• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.135-146
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• 2015

To provide the data necessary to determine the design wind speed for calculating the wind load acting on a greenhouse, we measured the wind speed below 10m height and analyzed the power law exponents at Buan and Gunwi. A wind speed greater than $5m{\cdot}s^{-1}$ is appropriate for calculating the power law exponent necessary to determine the wind speed distribution function according to height. We observed that the wind speed increased according to a power law function with increased height at Buan, showing a similar trend to the RDC and JGHA standards. Therefore, this result should be applied when determining the power law function for calculating the design wind speed of the greenhouse structure. The ordinary trend is that if terrain roughness increases the value of power law exponent also increases, but in the case of Gunwi the value of power law exponent was 0.06, which shows contrary value than that of the ordinary trend. This contrary trend was due to the elevations difference of 2m between tower installed and surrounding area, which cause contraction in streamline. The power law exponent started to decrease at 7 am, stopped decreasing and started to increase at 3 pm, and stopped increasing and remained constant at 12 pm at Buan. These changes correspond to the general change trends of the power law exponent. The calculated value of the shape parameter for Buan was 1.51, confirming that the wind characteristics at Buan, a reclaimed area near the coast, were similar to those of coastal areas in Jeju.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.5
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• pp.326-332
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• 2012

In this paper, error analyses on the calculation of offshore wind speed have been conducted using HeMOSU-1 data to develop offshore wind energy in Yeonggwang sea of Korea and onshore observed wind data in Buan, Gochang and Yeonggwang for 2011. Offshore wind speed data at 98.69 m height above M.S.L is estimated using relational expression induced by linear regression analysis between onshore and offshore wind data. In addition, estimated offshore wind speed data is set at 87.65 m above M.S.L using power law wind profile model with power law exponent(0.115) and its results are compared with the observed data. As a result, the spatial adjustment error are 1.6~2.2 m/s and the altitude adjustment error is approximately 0.1 m/s. This study shows that the altitude adjustment error is about 5% of the spatial adjustment error. Thus, long term observed data are needed when offshore wind speed was estimated by onshore wind speed data. because the conversion of onshore wind data lead to large error.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.3
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• pp.122-130
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• 2021

A wind-speed estimation at the arbitrary elevations is key component for the design of the offshore wind energy structures and the computation of the wind-wave generation. However, the wind-speed estimation of the target elevation has been carried out by using the typical functions and their typical parameters, e.g., power and logarithmic functions because the available wind speed data is limited to the specific elevation, such as 2~3m, 10 m, and so on. In this study, the parameters of the vertical profile functions are estimated with optimal and analyzed the parameter ranges using the HeMOSU-1 platform wind data monitored at the eight different locations. The results show that the mean value of the exponent of the power function is 0.1, which is significantly lower than the typically recommended value, 0.14. The values of the exponent, the friction velocity, and the roughness parameters are in the ranges 0.0~0.3, 0~10 (m/s), and 0.0~1.0 (m), respectively. The parameter ranges differ from the typical ranges because the atmospheric stability condition is assumed as the neutral condition. To improve the estimation accuracy, the atmospheric condition should be considered, and a more general (non-linear) vertical profile functions should be introduced to fit the diverse profile patterns and parameters.

• Journal of the Korean Solar Energy Society
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• v.36 no.4
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• pp.11-20
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• 2016

For accurate wind resource assessment and wind turbine performance test, it is essential to secure wind data covering a rotor plane of wind turbine including a hub height. In general, we can depict wind speed profile by extrapolating or interpolating the wind speed data measured from a meteorological tower where multiple anemometers are mounted at different heights using a power-law of wind speed profile. The most important parameter of a power-law equation is a vertical wind profile exponent which represents local characteristics of terrain and land cover. In this study, we calculated diurnal vertical wind profile exponents of 8 locations in Jeju Island who possesses excellent wind resource according to the GUM (Guide to the Expression of Uncertainty in Measurement) to evaluate its uncertainty. Expanded uncertainty is calculated by combined standard uncertainty, which is the result of composing type A standard uncertainty with type B standard uncertainty. Although pooled standard deviation should be considered to derive type A uncertainty, we used the standard deviation of vertical wind profile exponent of each day avoiding the difficult of uncertainty evaluation of diurnal wind profile variation. It is anticipated that the evaluated uncertainties of diurnal vertical wind profile exponents at 8 locations in Jeju Island are to be registered as a national standard reference data and widely used in the relevant areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3A
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• pp.209-216
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• 2009

