• Journal of Wind Energy
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• v.9 no.4
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• pp.65-71
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• 2018

To establish an offshore wind turbine test site, a wind resource assessment of the candidate site is required as a preliminary procedure. The wind resource assessment must be performed with at least one year of wind data. If the assessment is performed with short-term wind data, the results cannot validate the wind conditions of the candidate site. This study performs wind resource assessment of Kokunsangun-do to investigate the wind conditions of the candidate site. The wind data is measured by the Automatic Weather System (AWS) of the Korea Meteorological Administration, located at Maldo. The data is for five years, measured from 2013 to 2017. Measured wind data is statistically processed with a 10-minute average scheme to find out the dominant wind direction and wind power density, with yearly wind speed distribution (Weibull-based). This study contributes to build a database of wind energy resources around Maldo. Also, the results of this study could be used for the establishment of an offshore wind turbine test site.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.387-387
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• 2023

2022년 중부권 폭우로 인하여 서울 강남구에서 도시홍수가 발생하였고, 많은 인명 및 경제적 지해를 유발하였다. 기후변화로 야기되는 극한 강우의 발생 패턴 및 강우 패턴의 변화가 많은 연구에서 확인되어 오고 있다. 한국의 경우 극한 강우가 국지적이고 단기간에 많은 강우량을 발생시키는 패턴으로 변화하고 있는 것으로 연구되고 있다. 특히, 도시홍수의 경우 도달시간이 매주 짧기 때문에, 초단기간에 대한 강우분석이 필요하나, 강우관측시스템의 한계로 인하여 현재까지는 초단기간에 대한 극한 강우분석이 미비한 실정이다. 1997년 이후로 기상청에서는 지속적으로 방재기상관측망(Automatic Weather System, AWS)를 설치를 하였고, 최근에 설치된 AWS의 경우 초단기간 강우량 자료를 관측할 수 있는 장비 및 시스템을 구축하고 있으나, 운영된 기간이 짧아 빈도해석에 적용하기에 한계점이 많다. 본 연구에서는 서울 지역에서 영향을 주는 40여개의 AWS의 초단기간 강우량 자료를 이용하여 서울 지역을 확률강우량을 산정하고자 한다. 짧은 관측기간으로부터 발생하는 확률강우량 추정불확실성의 저감을 위해서 지역빈도해석을 적용하였다. 지역빈도해석으로는 지수홍수법을 적용하였다. 추가적으로 서울안에서 공간적으로 확률강우량의 편차에 대하여 조사 분석하였다. 본 연구의 결과를 통하여 서울지역의 초단기간에 대한 안정적인 확률강우량의 추정이 가능할 것으로 예상되며, 추가적으로 지역별 확률강우량의 차이를 비교분석 할 수 있을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.513-513
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• 2023

최근 이상기후의 영향으로 국지성 및 집중호우로 인한 침수 피해가 증가하고 있다. 도시유역의 홍수는 사회적·경제적으로 큰 손실을 야기할 수 있어 실제 호우에 대한 침수 양상을 신속하게 예측하는것은 매우 중요하다. 이로 인해 침수 해석에 대한 결과를 빨리 제공할 수 있는 기계학습을 기반으로 한 도시 홍수 분석에 대한 연구가 증가하고 있다. 본 연구에서 적용한 LSTM(Long Short-Term Memory) 신경망은 기존 RNN(Recurrent neural network)이 가지고 있는 장기 의존성 문제를 해결하기 위해 고안된 모델으로 시계열 데이터에 대한 예측능력이 뛰어나다는 장점을 가지고있다. LSTM 신경망은 강우에 대한 격자별 침수심을 예측하기 위해 사용되었으며, 입력자료로 2000~2022년도에 걸친 도림천 유역의 침수피해를 야기한 지속시간 6시간 AWS(Automatic Weather System) 관측 강우 자료를 사용하였고 목표값으로 수집된 도림천 유역의 강우자료를 이용하여 SWMM(Storm Water Management Model)의 유출 결과를 바탕으로 수행된 2차원 침수해석 모의 결과를 사용하였다. 연구유역의 SWMM 배수 관망 입력자료의 정확성을 높이기 위해 서울시 하수관로 수위 현황 자료를 활용하여 매개변수 조정을 실시하였으며, 하수관로의 실측 수위와 모의 수위를 일치시켰다. LSTM 신경망을 이용하여 격자별로 예측된 침수심 데이터를 시각화하여 침수흔적도와 비교하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.16-16
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• 2019

