• Wind and Structures
• /
• v.12 no.4
• /
• pp.349-381
• /
• 2009

Since 1998, several institutions have deployed mobile instrumented towers to collect research-grade meteorological data from landfalling tropical cyclones. This study examines the wind flow characteristics from seven landfalling tropical cyclones using data collected from eight individual mobile tower deployments which occurred from 1998-2005. Gust factor, turbulence intensity, and integral scale statistics are inspected relative to changing surface roughness, mean wind speed and storm-relative position. Radar data, acquired from the National Weather Service (NWS) Weather Surveillance Radar - 1988 Doppler (WSR-88D) network, are examined to explore potential relationships with respect to radar reflectivity and precipitation structure (convective versus stratiform). The results indicate tropical cyclone wind flow characteristics are strongly influenced by the surrounding surface roughness (i.e., exposure) at each observation site, but some secondary storm dependencies are also documented.

• Journal of the Korean Solar Energy Society
• /
• v.32 no.2
• /
• pp.87-94
• /
• 2012

In this study, we found a relationship between wind shear exponent, ${\alpha}$, and a few factors such as the wind speed, $V$, ruggedness index($RIX$), and the Weibull shape parameter, $k$ of sites in complex terrain in Korea. Wind shear exponents in main wind directions were calculated using wind speed data measured for one year from various heights of eleven meteorological masts in Gangwon province. It was found from the analysis that the reciprocal of the wind shear exponent can be expressed by an exponentially decaying function with respect to a multiple of $V$, $RIX$ and $k$. This result is considered useful to be used to characterize wind characteristics of specific sites in complex terrain in Korea with limited information.

• Agricultural and Biosystems Engineering
• /
• v.6 no.2
• /
• pp.65-69
• /
• 2005

A windheat generation system with a Savonius windturbine was developed and the performance was evaluated through field tests. The system consisted of a heat generation drum, heat exchanger, water storage tank, and two circulation pumps. Frictional heat is created by rotation of a rotor inside the drum containing thermo oil, and was used to heat water. In order to estimate the capacity of this windheat generation system, weather data was collected for one year at the site near the windheat generation system. Wind Power from the savonius wind turbine mill was transmitted to the heat generation system with an one-to-three gear system. Starting force to rotate the savonius wind turbine and the whole system including the windheat generation system were 1.0 and 2.5 kg, respectively. Under the outdoor wind condition, maximum speed of the rotor in the drum was 75rpm at wind speed 6.5 m/sec, which was not fast enough to produce heat for greenhouse heating. Annual cumulative hours for wind speeds greater than 5 m/sec at height of 10, 20, 30 m were 190, 300 and 1020 hrs, respectively. A $5^{\circ}C$ increase in water temperature was achieved by the windheat generation system under the tested wind environment.

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

• 한국태양에너지학회:학술대회논문집
• /
• 2008.11a
• /
• pp.318-321
• /
• 2008

We classified wind sectors according to the wind features in South Korea. In order to get the information of wind speed and wind direction, we used and improved on the atmospheric numerical model. We made use of detailed topographical data such as terrain height data of an interval of 3 seconds and landuse data produced at ministry of environment, Republic of Korea. The result of simulated wind field was improved. We carried out the cluster analysis to classify the wind sectors using the K-means clustering. South Korea was classified as 10 wind sectors which have a clear wind features.

• Journal of the Korean Solar Energy Society
• /
• v.30 no.2
• /
• pp.46-53
• /
• 2010

In this paper the feasibility of wind power forecasting from MOS(Model Output Statistics) was evaluated at Gosan area in Jeju during February to Octoberin 2008. The observed wind data from wind turbine was compared with 24 hours and 48 hours forecasting wind data from MOS predicting. Coefficient of determination of measured wind speed from wind turbine and 24 hours forecasting from MOS was around 0.53 and 48 hours was around 0.30. These determination factors were increased to 0.65 from 0.53 and 0.35 from 0.30, respectively, when it comes to the prevailing wind direction($300^{\circ}\sim60^{\circ}$). Wind power forecasting ratio in 24 hours of MOS showed a value of 0.81 within 70% confidence interval and it also showed 0.65 in 80% confidence interval. It is suggested that the additional study of weather conditions be carried out when large error happened in MOS forecasting.

• Journal of Environmental Science International
• /
• v.19 no.8
• /
• pp.983-999
• /
• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

• Journal of Environmental Impact Assessment
• /
• v.20 no.4
• /
• pp.539-555
• /
• 2011

Newly constructed, high-rise dense building areas by urban development can cause changes in local wind fields. Wind fields were analyzed to assess the impact on the local meteorology due to the land use changes during the urban redevelopment called "Eunpyeong new town" in north-western Seoul using CFD_NIMR_SNU (Computational Fluid Dynamics, National Institute of Meteorological Research, Seoul National University) model. Initial value of wind speed and direction use analysis value of AWS (Automatic Weather Station) data during 5 years. In the case of the pre-construction with low rise built-up area, it was simulated that the spatial distribution of horizontal wind fields depends on the topography and wind direction of initial inflow. But, in the case of the post-construction with high rise built-up area, it was analyzed that the wind field was affected by high rise buildings as well as terrain. High-rise buildings can generate new circulations among buildings. In addition, small size vortexes were newly generated by terrain and high rise buildings after the construction. As high-rise buildings act as a barrier, we found that the horizontal wind flow was separated and wind speed was reduced behind the buildings. CFD_NIMR_SNU was able to analyze the impact of high-rise buildings during the urban development. With the support of high power computing, it will be more common to utilize sophisticated numerical analysis models such as CFD_NIMR_SNU in evaluating the impact of urban development on wind flow or channel.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.1
• /
• pp.32-39
• /
• 2013

Effects of Weibull shape parameter, k, on capacity factors of wind turbines were investigated. Wind distributions with mean wind speeds of 5 m/s, 6 m/s, 7 m/s and 8 m/s were simulated and used to estimate the annual energy productions and capacity factors of a 2MW wind turbine for various Weibull shape parameters. It was found from the study that the capacity factors of wind turbines are much affected by Weibull shape parameters. When the annual mean wind speed at the hub height of a wind turbine was about 7 m/s, and the air density was assumed to be 1.225 $kg/m^3$, the maximum capacity factor of a 2 MW wind turbine having a rated wind speed of 13 m/s was found to occur with the shape parameter of 2. It was also found that as the mean wind speed increased, the Weibull k parameter which yielded the maximum capacity factor increased. The simulated results were also validated by predictions of capacity factors of wind turbines using wind data measured in complex terrain.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.149-155
• /
• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.