The structural stability as well as economical efficiency of the wind sensitive structures are strongly dependant on accurate evaluation of the design wind speed. Present study demonstrates a useful wind data obtained at the wind monitoring tower in the Kwangyang Suspension Bridge site. Moreover the Measure-Correlate-Predict (MCP) method has been applied to estimate the long-term wind data at the bridge site based on the wind data at the local weather station. The measured data indicate that the turbulent intensities and roughness exponents are strongly affected by the wind direction and surrounding topography. The new design wind speed based on MCP method is 20m/s lower than that at the original estimation, and the resulting design wind load is only 36% of the old prediction. The field measurement of wind data is recommended to ensure the economical and secure design of the wind sensitive structures because the measured wind data reveal much different from the estimated one due to local topography.

• Journal of the Korean Geographical Society
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• v.37 no.3
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• pp.237-246
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• 2002

This study classified wintertime bioclimatic zones of South Korea based on daytime and nighttime distribution of wind chill index calculated from climate data during the coldest month for latest 30 years (1971- 2000). The results show that the winter daytime and nighttime wind chill index were influenced by climatic factors such as elevation, land-sea breeze, topology, and sea currents etc. as well as climatic components such as temperature, wind speed, and sunshine, so that South Korea was divided into five bioclimatic zones; Cool day- cold night zone, Keen day- Cold night zone, Keen day-Very Cold night zone, Cold day and night zone, and Cold day-Extremely Cold night zone. Especially, coasts and island areas, except for south coast of Korea, shows Keen bioclimatic response during daytime and Very Cold bioclimatic response during nighttime. This indicates that coasts and island areas, except for south coast of Korea are affected by moonson and land-sea breeze. In addition, highly elevated Daegwallyeong shows Cold bioclimatic response during daytime and Extremely Cold during nighttime due to the influence of adiabatic temperature lapse rate and monsoon. This study offers basic data necessary to make decisions concerning insulation such as clothing and architect etc. by classifying winter bioclimatic zones of South Korea based on various daytime and nighttime distribution of wind chill.

• Journal of the Korean earth science society
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• v.30 no.7
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• pp.845-854
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• 2009

To examine the wind energy potential of North Korea, climatological wind map was constructed using the 30-year hourly observations of wind speed and direction at 27 meteorological stations. This examination was based on the Weibull model, which represents the probability density distribution of wind speed. It was found that overall, high terrain(Geama Gowon) in the central-northern part and south-west coast (Hwanghae-do) of North Korea have the annual average wind speed which exceeds 4 m/s at 50 m altitude above ground. The wind speed >5 m/s is more persistent in spring, but less in summer. Amongst the meteorological stations, Changjin and Yangdok show the most persistent wind speed in time and strength.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.679-692
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• 2020

We coupled a CFD model to the WRF-Chem model (WRF-CFD model) and investigated the characteristics of flows and carbon monoxide (CO) distributions in a building-congested district. We validated the simulated results against the measured wind speeds, wind directions, and CO concentrations. The WRF-Chem model simulated the winds from southwesterly to southeasterly, overestimating the measured wind speeds. The statistical validation showed that the WRF-CFD model simulated the measured wind speeds more realistically than the WRF-Chem model. The WRF-Chem model significantly underestimated the measured CO concentrations, and the WRF-CFD model improved the CO concentration prediction. Based on the statistical validation results, the WRF-CFD model improved the performance in predicting the CO concentrations by taking complicatedly distributed buildings and mobiles sources of CO into account. At 04 KST on May 22, there was a downdraft around the AQMS, and airflow with a relatively low CO concentration was advected from the upper layer. Resultantly, the CO concentration was lower at the AQMS than the surrounding area. At 15 KST on May 22, there was an updraft around the AQMS. This resulted in a slightly higher CO concentration than the surroundings. The WRF-CFD model transported CO emitted from the mobile sources to the AQMS measurement altitude, well reproducing the measured CO concentration. At 18 KST on May 22, the WRF-CFD model simulated high CO concentrations because of high CO emission, broad updraft area, and an increase in turbulent diffusion cause by wind-shear increase near the ground.