• Korean Journal of Remote Sensing
• /
• v.27 no.6
• /
• pp.729-741
• /
• 2011

Wind speeds have long been estimated from C-band VV-polarized SAR data by using the CMOD algorithms such as CMOD4, CMOD5, and CMOD_IFR2. Some SAR data with HH-polarization without any observations in VV-polarization mode should be converted to VV-polarized value in order to use the previous algorithms based on VV-polarized observation. To satisfy the necessity of polarization ratio (PR) for the conversion, we retrieved the conversion parameter from full-polarized SIR-C SAR image off the east coast of Korea. The polarization ratio for SIR-C SAR data was estimated to 0.47. To assess the accuracy of the polarization ratio coefficient, pseudo VV-polarized normalized radar cross section (NRCS) values were calculated and compared with the original VV-polarized ones. As a result, the estimated psudo values showed a good agreement with the original VV-polarized data with an root mean square error by 0.99 dB. We applied the psudo NRCS to the estimation of wind speeds based on the CMOD wind models. Comparison of the retrieved wind field with the ECMWF and NCEP/NCAR reanalysis wind data showed relatively small rms errors of 1.88 and 1.91 m/s, respectively. SIR-C HH-polarized SAR wind retrievals met the requirement of the scatterometer winds in overall. However, the polarization ratio coefficient revealed dependence on NRCS value, wind speed, and incident angle.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.213-225
• /
• 2018

In order to access the accuracy of the gridded daily Advanced Scatterometer (hereafter DASCAT) ocean surface wind data in the surrounding of Korea, the DASCAT was compared with the wind data from buoys. In addition, the reanalysis data for wind at 10 m provided by European Centre for Medium-Range Weather Forecasts (ECMWF, hereafter ECMWF), National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR, hereafter NCEP), Modern Era Retrospective-analysis for Research and Applications-2 (MERRA-2, hereafter MERRA) were compared and analyzed. As a result, the RMSE of DASCAT for the actual wind speed is about 3 m/s. The zonal components of wind of buoys and the DASCAT have strong correlation more than 0.8 and the meridional components of wind them have lower correlation than that of zonal wind and are the lowest in the Yellow Sea (r=0.7). When the actual wind speed is below 10 m/s, the EMCWF has the highest accuracy, followed by DASCAT, MERRA, and NCEP. However, under the wind speed more than 10 m/s, DASCAT shows the highest accuracy. In the nature of error according to the wind direction, when the zonal wind is strong, all dataset has the error of more than $70^{\circ}$ on the average. On the other hand, the RMSE of wind direction was recorded $50^{\circ}$ under the strong meridional winds. ECMWF shows the highest accuracy in these results. The RMSE of the wind speed according to the wind direction varied depending on the actual wind direction. Especially, MERRA has the highest RMSE under the westerly and southerly wind condition, while the NCEP has the highest RMSE under the easterly and northerly wind condition.

• Journal of Korean Society for Atmospheric Environment
• /
• v.24 no.5
• /
• pp.604-612
• /
• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.92-96
• /
• 2007

Microphone array beamforming method has been recognized as an important aeroacoustic research field and become a standard technique in localizing sound sources. This method also used in flight acoustic measurement, and especially, it is very useful when measure sounds inside the wind tunnel. In measuring sound which is inside the wind tunnel by traditional beamforming method, there are some errors caused by airstream. The speed and the propagation path of the sound changes as it travel through the airstream. This makes the error which the position of sound is changed a little bit to the down stream direction. In this paper, validation test has made about the correction equation for this wind effects of previous researches. And beamforming including shear layer correction was performed about a sound source in the anechoic open-jet windtunnel.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.10a
• /
• pp.337-343
• /
• 2012

