• 대기
• /
• 제23권4호
• /
• pp.413-424
• /
• 2013

Urban Heat Island (UHI) intensity is one of vital parameters in studying urban boundary layer meteorology as well as urban planning. Because the UHI intensity is defined as air temperature difference between urban and rural sites, an objective sites selection criterion is necessary for proper quantification of the spatial variations of the UHI intensity. This study quantified the UHI intensity and its spatial pattern, and then analyzed their connections with urban structure and metabolism in Seoul metropolitan area where many kinds of land use and land cover types coexist. In this study, screen-level temperature data in non-precipitation day conditions observed from 29 automatic weather stations (AWS) in Seoul were analyzed to delineate the characteristics of UHI. For quality control of the data, gap test, limit test, and step test based on guideline of World Meteorological Organization were conducted. After classifying all stations by their own local climatological properties, UHI intensity and diurnal temperature range (DTR) are calculated, and then their seasonal patterns are discussed. Maximum UHI intensity was $4.3^{\circ}C$ in autumn and minimum was $3.6^{\circ}C$ in spring. Maximum DTR appeared in autumn as $3.8^{\circ}C$, but minimum was $2.3^{\circ}C$ in summer. UHI intensity and DTR showed large variations with different local climate zones. Despite limited information on accuracy and exposure errors of the automatic weather stations, the observed data from AWS network represented theoretical UHI intensities with difference local climate zone in Seoul.

• 한국수자원학회논문집
• /
• 제37권3호
• /
• pp.185-193
• /
• 2004

본 연구는 기후변화가 유역의 유출량과 수자원에 미치는 영향을 조사하고 평가하는데 목적이 있다. 이를 위하여 먼저, YONU GCM의 제한실험과 점증실험을 실시하여 전구적 규모의 기후변화 시나리오를 작성하였으며, 통계학적 축소기법과 추계학적 일기발생기법을 이용하여 대상지점의 일 수문기상 시계열을 모의하였다. 이렇게 얻은 시계열자료를 2CO2 상황에서의 유출량자료로 변환하기 위해 준 분포형 강우-유출 모형인 SLURP 모형에 입력하였다. 본 연구에서는 이 방법을 용담댐 유역에 적용하였으며, 그 결과, 기후변화시 연 평균 유출량의 경우 현재상황에 비해 약7.6％ 감소하는 것으로 모의되었으며, 계절적으로 볼 때 겨울철과 가을철에는 유출량이 증가하였으나 여름철에는 감소하였다. 그러나, 유출량의 계절적 패턴은 변화가 없는 것으로 모의되었다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2018년도 학술발표회
• /
• pp.154-154
• /
• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• 한국시뮬레이션학회논문지
• /
• 제26권3호
• /
• pp.73-86
• /
• 2017

환경 자료는 M&S(Modeling and Simulation)에서 실험 결과의 신뢰도를 높이는데 중요한 역할을 한다. 특히 국방 M&S에서는 극한 기상 현상들이 가상훈련 및 실험에서 중요하게 활용될 수 있다. 그러나 환경 자료는 여러 기관에 분산되어 있고, 방대하다. 이 때문에 M&S 운영자들이 실제 환경 자료에서 극한 기상 현상이 발생한 일자와 지역을 선정하여 획득하는 것은 어려운 일이다. 퍼지논리 기반의 환경 자료 검색 기술은 환경 시나리오 발생기 개발의 핵심 기술 중 하나이다. 연구결과 4개의 주요 매개변수(RV, MF, FRA, MRV)로 구성된 퍼지 검색 알고리즘을 제시하였다. 이 연구는 강풍을 동반한 호우 발생 일자를 검색하기 위해 RV는 풍속과 강수량을 위해 각각 14 m/s와 80 mm/d, FRA는 0.2, MRV는 1, 그리고 MF는 시그모이드를 활용할 것을 제안한다. 이 연구에서 제안하는 알고리즘은 국방 M&S에서 필요로 하는 극한 기상 현상들이 발생한 일자를 검색하는데 매우 유용할 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권1호
• /
• pp.216-238
• /
• 2023

In intelligent transportation systems, traffic management is an important task. The accurate forecasting of traffic characteristics like flow, congestion, and density is still active research because of the non-linear nature and uncertainty of the spatiotemporal data. Inclement weather, such as rain and snow, and other special events such as holidays, accidents, and road closures have a significant impact on driving and the average speed of vehicles on the road, which lowers traffic capacity and causes congestion in a widespread manner. This work designs a model for multivariate short-term traffic congestion prediction using SLSTM_AE-BiLSTM. The proposed design consists of a Bidirectional Long Short Term Memory(BiLSTM) network to predict traffic flow value and a Convolutional Neural network (CNN) model for detecting the congestion status. This model uses spatial static temporal dynamic data. The stacked Long Short Term Memory Autoencoder (SLSTM AE) is used to encode the weather features into a reduced and more informative feature space. BiLSTM model is used to capture the features from the past and present traffic data simultaneously and also to identify the long-term dependencies. It uses the traffic data and encoded weather data to perform the traffic flow prediction. The CNN model is used to predict the recurring congestion status based on the predicted traffic flow value at a particular urban traffic network. In this work, a publicly available Caltrans PEMS dataset with traffic parameters is used. The proposed model generates the congestion prediction with an accuracy rate of 92.74% which is slightly better when compared with other deep learning models for congestion prediction.

