A heavy (93 mm hr−1) rainfall event accompanied by lightning occurred over Gangneung in the Yeongdong region of South Korea on August 6, 2018. This study investigated the underlying mechanism for the heavy rainfall event by using COMS satellite cloud products, surface- and upper-level weather charts, ECMWF reanalysis data, and radiosonde data. The COMS satellite cloud products showed rainfall exceeding 10 mm hr−1, with the lowest cloud-top temperature of approximately −65oC and high cloud optical thickness of approximately 20-25. The radiosonde data showed the existence of strong vertical wind shear between the upper and lower cloud layers. Furthermore, a strong inversion in the equivalent potential temperature was observed at a pressure altitude of 700 hPa. In addition, there was a highly developed cloud layer at a height of 13 km, corresponding with the vertical analysis of the ECMWF data. This demonstrated the increased atmospheric instability induced by the vertical differences in equivalent potential temperature in the Yeongdong region. Consequently, cold, dry air was trapped within relatively warm, humid air in the upper atmosphere over the East Sea and adjacent Yeongdong region. This caused unstable atmospheric conditions that led to rapidly developing convective clouds and heavy rainfall over Gangneung.

2011년 7월 26일 서울은 장마에 동반된 기록적인 대류성 집중호우로 인해 약 2천5백억 원 이상의 재산피해와 57명(사망자)의 인명손실이 발생되었고, 2012년 8월 27일 15호 태풍 볼라벤에 동반된 집중호우로 광주광역시에는 보다 약한 집중호우와 강풍을 동반하여 피해는 상대적으로 적게 발생시켰다. 위의 사례에 대해 KLAPS(기상청 국지분석 및 예측시스템)을 사용하여 집중호우 시 다른 물리적 요소들에 의한 중규모 과정들의 조사 및 분석을 수행하였다. 이것은 레이더관측과 천리안 위성관측 자료로부터 강우강도를 도출하는데 호조건의 전형적인 중규모 시스템이기 때문에 선택되었으며, 두 사례는 모두 집중호우 발생에 좋은 환경임을 보였다. 2011년 장마에 동반되어 서울에 나타난 사례에서 레이더와 천리안의 정량적인 강우강도를 지상강우계 관측과 비교했을 때, 최대 관측값이 85 mm/hr 이상이 나타난 시점에 비해 약 50 mm/hr 이상이 과소 추정되는 차이가 나타났으나, 레이더 강우강도는 35 mm/hr의 차이와 천리안 강우강도는 60 mm/hr의 차이를 보였다. 그러나 2012년 8월 27일 15호 태풍 볼라벤에 동반되어 광주광역시에 나타난 강우강도와 지상강우강도의 경향은 위의 사례와 유사하게 나타났으며, 정량적인 강우강도 차이는 최대 관측값이 17 mm/hr 이상이 나타난 시점에 비해 약 10 mm/hr 이상이 과소 추정되는 차이가 나타났으나, 레이더 강우강도는 5 mm/hr의 차이와 천리안 강우강도는 10 mm/hr의 차이를 보였다. 이것은 태풍 볼라벤에 의한 집중호우가 상대적으로 약했기 때문이었다. 두 사례에 대해 레이더 강우강도와 천리안 강우강도는 지상강우강도와 시계열적으로 비교했을 때, 모두 유사한 경향을 보였다.

2010년 8월 13일부터 9월 3일까지 수도권지역에서 집중관측(Predictability and Observation Experiment of Korea-2010, ProbeX-2010)이 수행되었으며, 그 일환으로 동두천, 인천공항, 양평에서 6시간 간격으로 라디오존데 관측이 실행되었다. 관측기간 동안 우리나라의 전형적인 호우 패턴인 스콜선형, 정체전선형, 태풍 전면 수렴형, 열대저압부형, 태풍 직접형 호우가 연속적으로 발생하였다. 8월 15일 03 KST 경에는 스콜선형 구름대가 수도권 지역에서 발달하였다. 따뜻하고 습한 하층 공기 위로 건조한 중층 공기가 유입되어 강한 대류 불안정이 형성되었으며 이로 인해 호우가 발생하였다. 8월 23일부터 26일, 8월 27일부터 29일은 각각 정체전선과 태풍 전면 수렴대의 영향을 받아 강우가 발생하였다. 정체전선형 강우 초기에는 열적 불안정이 우세하게 나타났으나 강우 후기에는 역학적 불안정이 강화되었다. 이 기간 중 특히 강한 강수는 서해상에서 남풍류 하층 제트가 발달한 8월 25일에 발생하였다. 태풍 전면 수렴형 강우기간에는 열적 불안정과 역학적 불안정이 모두 유지되는 특징을 보였다. 이 기간 중 특히 강한 강수는 높은 상당온위(〉345 K)를 가진 열대 공기가 대기 중하층에 거쳐 유입되었을 때 발생하였다. 8월 27일과 9월 2일에는 각각 열대저압부와 태풍 곤파스의 영향에 의해 강우가 발생하였다. 이 사례들 동안에는 역학적 불안정이 매우 강하게 발달하였다.

