• Journal of Korean Society of Disaster and Security
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• v.6 no.2
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• pp.1-8
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• 2013

This study is concerned with the estimation of fluctuate wind velocity statistic properties in the major cities reflecting the recent meteorological with largest data samples (yearly 2003-2012). The basic wind speeds were standardized homogeneously to the surface roughness category C, and to 10m above the ground surface. The estimation of the extreme of non-Gaussian load effects for design applications has often been treated tacitly by invoking a conventional wind design (gust load peak factor) on the basis of Gaussian processes. This assumption breaks down when the loading processes exhibits non-Gaussianity, in which a conventional wind design yields relatively non conservative estimates because of failure to include long tail regions inherent to non-Gaussian processes. This study seeks to ascertain the probability distribution function from recently wind data with effected typhoon & maximum instantaneous wind speed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.9 no.3
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• pp.111-118
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• 2004

An ocean buoy was deployed 10 km off Donghae city, Korea at a depth of 130 m to measure meteorological (air pressure, air temperature, wind speed, wind gust, wind direction, relative humidity) and oceanographic data (water properties and currents in the whole column) in real-time. The buoy recorded a maximum wind gust of 25 m/s (10 minutes' average speed of 20 m/s) and a minimum air pressure of 980 hPa when the eye of typhoon Maemi passed by near the Uljin city, Korea at 03:00 on 13 September 2003. The wave height reached maximum of 9 m with the significant wave height of 4 m at 04:00 (1 hour after the passage of Maemi). The currents measured near the surface reached up to about 100 cm/s at 13:00 (10 hours after the passage of Maemi). The mixed layer (high temperature and low salinity) thickness, which was accompanied by strong southward current, gradually increased from 20 m to 40 m during the 10 hours. A simple two layer model for the response to an impulsive alongshore wind over an uniformly sloping bottom developed by Csanady (1984) showed reasonable estimates of alongshore and offshore currents and interface displacement for the condition of typhoon Maemi at the buoy position (x=8.15 km) during the 10 hours.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.375-375
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• 2012

우리나라는 연강우량의 여름철 집중현상이 뚜렷하며 많은 부분이 태풍에 기인한다. 기후변화로 인하여 최근 들어서 태풍이 수반하는 폭우나 국지성 호우로 인한 강우사상이 증가하고 있어 짧은 시간에 많은 강우량이 발생하여 단기강우의 강도가 증가하고 있다. 이로 인하여 단기간에 예측하기 힘든 큰 강우량이 발생하는 경우가 빈번하여 이와 같은 강우에 의한 홍수를 대비할 필요성이 대두되고 있다. 따라서 본 연구에서는 태풍으로 인한 강우에 대하여 빈도해석을 수행하여 태풍으로 인하여 발생하는 확률강우량을 산정하였다. 태풍은 여러 인자를 포함하고 있는데 강우(1시간, 24시간, 총합), 풍속(최대, 순간최대), 중심최저기압, 중심최대풍속 등이 그것들이며, 강우와 동시에 그 이외의 인자들을 고려하기 위하여 이변량 빈도해석 모형인 copula 모형을 이용하여 빈도해석을 수행하였다. 이와 같이 copula 모형이 구성되면, 조건부 copula의 개념을 이용하여 강우 이외의 인자가 주어졌을 경우의 확률강우량을 산정할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.206-206
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• 2023

지구온난화로 인한 기후변화에 따라 평균강수량과 증발량이 증가하며 강우지역 집중화와 강우강도가 높아질 가능성이 크다. 우리나라의 경우 협소한 국토면적과 높은 인구밀도로 기후변동의 영향이 크기 때문에 한반도에 적합한 유역규모의 수자원 예측과 대응방안을 마련해야 한다. 이를 위한 수자원 관리를 위해서는 유역에서 강수량, 유출량, 증발량 등의 장기적인 자료가 필요하며 경험식, 물리적 강우-유출 모형 등이 사용되었고, 최근들어 연구의 확장성과 비 선형성 등을 고려하기 위해 딥러닝등 인공지능 기술들이 접목되고 있다. 본 연구에서는 ASOS(동해, 태백)와 AWS(삼척, 신기, 도계) 5곳의 관측소에서 2011년~2020년까지의 일 단위 기상관측자료를 수집하고 WAMIS에서 같은 기간의 오십천 하구 일 유출량 자료를 수집 후 5개 관측소를 기준으로Thiessen 면적비를 적용해 기상자료를 구축했으며 Angstrom & Hargreaves 공식으로 잠재증발산량 산정해 3개의 모델에 각각 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온), 일 강수량과 잠재증발산량, 일 강수량 - 잠재증발산량을 학습 후 관측 유출량과 비교결과 기상자료(일 강수량, 최고기온, 최대 순간 풍속, 최저기온, 평균풍속, 평균기온)로 학습한 모델성능이 가장 높아 최적 모델로 선정했으며 일, 월, 연 관측유출량 시계열과 비교했다. 또한 같은 학습자료를 사용해 다층 퍼셉트론(Multi Layer Perceptron, MLP) 앙상블 모델을 구축하여 수자원 분야에서의 인공지능 활용성을 평가했다.

