• Journal of Astronomy and Space Sciences
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• v.35 no.3
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• pp.175-183
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• 2018

Many recent satellites have mission periods longer than 10 years; thus, satellite-based local space weather monitoring is becoming more important than ever. This article describes the instruments and data applications of the Korea Space wEather Monitor (KSEM), which is a space weather payload of the GeoKompsat-2A (GK-2A) geostationary satellite. The KSEM payload consists of energetic particle detectors, magnetometers, and a satellite charging monitor. KSEM will provide accurate measurements of the energetic particle flux and three-axis magnetic field, which are the most essential elements of space weather events, and use sensors and external data such as GOES and DSCOVR to provide five essential space weather products. The longitude of GK-2A is $128.2^{\circ}E$, while those of the GOES satellite series are $75^{\circ}W$ and $135^{\circ}W$. Multi-satellite measurements of a wide distribution of geostationary equatorial orbits by KSEM/GK-2A and other satellites will enable the development, improvement, and verification of new space weather forecasting models. KSEM employs a service-oriented magnetometer designed by ESA to reduce magnetic noise from the satellite in real time with a very short boom (1 m), which demonstrates that a satellite-based magnetometer can be made simpler and more convenient without losing any performance.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.4 no.2
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• pp.155-159
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• 1999

The mini-weather buoy using newly developed Weather Observation Through Ambient Noise (WOTAN) technology is developed. The buoy uses the cellular phone system for communication between the mini-weather buoy and the receiving station. The developed mini-weather buoy was deployed near Kijang and the comparison with land observation station was good: the rms error for wind speed estimation from underwater ambient noise was about 1 m/s. The only shortcoming of developed mini-weather buoy is that the buoy must be within the range of the cellular phone system, but it can be easily solved when satellite phone system is available.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.398-402
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• 2011

High resolution windspeed profile measurements are needed in a weather radar to provide the reliable information of rapidly changing weather conditions. However, it is necessary to remove both stationary and moving clutter to obtain the accurate pulse pair estimates. To overcome these problems, a simple adaptive array antenna may be applied to clutter removal. Using the simulated weather and clutter data, the clutter cancellation capability is analyzed for a weather radar with an adaptive antenna. The pulse pair estimates obtained from the adaptive weather radar are compared with those of the raw data.

• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.1-11
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• 2018

In this work, we propose weather prediction models to estimate hourly outdoor temperatures and solar irradiance in the next day using forecasting information. Hourly weather data predicted by the proposed models are useful for setting system operating strategies for the next day. The outside temperature prediction model considers 3-hourly temperatures forecasted by Korea Meteorological Administration. Hourly data are obtained by a simple interpolation scheme. The solar irradiance prediction is achieved by constructing a dataset with the observed cloudiness and correspondent solar irradiance during the last two weeks and then by matching the forecasted cloud factor for the next day with the solar irradiance values in the dataset. To verify the usefulness of the weather prediction models in predicting a short-term building load, the predicted data are inputted to a TRNSYS building model, and results are compared with a reference case. Results show that the test case can meet the acceptance error level defined by the ASHRAE guideline showing 8.8% in CVRMSE in spite of some inaccurate predictions for hourly weather data.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.1
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• pp.98-118
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• 2014

An auto weather-vaning system for a Dynamic Positioning (DP) vessel is proposed. When a DP vessel is operating, its position keeping is hindered by ocean environmental disturbances which include the ocean current, wave and wind. Generally, most ocean vessels have a longitudinal length that is larger than the transverse width. The largest load acts on the DP vessel by ocean disturbances, when the disturbances are incoming in the transverse direction. Weather-vaning is the concept of making the heading angle of the DP vessel head toward (or sway from) the disturbance direction. This enables the DP vessel to not only perform marine operations stably and safely, but also to maintain its position with minimum control forces (surge & sway components). To implement auto weather-vaning, a nonlinear controller and a disturbance observer are used. The disturbance observer transforms a real plant to the nominal model without disturbance to enhance the control performance. And the nonlinear controller deals with the kinematic nonlinearity. The auto weather-vaning system is completed by adding a weather-vaning algorithm to disturbance based controller. Numerical simulations of a semi-submersible type vessel were performed for the validation. The results show that the proposed method enables a DP vessel to maintain its position with minimum control force.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1435-1448
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• 2021

Recent years, due to the direct or indirect damages caused by meteorological disasters more and more attention have been paid to natural disasters. At same time, diversified and multi-sensory interactive meteorological services is increasingly demanded. In this study, novel interactive meteorological service was compared with the traditional communication methods. Combining with case studies and systems creation, a virtual reality weather simulation framework was proposed, and a realistic virtual game environment providing real-time and historical weather information was created. The primary goal of this study is to build a weather display cabinet game system by using virtual reality technology, and promoting public's understanding of the principles of weather changes. With the interactive games in realistic scenarios, public's awareness for disasters prevention could be promoted. It is helping to change public's traditional understanding of meteorological theories, and will provide a more convenient way for the public to explore more effective weather forecasts. The simulation system is supported by VR technology. It was combined with Leap Motion interactive equipment to make popularization games for weather science. T-test data analysis showed that the application of VR technology in weather games has strong operability and interactivity.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.461-470
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• 2018

The frequency of extreme weather events was analyzed using meteorological data (air temperature, precipitation, and duration of sunshine) collected from 61 stations over a 36-year span (1981-2016). The 10-day meteorological data were used as a basic unit for this analysis. On average, the frequency of occurrence of abnormal weather was 9.88 per year and has increased significantly during this 36-year period. According to the type of abnormal weather, the frequencies of occurrence of abnormally high air temperature and short duration of sunshine have increased by 0.50 and 0.41 per 10 years, respectively; however, that for abnormally low air temperature has decreased by 0.31 per 10 years and the trend was statistically significant. The highest frequency of abnormal weather appeared in 2007, with a frequency of 14.31. Abnormal weather was the most frequent at Yeongdeok station with an average frequency of 11.78 per year over this 36-year span.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.108.2-109
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• 2003

The Gyeonggi-do Agricultural Research and Extension Services has developed a web-site (www.epilove.com) in collaboration with EPINET to provide information on agricultural weather and rice disease and insect pest management in Gyeonggi-do. Weather information includes near real-time weather data monitored by automated weather stations (AWS) installed at rice paddy fields of 11 Agricultural Technology Centers (ATC) in Gyeonggi-do, and weekly weather forecast by Korea Meteorological Administration (KMA). Map images of hourly air temperature and rainfall are also generated at 309m x 309m resolution using hourly data obtained from AWS installed at 191 locations by KMA. Based on near real-time weather data from 11 ATC, hourly infection risks of rice blast, sheath blight, and bacterial grain rot for individual districts are estimated by disease forecasting models, BLAST, SHBLIGHT, and GRAINROT. Users can diagnose various diseases and insects of rice and find their information in detail by browsing thumbnail images of them. A database on agrochemicals is linked to the system for disease and insect diagnosis to help users search for appropriate agrochemicals to control diseases and insect pests.