2006년 10월 22일에서 24일에 걸쳐 한반도 강릉지역에서 강한 집중호우가 발생했다. 이 기간에 대해서 집중호우가 발생하기 전과 강우강도가 가장 강했을 때 나타나는 종관적, 운동학적 특성을 조사하기 위하여 본 연구에서는 지상일기도, 상층일기도, 적외위성영상, 자동기상관측장비(AWS) 자료, NCEP(National Centers for Environmental Prediction) 전구분석자료를 이용하였다. 분석 기간 동안 강릉에서 관측된 총 강수량은 316.5 mm이고, 최대 순간풍속은 63.7m s-1이다. 일기도를 분석해보면 집중호우가 시작하기 전 온대저기압이 한반도 중부에서 발달하였으며 한반도 북부에 역전기압골이 형성되어 있다. 또한 300 hPa 상층일기도에서는 서해와 한반도 남부에 제트기류가 위치하며, 위치 소용돌이도 이상과 관련된 절리 저기압이 한반도 북서부에서 발달하고 있다. 강우강도가 가장 강했을 때의 특성을 좀 더 자세히 알아보기 위하여 강릉지역의 위치 소용돌이도 이상과 바람, 위치 소용돌이도의 시간-연직 단면도, 연직운동, 발산장과 수렴장, 역학적 온위의 연직분포에 대해 조사하였다. 종관적, 운동학적 과정을 분석해 본 결과 대류권계면 접힘이 집중호우 발생에 큰 역할을 한 것으로 사료된다.

During the period from August 18 to August 19, 1972, a heavy rainfall was observed in Kyeonggi district. The total amount of rainfall over that period exceeded 450㎜. Some synoptic features of the heavy rainfall were studied by the use of synoptic data. The notable feature was the synoptic situation which built up a deep convectively unstable layer over Korea peninsula. The observed low level jet stream seems to be formed by the heavy rainfall activity.

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.

In this study, the impact of cumulus parameterization usage in Weather Research and Forecasting (WRF) model on reproducing summer precipitation in South Korea is evaluated. Two sensitivity experiments are set up with using cumulus parameterization (ON experiment) and without using cumulus parameterization, which is called Convection Permitting Model (OFF experiment). For the both ON and OFF experiments, the horizontal grid resolution is 2.5km, and initial and lateral boundary conditions are derived from ERA5 reanalysis data. Overall, both of the two experiments can capture the spatial distribution of 2014 summer mean and extreme precipitation but show dry biases in the southern region of Korean Peninsula. Occurrence percentage analyses for different precipitation intensity reveal that OFF experiments show better performance than ON experiment for extreme precipitation. In the case of heavy rainfall over Gyeongnam region for 25 August 2014, OFF experiment shows similar characteristic of rainfall to the observations, although it simulates earlier precipitation peak. On the other hand, ON experiment underestimates the amount of precipitation. Also, vertical distribution of equivalent potential temperature and strong southerly wind which play an important role in developing heavy rainfall on 25 August 2014 are better simulated in OFF experiment.