• Journal of Bio-Environment Control
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• v.14 no.3
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• pp.166-173
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• 2005

This study was carried out to evaluate the structural stability in a non-heating greenhouse with a single cover for Citrus cultivation which was built up in Jeju on the basis of the drawing designed by Jejudo Agricultural Research & Extension Services and also to make use of the data for developing a standardized non-heating greenhouse in Jeju. The analysis of a structural stability was conducted by using CFX-5.7 and ANSYS under the design condition of a maximum accumulated snow-depth of 19.1 cm as well as an instantaneous maximum wind velocity of $36.6\;m{\cdot}s^{-1}$ which was set up on the basis of meteorological statistics in Jeju. As a result, the maximum von-Mises stress applied on pipes under the wind velocity of $36.6\;m{\cdot}s^{-1}$ showed a value of $250\;N{\cdot}mm^{-2}$ which was greater than the allowable stress of the pipe with a value of $235.4\;N{\cdot}mm^{-2}$ (=$2,400\;kg{\cdot}cm^{-2}$) and also $53.8\;N{\cdot}mm^{-2}$ under the snow-depth of 19.1 cm, respectively. This result suggested that the greenhouse be unstable under the design condition of an instantaneous wind velocity of $36.6\;m{\cdot}s^{-1}$ so that it was necessary for the greenhouse to be reinforced to secure the structural stability.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2002.08a
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• pp.100-117
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• 2002

소흑산도항 방파제의 전체 길이 500m가 완공된 것이 1999년 12월 말일경 이었고 2000년 8월 31일 제12호 태풍 프라피룬(PRAPIRON)이 내습(순간최대풍속：58.3m/sec,4시간 지속 최 대풍속 47.4m/sec)하여 완공된 방파제 두부구간 64m가 유실되고 제간부 436m의 부분파손을 입는 피해를 보았다. (중략)

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.246-255
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• 2011

This study was carried out to investigate the characteristics of wind damaged trees in the Hongneung Arboretumin Korea. Many trees in the Hongneung Arboretum were damaged by the 'Kompasu', the seventh typhoon in the year of 2010 having strong impacts on the central region of Korea. Damaged trees were divided into 3 damage types: windthrow, slanted and broken trees. Most of damaged trees (97.3%) were found at low slope (below 15 degree) or medium slope (15~20 degree). The 45.3% of damaged trees were coniferous and 54.7% were deciduous trees. The wind speed was recorded for the duration of the typhoon from 04:00 AM to 12:00 PM on September 2. The average wind speed and the maximum instantaneous wind speed inside the forest were 1.4 m/sec and 3.5 m/sec, respectively. The highest peak of the average and the maximum instantaneous wind speed inside of forest, 3.4 m/sec and 8.7 m/sec respectively, were recorded at 06:10 AM on September 2. To analyze relationship between wind characteristics and the damage types, the average wind speed and the frequency of wind wave was compared to those of pre-typhoon period (Feb. 13, Feb. 20, Apr. 21, Oct. 16, 2009 and Mar. 20, 2010). The results indicated that the damaged trees were affected by frequent wind wave rather than the wind speed itself. Also average wind direction inside the forest was analyzed to investigate the impact of wind direction on the damaged trees. The wind had mainly blown from SE and SW, and the maximum instantaneous wind direction was SE direction overall. Most of the damaged trees (84.0%) had fallen down to the NW or NE direction. This result showed that the fallen direction of the damaged trees and wind direction was highly related. Therefore, we found that the frequency of wind wave was the main factor of wind damages during the typhoon 'Kompasu' and wind direction was highly related to the fallen direction.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.3
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• pp.240-248
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• 2016

As a new technical approach, the achievement of an eco-environmental leisure boat with a photovoltaic system is attempted by simultaneously actuating nine photovoltaic solar panels, in association with an applied sail control system by use of wind. In this approach, the photovoltaic system consists of a solar module, an inverter, a battery, and relevant components, while the sail control device is equipped with a sail up/down and mast-turning instrument. Furthermore, this research conducts a performance evaluation of the manufactured prototype and acquires the purposed quantity value and the development purpose items. The significant items-sail up/down speed (seconds) and mast turning angle (degrees)-are evaluated during the performance test. In the process of the performance evaluation, a wind direction sensitivity of 90% and a maximum instant charging power of 900 W were also obtained. In addition, the maximum sail time is evaluated in order to acquire the optimum quantity.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.