기후변화 시나리오 및 계절예측 자료를 포함한 기후정보를 수자원 분야에 활용하기 위해서는 기후정보의 시 공간적인 상세화(donwscaling)을 필요로 한다. 상세화의 경우 역학적 상세화와 통계학적 상세화로 구분될 수 있으며, 통계학적 상세화를 위해서는 대상 지역의 기후특성을 대표할 수 있는 장기 관측 자료의 확보가 중요하다. 국내의 경우에는 자동기상관측장비(Automatic Weather System, AWS)와 종관기상관측장비(Automatic Synoptic Observation System, ASOS)로 부터 수집된 기상관측자료를 사용할 수 있으나 기후변화 시나리오의 통계적 상세화를 위해서는 30년 이상의 자료 기간을 포함하는 ASOS 자료가 적합하다. 하지만 개발도상국과 같이 기상관측기반이 열악한 지역에서는 잦은 결측 등으로 인하여 품질이 좋은 관측자료의 획득이 어려운 상황이다. 따라서 본 연구에서는 측이 포함된 장기 기상관측 자료로부터 대상 지역의 기후특성을 재현할 수 있도록 기본적인 QC(Quality Control)을 거쳐 결측 자료를 보완할 수 있는 기법 및 R 기반패키지를 개발하여 적용성을 평가하였다. 개발된 기법의 적용성 평가를 위해서 기상청에서 QC를 통해 제공하고 있는 60개 ASOS 지점의 관측자료 중 강수량과 기온 변수를 사용하였다. 최대 50%까지의 현실적인 결측 패턴을 임의로 생성하기 위해 실제 개발도상국 관측자료의 일단위 결측 패턴을 이용하였다. 자료의 QC는 관측일 누락/중복 및 문자형 관측값 등 기본적인 오류 검사, 기온의 경우 물리적 허용 범위에 대한 검사, 최고기온과 최저기온의 비교 및 계측기 오작동에 의한 동일한 값의 반복 등을 포함한 내적 일치성 검사를 우선적으로 수행한다. 이후 결측값에 대해서 인근 기상관측소와의 상관성 분석 결과를 기반으로 결측값을 채우고, 최종적으로는 다양한 위성자료 및 재분석 자료 중에서 일단위 기후특성의 재현성 평가를 통해 선정된 격자형 자료와의 상관성 분석 결과를 기반으로 결측값을 보정하였다. 기온의 경우는 결측률이 높더라도 월평균 기후특성에 큰 영향을 미치지 않았지만 강수의 경우에는 5% 이상의 결측이 발생하는 경우 월평균 강수량에 영향을 미쳐 지역의 강수량을 과소 추정하는 결과를 보였다. 개발된 QC 기법을 강수 자료에 적용한 결과 월평균 기후특성을 잘 복원하는 결과를 보였지만, 일단위 강우 사상의 재현에 있어서는 미흡한 결과를 보였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.2
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• pp.75-88
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• 2017

This study analyzed wind flows based on spatial and geomorphological characteristics of Daegu Metropolitan City. A three-stage analysis was performed, starting with a comparison of meteorological relationships between local wind direction (synoptic wind) and local wind flow. In the second stage the study area was subdivided into districts and suburban districts to analyze the relative change of local wind flow. In stage three, the formation of wind corridor for local wind flow, wind flow for the entire urban space, and spatial relationships between flows were verified comparatively using KLAM_21. Three results are notable, the first of which is a low correlation between synoptic wind of a region, and local wind, flow in terms of meteorology. Secondly, observations of local wind flow at five downtown districts and two suburban districts showed that there were diverse wind directions at each measurement point. This indicates that the spatial and geomorphological characteristics of areas neighboring the measurement points could affect the local wind flow. Thirdly, verifying the results analyzed using KLAM_21, compared to Atomatic Weather System(AWS) measurement data, confirmed the reliability of the numerical modelling analysis. It was determined that local wind flow in a city performs a spatial function and role in ameliorating the urban heat island phenomena. This indicates that, when an urban planning project is designed, the urban heat island phenomena could be ameliorated effectively and sustainably if local wind flow caused by immediate spatial and geomorphological characteristics is confirmed systematically and techniques are intentionally applied to connect the flows spatially within areas where urban heat islands occur.

