• Wind and Structures
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• v.24 no.4
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• pp.351-365
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• 2017

The minimization method is widely used to predict the dynamic characteristics of a system. Generally, data recorded by experiment (for example displacement) tends to contain noise, and the error in the properties of the system is proportional to the noise level (NL). In addition, the accuracy of the results depends on various factors such as the signal character, filtering method or cut off frequency. In particular, coupled terms in multimode systems show larger differences compared to the true value when measured in an environment with a high NL. The iterative least square (ILS) method was proposed to reduce these errors that occur under a high NL, and has been verified in previous research. However, the ILS method might be sensitive to the signal processing, including the determination of cutoff frequency. This paper focused on improving the accuracy of the ILS method, and proposed the modified ILS (MILS) method, which differs from the ILS method by the addition of a new calculation process based on correlation coefficients for each degree of freedom. Comparing the results of these systems with those of a numerical simulation revealed that both ILS and the proposed MILS method provided good prediction of the dynamic properties of the system under investigation (in this case, the damping ratio and damped frequency). Moreover, the proposed MILS method provided even better prediction results for the coupling terms of stiffness and damping coefficient matrix.

• Journal of the Korean earth science society
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• v.24 no.1
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• pp.7-21
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• 2003

The state-of-art third generation wave prediction model WAM was applied to the Korean seas for a winter monsoon period of January 1997. The wind field used in the present study is the global NSCAT-ERS/NCEP blended winds, which was further interpolated using a bi-cubic spline interpolator to fine grid limited area shallow water regime surrounding the Korean seas. To evaluate and investigate the accuracy of WAM, the hindcasted wave heights are compared with observed data from two shallow water buoys off Chil-Bal and Duk-Juk. A detailed study has been carried with the various meteorological parameters in observed buoy data and its inter-dependency on model computed wave fields was also investigated. The RMS error between the observation and model computed wave heights results to 0.489 for Chil-Bal and 0.417 for Duk-Juk. A similar comparison between the observation and interpolated winds off Duk-Juk show RMS error of 2.28 which suggest a good estimate for wave modelling studies.

• Atmosphere
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• v.16 no.4
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• pp.303-318
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• 2006

A parameterization of gravity wave drag induced by cumulus convection (GWDC) proposed by Chun and Baik is implemented in the KMA operational global NWP model (GDAPS), and effects of the GWDC on the forecast for July 2005 by GDAPS are investigated. The forecast result is compared with NCEP final analyses data (FNL) and model's own analysis data. Cloud-top gravity wave stresses are concentrated in the tropical region, and the resultant forcing by the GWDC is strong in the tropical upper troposphere and lower stratosphere. Nevertheless, the effect of the GWDC is strong in the mid- to high latitudes of Southern Hemisphere and high latitudes of Northern Hemisphere. By examining the effect of the GWDC on the amplitude of the geopotential height perturbation with zonal wavenumbers 1-3, it is found that impact of the GWDC is extended to the high latitudes through the change of planetary wave activity, which is maximum in the winter hemisphere. The GWDC reduces the amplitude of zonal wavenumber 1 but increases wavenumber 2 in the winter hemisphere. This change alleviates model biases in the zonal wind not only in the lower stratosphere where the GWDC is imposed, but also in the whole troposphere, especially in the mid- to high latitudes of Southern Hemisphere. By examining root mean square error, it is found that the GWDC parameterization improves GDAPS forecast skill in the Southern Hemisphere before 7 days and partially in the Northern Hemisphere after about 5 days.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.2
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• pp.84-94
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• 2018