본 연구에서는 기존의 정량적인 강수량 정보를 제공하는 방식에서 벗어나 호우발생에 따른 생활환경의 변화에 끼치는 영향을 고려한 호우영향예 보서비스의 필요성을 기반으로 호우위험영향도 평가가 가능한 호우재해 위험영향 매트릭스를 개발하고, 이를 통해 호우위험영향을 평가하는 방법을 제시하였다. 사당동 일대를 대상으로 실제 발생 호우사상(2011년 7월 27일)을 적용하였으며, 호우에 의한 침수로 영향을 받는 대상별(사람, 교통, 시설) 호우위험영향평가를 수행하였다. 이를 위해 1 km 격자기반으로 호우위험정도(Impact Level)를 산정하고, 침수심 결과를 조합하여 격자 기반의 잠재호우위험영향(Potential Risk Impact)을 산정하였다. 여기에 강우발생가능성 Likelihood와의 조합을 통해 호우영향예보가 가능한 호우위험영향(Heavy Rainfall Risk Impact) 값을 산정하여 사당동 지역의 호우영향정도를 격자기반으로 4개의 등급으로 분석, 제시하였다.

In this study, we identified heavy rain damage and rainfall characteristics for each region, and proposed Hazard-Triggering rainfall according to heavy rain damage scale focused on Gyeonggi-do. We classified the damage scale into three groups (total damage, over 100 million won, over 1 billion won) to identify the characteristics of heavy rain damage, and we determined criteria of the rainfall class for each rainfall variable (maximum rainfalls for the durations of 1, 3, 6, 12 hours) to identify the rainfall characteristics. We calculated the cumulative probability of heavy rain damage based on the rain criteria mentioned above to establish the Hazard-Triggering rainfall according to the heavy rainfall damage scale. Using the results, we establish the Hazard-Triggering rainfall for each rain variable according to heavy rain damage. Finally, this study calculated the assessment indicator (F1-Score) for classification performance to test the performance of the Hazard-Triggering rainfall. As the results, the classification performance of the Hazard-Triggering rainfall which proposed in this study was 11%, 30%, 10% higher than the criteria by KMA (Korea Meteorological Administration).

The development of GIS technology has enabled the analysis of heavy rainfall vulnerability based on spatial analysis. In general, spatial analysis is performed based on property data and spatial data. Spatial data and attribute data differ in the generation units due to various reasons. The difference in these units can also cause problems in the results of the analysis. In particular, the Modifiable Areal Unit Problem (MAUP) that occurs according to the spatial unit setting is the most representative. The Modifiable Temporal Unit Problem (MTUP), which occurs according to the recent time unit setting, is also being raised. In this study, we analyzed the vulnerability of heavy rainfall in consideration of MAUP and MTUP. To analyze the effect of the MAUP, different administrative units were constructed and analyzed. In Seoul, Busan, and Ulsan, there was a scale effect in which disaster vulnerability was analyzed differently according to the analysis unit. In order to analyze the impact of MTUP, the range of study period was configured differently. The impact of the temporal boundary, in which overall disaster characteristics change and disaster vulnerability changes, has been identified. Analysis of regional disaster vulnerability considering MAUP and MTUP will be effective not only for the study of heavy rainfall disaster but also for setting the standard of disaster prevention policy.

The changes on community structures of benthic macroinvertebrates, relevance to the environment and interrelationship between benthos were studied over two years in stream with large environmental disturbance, which caused by localized heavy rain during Typhoon Chaba in October 2016. As a result, the number of species and individuals were increased after localized heavy rain, especially numbers of individuals of Ephemeroptera and Plecoptera were greatly increased. On the contrary, those of Semisulcospira libertina and Semisulcospira forticosta of Mesogastropoda were greatly decreased. Dominant species was Baetis fuscatus of Ephemeroptera, numbers of species and individuals of Ephemeroptera, Plecoptera and Trichoptera(EPT group) were dramatically increased from 26 species, 110 individuals to 32 species, 365 individuals respectively. This suggests that the change of river bed and flow velocity due to heavy rain provided a suitable environment for the EPT group that preferred the rift of a stream. In the functional feeding group, only gathering collectors and filtering collectors were identified in autumn of 2017 because some functional groups preferentially adapted to the changed environment. The interspecific competition and environmental condition were the worst in autumn after heavy rain due to the increase individuals of some species. The ecological score of benthic macroinvertebrate community(ESB) was higher after the heavy rain than before. Results of the Group Pollution Index(GPI), Korean Saprobic Index(KSI) and Benthic Macroinvertebrate Index(BMI) were similar to those before and after heavy rainfall. Therefore, ESB was the most discriminating method for estimating the biological water quality in this study. Some species that are sensitive to water quality changes still appear or increase individuals in the area under investigation after the heavy rain. On the other hand, the individuals of some pollutant species decreased. This is thought to be because the habitat fluctuation caused by heavy rainfall has improved the water environment.