• Journal of Environmental Impact Assessment
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• v.11 no.2
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• pp.79-91
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• 2002

Four dimensional data assimilation (FDDA) technique was considered for 3 dimensional wind field in coastal area and a set of 3 numerical experiments including control experiments has been tested for the case of the synoptic weather pattern of the weak northerly geostrophic wind with the cloud amount of less than 5/10 in autumn. A three dimensional land and sea breeze model with the sea surface temperature (SST) of 290K was performed without nudging the observed wind field and surface temperature of AWS (Automatic Weather System) for the control experiment. The results of the control experiment showed that the horizontal temperature gradient across the coastline was weakly simulated so that the strength of the sea breeze in the model was much weaker than that of observed one. The experiment with only observed horizontal wind field showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated. However, the horizontal wind speed and vertical motion in the convergence zone were weakly simulated. The experiment with nudgings of both the surface temperature and wind speed showed that both the pattern of local change of wind direction and the times of starting and ending of the land-sea breeze were fairly well simulated even though the ending time of the sea breeze was delayed due to oversimulated temperature gradient along the shoreline.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.16-16
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• 2018

Flora investigations have been conducted by many researchers for a long time in Korea. Even though large amount of investigation data has been accumulated, there is no accurate statistics or database because most of data were published in a printed form. We developed a web-based database of flora investigation, named as the Integrated Korean Flora Database (http://www.floradb.net/) to understand distribution patterns and habitats of plants in Korea. Till now, 480 published paper, 356 thesis, 76 reports and books, and 8 unpublished papers written in between 1962 and 2017 were collected and their species lists from 280 papers were parsed into the database. From 124,105 records, 3,100 species belonging to 206 families and 965 genera were identified via comparing with two major Korean plant species lists. 55 endangered species, 159 endemic species, and 367 rare species were identified. The most frequently surveyed species were Commelina communis in herbaceous and Rosa multiflora in woody plants. Microclimate data provided by Korea Meteorological Administration were also integrated and analyzed to assign cold hardness zones for each species. By comparing minimum temperature (<2%) acquired from automated weather stations (AWS) near by plant species, 6a to 10b zones (7b is the most frequent zone) were identified. Integrated Korean Flora Database will be a fundamental platform of korea flora investigation as well as a new standard for classifying distribution of plants based on accurate microclimate data. Moreover, it can also provide evidences of investigated plant species, such as specimen and/or pictures with connecting to the InfoBoss Cyber Herbarium (http://herbarium.infoboss. co.kr/) and Biodiversity Observation Datbase (BODB; http://www.biodiversitydb.org/).

• Journal of Environmental Science International
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• v.11 no.7
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• pp.651-662
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• 2002

Weather elements were observed by the AWS (Automatic Weather System) and dustfall particles were collected by the modified American dust jar (wide inlet bottle type) at 4 sampling sites in Busan area from March. 1999 to February, 2000. Thirteen chemical species (Al, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, Si, and Zn) were analyzed by AAS and ICP. The purposes of this study were to estimate qualitatively various bulk deposition flux of dustfall and insoluble components by applying regional and seasonal wind intensity. Frequency of wind speed were found in order of low(1-3m/s), very low(<1m/s), medium(3-8m/s) and high(>8m/s), and annual mean had higher range at low(1-3m/s) for 56.3%. Strong negative linear correlation were observed between dustfall and wind direction (northeastern and eastern), but strong positive linear correlation were observed between dustfall and wind direction (western and northwestern) at industrial, commercial and coastal zone(p<0.05). While a negative correlation were observed between wind speed frequency of very low(<1 m/s) and dustfall, and positive correlation were observed between wind speed frequency of low(1-3m/s) and dustfall in coastal zone(p<0.05). The correlation coefficient was observed 0.556 between wind speed frequency of low(1-3m/s) and Ni by commercial zone(p<0.05). The correlation coeffcient show well-defined insoluble trace metals (Al, Ca, Cr, Cu, Fe, Pb, and Zn) and wind speed frequency of low(1-3m/s) at coastal zone, which was found significant difference(p<0.01).

• Journal of Environmental Science International
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• v.19 no.1
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• pp.27-37
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• 2010

A system coupled the prognostic WRF mesoscale model and CALMET diagnostic model has been employed for predicting high-resolution wind field over complex coastal area. WRF has three nested grids down to from during two days from 24 August 2007 to 26 August 2007. CALMET simulation is performed using both initial meteorological field from WRF coarsest results and surface boundary condition that is Shuttle Radar Topography Mission (SRTM) 90m topography and Environmental Geographic Information System (EGIS) 30m landuse during same periods above. Four Automatic Weather System (AWS) and a Sonic Detection And Ranging (SODAR) are used to verify modeled wind fields. Horizontal wind fields in CM_100m is not only more complex but better simulated than WRF_1km results at Backwoon and Geumho in which there are shown stagnation, blocking effects and orographically driven winds. Being increased in horizontal grid spacing, CM_100m is well matched with vertically wind profile compared SODAR. This also mentions the importance of high-resolution surface boundary conditions when horizontal grid spacing is increased to produce detailed wind fields over complex terrain features.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.93-98
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• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.