In order to overcome the limitation of deterministic forecast, an ensemble forecasting system for regional ocean wave is developed. This system predicts ocean wind waves based on the meteorological forcing from the Ensemble Prediction System for Global of the Korea Meteorological Administration, which is consisted of 24 ensemble members. The ensemble wave forecasting system is evaluated by using the moored buoy data around Korea. The root mean squared error (RMSE) of ensemble mean showed the better performance than the deterministic forecast system after 2 days, especially RMSE of ensemble mean is improved by 15% compared with the deterministic forecast for 3-day lead time. It means that the ensemble method could reduce the uncertainty of the deterministic prediction system. The Relative Operating Characteristic as an evaluation scheme of probability prediction was bigger than 0.9 showing high predictability, meaning that the ensemble wave forecast could be usefully applied.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.5
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• pp.15-20
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• 2014

The wind industry grew in the first decade of the 21st century at rates consistently above 20% a year. For wind turbine, gearbox failure can be extremely costly in terms of repair costs, replacement parts, and in lost power production due to downtime. In this paper, gear tooth micro-modification for the high speed stage was used to compensate for the deformation of the teeth due to load and to ensure a proper meshing to achieve an optimized tooth contact pattern. The gearbox was firstly modeled in a software, and then the various combined tooth modification were presented, and the prediction of transmission under the loaded torque for the helical gear pair was investigated, the normal load distribution and root stress were also obtained and compared before and after tooth modification under one torque. The simulation results showed that the transmission error and normal load distribution under the load can be minimized by the appropriate tooth modification. It is a good approach where the simulated result is used to improve the design before the prototype is available for the test.

• Atmosphere
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• v.18 no.3
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• Journal of Environmental Health Sciences
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• v.35 no.4
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• pp.304-314
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• 2009

A new harbor as been constructing in Gadukdo. However, a lot of fugitive dust gas been often generated from construction site reclaiming sea sand, especially when the Northwester is blown strongly. It has resulted insome appeals of residents in Gadukdo. In this study, we estimated the amount of fugitive dust caused by new harbor construction using Fugitive dust formula. Also, the concentration of PM10 for recipient is predicted by AERMOD. The amount of fugitive dust is 26.56 ${\mu}g/sec{\cdot}m^2$ and 11.84 ${\mu}g/sec{\cdot}m^2$ respectively by the Fugitive dust formula. PM10 outlet concentration and the amount of fugitive dust increase according to wind velocity and directions. AERMOD is performed on the basis of weather data and the amount of fugitive dust generated with wind velocity. As a result of AERMOD, the PM10 concentration of Sunchang and Oinul are predicted over 100 ${\mu}g/m^3$. The PM10 concentration of Sunchang and Oinul are predicted over 130 ${\mu}g/m^3$ when wind velocity of northwester in winter is over 11 m/s (Air Quality for Particulate Matter (100 ${\mu}g/m^3$ for 24 hours)). Also, the measured error between AERMOD and actual measurement is lower than 5%.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Agricultural and Biosystems Engineering
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• v.7 no.1
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• pp.1-7
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• 2006

A need exists to monitor and control the localized high temperatures often experienced in solar grain dryers, which result in grain cracking, reduced germination and loss of cooking quality. A verified finite element model would be a useful to monitor and control the drying process. This study examined the feasibility of the finite element method (FEM) to predict temperature distribution in solar grain dryers. To achieve this, an indirect solar grain dryer system was developed. It consisted of a solar collector, plenum and drying chambers, and an electric fan. The system was used to acquire the necessary input and output data for the finite element model. The input data comprised ambient and plenum chamber temperatures, prevailing wind velocities, thermal conductivities of air, grain and dryer wall, and node locations in the xy-plane. The outputs were temperature at the different nodes, and these were compared with measured values. The ${\pm}5%$ residual error interval employed in the analysis yielded an overall prediction performance level of 83.3% for temperature distribution in the dryer. Satisfactory prediction levels were also attained for the lateral (61.5-96.2%) and vertical (73.1-92.3%) directions of grain drying. These results demonstrate that it is feasible to use a two-dimensional (2-D) finite element model to predict temperature distribution in a grain solar dryer. Consequently, the method offers considerable advantage over experimental approaches as it reduces time requirements and the need for expensive measuring equipment, and it also yields relatively accurate results.