• 한국지구과학회지
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• 제30권3호
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• pp.294-304
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• 2009

북서태평양에서 발생한 태풍에 대해 발생 후 5일 동안 12시간 간격으로 태풍의 강도 및 진로를 예측할 수 있는 인공신경망 모델을 개발하였다. 사용되어진 예측인지는 CLIPER(발생 위치 강도 일자), 운동학적 파라미터(연직바람시어, 상층발산, 하층상대와도), 열적 파라미터(상층 상당온위, ENSO, 상층온도, 중층 상대습도)로 구성되어졌다. 예측인자의 특성에 따라 일곱개의 인공신경망 모델들이 개발되었으며, CLIPER와 열적 파라미터가 조합된(CLIPER-THERM) 모델이 가장 좋은 예측성능을 보였다. 이 CLIPER-THERM 모델은 강도 및 진로 모두에서 동절기보다 하절기에 더 나은 예측성능을 나타내었다. 또한 태풍의 발생이 아열대 서태평양의 남동쪽에 위치할수록 강도예측에서는 큰 오차를 보였고, 진로예측에서는 아열대 서태평양의 북서쪽에서 발생할수록 큰 오차를 보였다. 이후 인공신경망 모델의 예측성능을 검증하기 위해 같은 예측인자들을 이용하여 다중선형회귀모델을 개발하였으며, 결과로서 비선형 통계기법인 인공신경망 모델이 다중선형회귀모형보다는 더 나은 예측성능을 보였다.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2007년도 정기총회 및 학술발표대회
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• pp.327-331
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• 2007

23 tropical cyclones of tropical storm(TS) intensity or higher formed in the western North Pacific and the South China Sea in 2006. The total number is less than the 30-year $(1971{\sim}2000)$ average frequency of 26.7, Out of 23, 15 cyclones reached typhoon(TY) intensity, three severe tropical storm(STS) intensity, and five TS intensity. The tropical cyclone season in 2006 began in May with the formation of CHANCHU(0601). While convective activity was slightly inactive around the Philippines from late June to early August. In addition, subtropical high was more enhanced than normal over the south of Japan from May to early August. Consequently, most tropical cyclones formed over the sea east of the Philippines after late June, and many of them moved westwards to China. CHANCHU(0601), BILIS(0604), KAEMI(0605), PRAPIROON(0606) and SAOMI(0608) brought damage to China, the Philippines, and Vietnam. On the other hand, EWINIAR(0603) moved northwards and hit the Republic of Korea, causing damage to the country From late August to early September, convective activity was temporarily inactive over the sea east of the Philippines. However, it turned active again after late September. Subtropical high was weak over the south of Japan after late August. Therefore, most tropical cyclones formed over the sea east of the Philippines and moved northwards. WUKONG(0610) and SHANSHAN(0613) hit Japan to bring damage to the country. On the other hand, XANGSANE(0615) and CIMARON(0619) moved westwards in the South China Sea, causing damage to the Philippines, Thailand, and Vietnam. In addition, IOKE(0612) was the first namded cyclone formed in the central North Pacific and moved westwards across longitude 180 degrees east after HUKO(0224).

• 대기
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• 제31권3호
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• pp.311-326
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• 2021

To improve the predictability of high-impact weather phenomena around Seoul, where a larger number of people are densely populated, KMA conducted the intensive observation from 22 June to 20 September in 2020 over the Seoul area. During the intensive observation period (IOP), the dropsonde from NIMS Atmospheric Research Aircraft (NARA) and the radiosonde from KMA research vessel Gisang1 were observed in the Yellow Sea, while, in the land, the radiosonde observation data were collected from Icheon and Incheon. Therefore, in this study, the effects of radiosonde and dropsonde data during the IOP were investigated by Observing System Experiment (OSE) based on Korean Integrated Model (KIM). We conducted two experiments: CTL assimilated the operational fifteen kinds of observations, and EXP assimilated not only operational observation data but also intensive observation data. Verifications over the Korean Peninsula area of two experiments were performed against analysis and observation data. The results showed that the predictability of short-range forecast (1~2 day) was improved for geopotential height at middle level and temperature at lower level. In three precipitation cases, EXP improved the distribution of precipitation against CTL. In typhoon cases, the predictability of EXP for typhoon track was better than CTL, although both experiments simulated weaker intensity as compared with the observed data.