• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• Journal of Ocean Engineering and Technology
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• v.29 no.3
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• pp.263-269
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• 2015

This paper proposes a new approach for solving the weather routing problem by dividing it into two phases with the goal of fuel saving. The problem is to decide two optimal variables: the heading angle and speed of the ship under several constraints. In the first phase, the optimal route is obtained using the Real-Time Adaptive A* algorithm with a fixed ship speed. In other words, only the heading angle is decided. The second phase is the speed scheduling phase. In this phase, the original problem, which is a nonlinear optimization problem, is converted into a geometric programming problem. By solving this geometric programming problem, which is a convex optimization problem, we can obtain an optimal speed scheduling solution very efficiently. A simple case of numerical simulation is conducted in order to validate the proposed method, and the results show that the proposed method can save fuel compared to a constant engine output voyage and constant speed voyage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.471-484
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• 2017

GNSS PWV (Precipitable Water Vapor) is recognized as an important factor for weather forecasts of typhoons and heavy rainfall. Domestic and foreign research have been published that improve weather forecasts using GNSS PWV as initial input data to NWP (Numerical Weather Prediction) model. For rainfall-related weather forecasts, PWV should be provided in real time or NRT (Near-Real Time) and the accuracy and integrity should be maintained. In this paper, the development process of NRT GNSS PWV system using PPP (Precise Point Positioning). To this end, we optimized the variables related to tropospheric delay estimation of PPP. For the analysis of the PPP NRT PWV system, we compared the PWV precision of RP (Relative Positioning) and PPP. As a result, the accuracy of PPP was lower than that of RP, but good results were obtained in the PWV data integrity. Future research is needed to improve the precision of PWV in the PPP method.

• Journal of Drive and Control
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• v.16 no.4
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• pp.48-55
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• 2019

The Electronic Stability Program (ESP), a system that improves vehicle safety, also known as YMC (Yaw Motion Controller) or VDC (Vehicle Dynamics Control), is a system that operates in unstable or sudden driving and braking situations. Developing conditions such as unstable or sudden driving and braking situations in a vehicle are very dangerous unless you are an experienced professional driver. Additionally, many repetitive tests are required to collect reliable data, and there are many variables to consider such as changes in the weather, road surface, and tire condition. To overcome this problem, in this paper, hardware and control software such as the ESP controller, vehicle engine, ABS, and TCS module, composed of three control zones, are modeled using MATLAB/SIMULINK, and the vehicle, climate, and road surface. Various environmental variables such as the driving course were modeled and studied for the real-time ESP real-time simulator that can be repeatedly tested under the same conditions.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.1-8
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• 2017

In recent years, the quixotic nature and concentration of rainfall due to global climate change has intensified. To monitor localized heavy rainfalls, a reliable disaster monitoring and warning system with advanced remote observation technology and high-precision display is important. In this paper, we propose a GIS-based intuitive and realistic 3D radar data display technique for accurate and detailed weather analysis. The proposed technique performs 3D object modeling of various radar variables along with ray profiles and then displays stereoscopic radar data on detailed geographical locations. Simulation outcomes show that 3D object modeling of weather radar data can be processed in real time and that changes at each moment of rainfall events can be observed three-dimensionally on GIS.

• Research in Plant Disease
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• v.8 no.4
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• pp.199-206
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• 2002

It was investigated on the relationship of the rice blast epidemics and the real-time meteorological factors, at the experimental paddy field in 1997. Weather factors(temperature, relative humidity, irradiation, precipitation, the direction of wind, wind speed, soil temperature and leaf-wetness, etc) were measured by using the automated weather station. The most influenced weather factor to blast epidemics, was the average max-temp($R^2$= 0.95) during 10 days before leaf blast epidemics, while the least thing was wind speed($R^2$= 0.24). The most potential weather factors correlated with the blast epidemics were T-ave(average temperature), T-max(maximum temperature), RH(Relative Humidity) and RD(Relative Humidity > 90% hrs). A statistics model(the regression equation) of the blast epidemics with the potential weather factors, was established as tallows ; Y = -3410.91 - 23.91 $\times$ T-ave ＋ 28.56 $\times$ T-max ＋ 41.0 $\times$ RH - 3.75 $\times$ RD, ($R^2$= 0.99). (T-ave >= 19$^{\circ}C$, T-max - T-ave >= 5.2$^{\circ}C$ and RH% >= 90.4%). According to the fitness test($\chi$$^2$) of the model, the observed blast disease severity was quite close to those expected.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.287-296
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• 2022

Today, air pollution is becoming a severe issue worldwide and various policies are being implemented to solve environmental pollution. In major cities, public bicycles are installed and operated to reduce pollution and solve transportation problems, and operational information is collected in real time. However, research using public bicycle operation information data has not been processed. This study uses the daily weather data of Korea Meteorological Agency and real-time air pollution data of Korea Environment Corporation to predict the amount of daily rental bicycles. Cross- validation, principal component analysis and multiple regression analysis were used to determine the independent variables of the predictive model. Then, the study selected the elements that satisfy the significance level, constructed a model, predicted the amount of daily rental bicycles, and measured the accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.117-127
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• 2020

The Early Warning System for agrometeorological hazard of the Rural Development Administration (Korea) forecasts detailed weather for each farm based on the meteorological information provided by the Korea Meteorological Administration, and estimates the growth of crops and predicts a meteorological hazard that can occur during the growing period by using the estimated detailed meteorological information. For verification of early warning system, automated weather observation network was constructed in the study area. Moreover, a real-time web display system was built to deliver near real-time weather data collected from the observation network. The meteorological observation system collected diverse meteorological variables including temperature, humidity, solar radiation, rainfall, soil moisture, sunshine duration, wind velocity, and wind direction. These elements were collected every minute and transmitted to the server every ten minutes. The data display system is composed of three phases: the first phase builds a database of meteorological data collected from the meteorological observation system every minute; the second phase statistically analyzes the collected meteorological data at ten-minutes, one-hour, or one-day time step; and the third phase displays the collected and analyzed meteorological data on the web. The meteorological data collected in the database can be inquired through the webpage for all data points or one data point in the unit of one minute, ten minutes, one hour, or one day. Moreover, the data can be downloaded in CSV format.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.