NREL(National Renewable Energy Laboratory) Baseline controller conduct using method proposed RISO National Laboratory in Region 3. which designed the blade-pitch control system using a single degree-of-freedom model of the wind turbine. Idealized PID-Controlled rotor-speed error will respond as a second-order system with the natural frequency and damping ratio. RISO proposed specific natural frequency(=0.6 rad/s) and damping ratio(=0.7). If specific Eigen value apply to NREL 5 MW wind turbine, differ with pitch respond for simulation results of RISO report. Variation of specific eigen value investigate performance of NREL 5 MW wind turbine.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.4
• /
• pp.221-228
• /
• 2019

The effects of wind speed on the temperature change during day time could be insignificant in a region with a complex terrain. The objective of this study was to derive empirical relationship between solar radiation and hourly temperature under a windy condition for the period from sunrise to sunset in order to improve hourly air temperature at a site-specific scale. The deviation of the temperature measurements was analyzed along with the changes of the hourly sunlight at weather observation sites located on the east and west slopes under given wind speed. An empirical model where wind speed use used as an independent variable was obtained to quantify the solar effects on the temperature change (MJ/㎡). This model was verified estimating the hourly temperature during the daytime (0600-1900 h) at 25 weather observation sites located in the study area that has complex topography for the period from January to December 2018. The mean error (ME) and root mean square error (RMSE)of the estimated and measured values ranged from -0.98 to 0.67 ℃, and from 0.95 to 2.04 ℃, respectively. The daytime temperature at 1500 h were estimated using new and previous models. It was found that to the model proposed in the present study reduced the measurement errors of the hourly temperature in the afternoon in comparison with the previous model. For example, the ME and RMSE of the previous model were (ME -0.91 ℃ and 1.47 ℃, respectively. In contrast, the values of ME and RMSE were -0.45 ℃ and 1.22 ℃ for the new model, respectively. Our results suggested that the reliability of hourly temperature estimates at a specific site could be improved taking into account the effect of wind as well as solar radiation.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.4
• /
• pp.863-871
• /
• 2013

Control of pitch angle of turbine blades is among the controlling methods in the wind turbines; this measure is taken for managing mechanical power generated by wind turbine in different wind velocities. Taking into account the high significance of the power generated by wind turbine and due to the fact that better performance of pitch angle is followed by better quality of turbine-generated power, it is therefore crucially important to optimize the performance of this controller. In the current paper, a PI controller is primarily used to control the pitch angle, and then another controller is designed and replaces PI controller through applying a new strategy i.e. alternating two ADALINE neural networks. According to simulation results, performance of controlling system improves in terms of response speed, response ripple, and ultimately, steady tracing error. The highly significant feature of the proposed intelligent controller is the considerable stability against variations of wind velocity and system parameters.

• Korean Journal of Remote Sensing
• /
• v.34 no.1
• /
• pp.1-15
• /
• 2018

Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor(GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to $0.93^{\circ}C$ and $0.05^{\circ}C$, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above $30^{\circ}N$. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.16 no.4
• /
• pp.297-303
• /
• 2014

The effect of solar irradiance has been used to estimate daily maximum temperature, which make it possible to reduce the error inherent to lapse-rate based elevation difference correction in mountainous terrain. Still, recent observations indicated that the effect of solar radiation would need correction for estimation of daily maximum temperature. It was attempted to examine what would cause the variability of solar irradiance effect in determination of daily maximum temperature under natural field conditions and to suggest improved methods for estimation of the temperature distribution over mountainous regions. Temperature at 1500 and the wind speed for 1100 to 1500 were obtained at 10 validation sites with various topographical features including slope and aspect within a mountainous $50km^2$ catchment for 2012-2013. Lapse-rate corrected temperature estimates on clear days were compared with these observations, which would represent the differential irradiance effect among sloped surfaces. Results indicated a negative correlation between the mean wind speed and the estimation error. A simple scheme was derived from relationship between wind speed and estimation error for daily temperature to correct the effect of solar radiation. This scheme was incorporated into an existing model to estimate daily maximum temperature based on the effect of solar radiation. At 10 validation sites on clear days, estimates of 1500 LST temperature with and without the correction scheme were compared. It was found that a substantial improvement was achieved when the correction scheme was applied in terms of bias correction as well as error size reduction at all sites.

• 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.