• 한국지구과학회지
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• 제40권5호
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• pp.476-486
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• 2019

This study examines the controlling role of synoptic disturbances on $PM_{10}$ spring variability in the Korean Peninsula by using empirical orthogonal function (EOF) and back trajectory analyses. Three leading EOF modes are identified, and a lead-lag analysis suggests that $PM_{10}$ variabilities be closely related to the synoptic weather systems. The first EOF shows the spatially homogeneous distribution of $PM_{10}$, which is influenced by travelling anticyclonic disturbance with negative precipitation and descending motion. The second and third modes exhibit the dipole structures of $PM_{10}$, being associated with propagating cyclones. Furthermore, the back-trajectory analysis suggests that the transport of pollutants by anomalous winds associated with synoptic disturbances also contribute to the altered $PM_{10}$ concentration. Hence, a substantial synoptic control should be considered in order to fully understand the $PM_{10}$ spatiotemporal variability.

• 대기
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• 제30권2호
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• pp.183-193
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• 2020

In this study, Yeongdong cold air damming (YCAD) cases that occur in winters have been selected using automatic weather station data of the Yeongdong region of Korea. The vertical and horizontal scales of YCAD were analyzed using rawinsonde and numerical weather model. YCAD occurred in two typical synoptic patterns such that low pressure and trough systems crossing and passing over Korea (low crossing type: LC and low passing type: LP). When the Siberian high does not expand enough to the Korean peninsula, low pressure and trough systems are likely to move over Korea. Eventually this could lead to surface temperature (3.1℃) higher during YCAD than the average in the winter season (1.6℃). The surface temperature during YCAD, however, was decrease by 1.3℃. The cold air layer was elevated around 120 m~450 m for LP-type. For LC-type, the cold layer were found at less than approximately 400 m and over 1,000 m, which could be thought of combined phenomena with synoptic and local weather forcing. The cross-sectional analysis results indicate the accumulation of cold air on the east mountain slope. Additionally, the north or northeasterly winds turned to the northwesterly wind near the coast in all cases. The horizontal wind turning point of LC-type was farther from the top of the mountain (52.2 km~71.5 km) than that of LP-type (20.0 km~43.0 km).

• 한국환경과학회지
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• 제23권12호
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• pp.1999-2014
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• 2014

A meteo-tsunami occurred along the coastline of South Korea on 31 March 2007, with an estimated maximum amplitude of 240 cm in Yeonggwang (YG). In this study, we investigated the synoptic weather systems around the Yellow sea including the Bohai Bay and Shandong Peninsula using a weather research and forecast model and weather charts of the surface pressure level, upper pressure level and auxiliary analysis. We found that 4-lows passed through the Yellow sea from the Shandung Peninsula to Korea during 5 days. Moreover, the passage of the cold front and the locally heavy rain with a sudden pressure change may make the resonance response in the near-shore and ocean with a regular time-lag. The sea-level pressure disturbance and absolute vorticity in 500 hPa projected over the Yellow sea was propagated with a similar velocity to the coastline of South Korea at the time that meteo-tsunami occurred.

• Water Engineering Research
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• 제3권2호
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• pp.123-134
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• 2002

A developed Quantitative Flood Forecasting (QFF) model was applied to the mid-Atlantic region of the United States. The model incorporated the evolving structure and frequency of intense weather systems of the study area for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters associated with synoptic atmospheric conditions as Input. Here, we present results from the application of the Quantitative Flood Forecasting (QFF) model in 2 small watersheds along the leeward side of the Appalachian Mountains in the mid-Atlantic region. Threat scores consistently above 0.6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 40% and up to 55 % were obtained.

• 대한지리학회지
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• 제45권3호
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• pp.319-341
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• 2010

본 연구에서는 지난 36년 동안(1973/74-2008/09) 우리나라 겨울철 강설자료와 북반구 대기순환장을 분석하여 대설 유형별 지상 종관 기후 패턴의 특징을 밝히고자 한다. 동아시아 기압 배치 및 대설 지역을 복합적으로 고려하여 우리나라 대설 발생 사례를 크게 4가지 종관 기후 범주, 세부적으로 17가지 유형으로 세분하였다. 지상 종관 기후 자료 분석에서 각 대설 유형마다 한반도 주변에 나타나는 기압과 바람벡터 아노말리 핵들의 위치 및 강도 차이가 뚜렷하게 나타난다. 특히, 시베리아 고기압의 장출과 이동성 저기압의 통과 여부가 이러한 차이점에 중요한 영향을 미친다. 반구 규모 종관 기후 패턴으로 북극진동의 음의 모드 또한 한반도 겨울철 대설 발생 증가에 영향을 미친다. 이러한 종관 기후 분석 결과들은 단기 또는 계절 대설 예보 향상에 기초 자료로 활용 될 수 있다.

• 한국환경과학회지
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• 제25권6호
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• pp.853-864
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• 2016

Meteo-tsunamis are tsunamis that are typically caused by strong atmospheric instability (e.g., pressure jumps) in low pressure systems, but some meteo-tsunamis in winter can be caused by local atmospheric instability in high pressure systems (e.g., the Siberian High). In this study, we investigated a meteo-tsunami event related to a high pressure system that occurred during winter on the Yellow Sea in 2005. Sea level data from tidal stations were analyed with a high-pass filter, and we also performed synoptic weather analyses by using various synoptic weather data (e.g., surface weather charts) collected during the winter season(DJF) of 2005. A numerical weather model (WRF) was used to analyze the atmospheric instability on the day of the selected event (21 Dec. 2005). On the basis of the results, we suggest that the meteo-tsunami triggered by the high pressure system occurred because of dynamic atmospheric instability induced by the expansion and contraction of the Siberian High.

• 대기
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• 제27권2호
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

• 대한지리학회지
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• 제50권1호
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• pp.1-21
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• 2015

본 연구에서는 지난 40년 동안(1973~2012)의 우리나라 기상청 산하 61개 지점 일기온 자료와 NCEP/NCAR 재분석 자료를 바탕으로 우리나라 지역별 겨울철 극한저온현상 발생 시 동아시아 영역의 종관 기후 패턴의 특징을 밝히고자 하였다. 일최고기온과 일최저기온 하위 10 퍼센타일 기준으로 정의된 겨울철 극한저온현상은 주로 겨울철 전반기(12월 초순~1월 중순)에 2~7일 간격으로 우리나라 전역 또는 주요 산맥 기준 동서지역으로 구분되어 발생함을 알 수 있다. 해수면기압과 바람벡터 등의 지상 종관 자료 합성장 분석에 따르면 총 13개로 구분되는 우리나라 겨울철 극한저온현상 발생 공간 패턴은 산맥뿐만 아니라 시베리아 고기압과 알류샨 저기압의 상대적인 확장 범위와 강도와 밀접한 관련성이 있음을 알 수 있다. 대류권 중층(500 hPa) 종관기후도 분석에 따르면, 블러킹 형태의 저기압이 상층 찬 공기를 고위도 지역에서 한반도로 이류시킬 때 우리나라에 겨울철 극한저온현상이 발생하기에 적합한 조건이 형성됨을 알 수 있다. 이러한 결과들은 지역규모 이상의 동아시아 겨울철 극한저온현상 예보를 향상시키기 위해 시베리아 고기압, 알류샨 저기압, 상층 블러킹 등의 종관 기후 요소를 모니터링하는 것이 중요함을 가리킨다.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.85-96
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• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.23-30
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• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.