• 한국빅데이터학회지
• /
• 제6권2호
• /
• pp.39-49
• /
• 2021

최근 국가 관측망, 기업 공기 측정기 등을 통해 많고 다양한 기상 데이터가 수집되고 있다. 기계학습 기법을 통해 기상 예측하려는 노력이 곳곳에서 이루어지고 있으며, 국내 미세먼지는 농도가 증가해오고 사람들의 관심이 높아 가장 관심있는 예측 대상 중 하나이다. 본 연구에서는 서울시 전역에 설치된 840여 개실외공기측정기 데이터를 사용하여 PM10·PM2.5 예측 모형을 비교하고자 한다. 5분 뒤 미세먼지 농도 예측을 통해 실시간으로 정보를 제공할 수 있으며, 이는 10분·30분·1시간 뒤 예측 모형 개발에 기반이 될 수 있다. 잡음 제거, 결측치 대체 등의 데이터 전처리를 진행하였고, 시·공간 변수를 고려할 수 있는 파생 변수를 생성하였다. 모형의 매개변수는 반응 표면 방법을 통해 선택하였다. XGBoost, 랜덤포레스트, 딥러닝(Multilayer Perceptron)을 예측 모형으로 사용하여, 미세먼지 농도와 예측값의 차이를 확인하고, 모형 간 성능을 비교하고자 한다.

• 한국농공학회논문집
• /
• 제52권5호
• /
• pp.1-9
• /
• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
• /
• 한국우주과학회 2008년도 한국우주과학회보 제17권2호
• /
• pp.33.2-33.2
• /
• 2008

Korea Astronomy and Space Science Institute (KASI) is developing an empirical model for Korean Space Weather Prediction Center (KSWPC). This model predicts the geomagnetic storm strength (Dst minimum) by using only CME parameters, such as the source location (L), speed (V), earthward direction (D), and magnetic field orientation of an overlaying potential field at CME source region. To derive an empirical formula, we considered that (1) the direction parameter has best correlation with the storm strength (2) west $15^{\circ}$ offset from the central meridian gives best correlation between the source location and the storm strength (3) consideration of two groups of CMEs according to their magnetic field orientation (southward or northward) provide better forecast. In this talk, we introduce current status of the empirical storm prediction model development.

• 한국전자파학회:학술대회논문집
• /
• 한국전자파학회 2003년도 종합학술발표회 논문집 Vol.13 No.1
• /
• pp.208-212
• /
• 2003

An airborne radar is an essential aviation electronic system of the helicopter to perform various missions in all-weather environments. This paper presents the conceptual design results of the multi-mode pulsed Doppler radar system testbed model for helicopter. Due to the inherent flight nature of the hovering vehicle which is flying in low-altitude and low speed, as well as rapid maneuvering, the moving clutters from the platform should be suppressed by using a special MTD (Moving Target Detector) processing. For the multi-mode radar system model design, the flight parameters of the moving helicopter platform were assumed: altitude of 3 Km, average cruising velocity of 150knots. The multi-mode operation capability was applied such as short-range, medium-range, and long-range depending on the mission of the vehicle. The nominal detection ranges is 30 Km for the testbed experimental model, but can be expanded up to 75 Km for the long range weather mode. The detection probability of each mode is also compared in terms of the signal-to noise ratio of each mode, and the designed radar system specifications ate provided as a design results.

• 천문학회보
• /
• 제40권1호
• /
• pp.65.3-66
• /
• 2015

The determination of three dimensional parameters (e.g., radial speed, angular width, source location) of Coronal Mass Ejections (CMEs) is very important for space weather forecast. To estimate these parameters, several cone models based on a flat cone or a shallow ice-cream cone with spherical front have been suggested. In this study, we investigate which cone model is proper for halo CME morphology using 33 CMEs which are identified as halo CMEs by one spacecraft (SOHO or STEREO-A or B) and as limb CMEs by the other ones. From geometrical parameters of these CMEs such as their front curvature, we find that near full ice-cream cone CMEs (28 events) are dominant over shallow ice-cream cone CMEs (5 events). So we develop a new full ice-cream cone model by assuming that a full ice-cream cone consists of many flat cones with different heights and angular widths. This model is carried out by the following steps: (1) construct a cone for given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, (4) minimize the difference between the estimated projection points with the observed ones. We apply this model to several halo CMEs and compare the results with those from other methods such as a Graduated Cylindrical Shell model and a geometrical triangulation method.