최근 집중호우의 발생빈도가 증가하고 있으며, 이를 고려한 강우분석을 실시하여야 한다. 현재 수문설계를 위한 강우분석은 한반도 조밀도 36 km 인 기상청 관할 종관기상관측지점(Automated Surface Observing System, ASOS)의 시 단위 강우를 이용하고 있다. 이로 인해 같은 강우지점의 티센망에 포함되는 중소규모 유역은 동일한 확률강우량과 강우시간분포로 분석하게 됨으로 유역특성을 고려하지 못하는 문제가 발생한다. 또한, 10~20 km 범위 내에서 발생하는 집중호우의 시 ․ 공간적 변화를 고려하지 못하는 문제점이 발생한다. 따라서 본 연구에서는 종관기상관측지점에 비해 상대적으로 조밀도가 우수한 방재기상관측지점(Automatic Weather System, AWS)의 분 단위 강우자료를 이용하여 집중호우를 고려한 확률강우량을 산정하였다. 또한, 유역에 적합한 Huff의 4분위 방법 산정을 위해 Case별 시간분포 산정과 유출분석을 실시하였다. 이는 집중호우와 유역특성을 반영한 설계수문량 산정에 크게 기여할 것으로 판단된다.

The purpose of this study is to identify the factors related to the heavy rain damage and to identify effect of repair and improvement for irrigation facilities on heavy rain damages. The results of the analysis are as follows. First, the imbalance of precipitation became worse over time from using the coefficient of variation. Second, the analysis using Spearman correlation coefficient shows positive relationship between heavy rain damage amount and precipitation amount, and negative correlation between heavy rain damage amount and repair and improvement for irrigation facilities cost. Third, the analysis of the panel regression model shows that the negative impact of the repair and improvement for irrigation facilities cost on the heavy rain damage, which means that the increase of the repair and improvement for irrigation facilities cost can reduce the heavy rain damage.

본 연구에서는 호우 방향성에 의한 유역 유출응답 특성을 살펴보았다. 이를 위해 호우와 하천망의 방향적 특성을 확률밀도함수로 정량화하였고, 각 방향성 함수를 회선적분하여 호우 방향성의 고려 유무에 따른 유출응답 특성을 비교하였다. 그 결과, 호우 방향성을 고려한 유출모의 결과는 호 우 방향성을 고려하지 않은 경우에 비해 관측 유출자료와 더욱 유사함을 알 수 있었다. 이러한 결과는 호우 방향성을 고려한 유역 반응함수에 의해 유출모의 결과가 보다 개선될 수 있음을 나타낸다. 따라서 본 연구성과는 호우 방향성에 따른 유역 반응함수의 비선형성을 고려함으로써 유출모의 의 불확실성을 줄이는데 기여할 수 있을 것으로 기대된다.

The disaster caused by the heavy rain results in the greatest damage Among the damages caused by the weather disaster. many previous researches actively analyzed heavy rainfall disasters. The work includes analyzing vulnerability of disaster using characteristics and size of disaster damage in local area, selecting influencing factors influencing disaster and analyzing its influence. Rainfall during the heavy rainfall disaster is concerned with regional vulnerability to rainfall. The disaster-induced rainfall averaged on scale was analyzed by classifying disaster damage scale. The damage per precipitation unit is assumed to be disaster vulnerability, and local vulnerability of disasters is analyzed and the tendency of disaster vulnerability is analyzed using a time series analysis. The total amount of rainfall during the disaster period was analyzed in a large amount of rainfall in the Seoul metropolitan area and Busan city. The analysis shows that The average rainfall per accident case is high, and the region with relatively high stability against heavy rainfall disaster is Seoul metropolitan city. Southwest regions of the Korean Peninsula are analyzed to be affected by a very small amount of precipitation. The damage analysis shows that Busan Metropolitan City and the metropolitan area are relatively safe area against disaster. The analysis of disaster vulnerability based on the precipitation during the heavy rainfall disaster provides a clear classification of vulnerability by region.