• Journal of the Korean Applied Science and Technology
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• v.38 no.6
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• pp.1355-1369
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• 2021

This study improved the algorithm for the road ice prediction algorithm and analyzed the prediction rate when comparing actual field measurement data and algorithm prediction value. For analysis, road and weather conditions were measured in Geumdong-ri, Sinbuk-myeon, Pocheon-si. First algorithm selected previous research result algorithm. And the 4th algorithm was improved according to the actual freezing conditions and measured values. Finally, five algorithms were developed: freezing by condensation, freezing by precipitation, freezing by snow, continuous freezing, and freezing by wind speed. When forecasting using an algorithm at the Pocheon site, the freezing hit rate was improved to 93.2%. When calculating the combination ratio for the algorithm. the algorithm for freezing due to condensation and the continuation of the frozen state accounted for 95.7%.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1275-1288
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• 2015

A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.

• Research in Plant Disease
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• v.10 no.3
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• pp.167-174
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• 2004

To make the forecasting model of rice bacterial grain rot (RGBR) using the statistical procedures with SAS(Statistical Analysis System) based on micro-weather factors during heading period of rice, 21 rice varieties having the different heading time (40％ panicles headed) were planted at 30 May and 15 June in Naju. Heading time and diseased panicles were investigated from July to August in 1998. RGBR mainly occurred on varieties headed from 29 July to 19 August, but not on varieties headed after 22 August. RGBR was highly correlated with diurnal temperature during 7 days (r =-0.871 **) and 10 days (r =-0.867**) and minimum relative humidity during 15 days from 3 days before heading time. After examining the models with several ways ($R^2$, Adjusted $R^2$, MSE), one equations were selected： Y =92.83 - 2.43Tavr + 1.88Tmin - 1.04RHavr + 0.37RHmin + 0.43RD - 3.68WS ($R^2$=0.824) using six variables of average and minimum temperature (Tavr and Tmin), average and minimum relative humidity (RHavr and RHmin), rainy days (RD), and wind speed (WS) during 7 days from 3 days before to 3 days after heading time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.116-118
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• 2023

In recent years, the need for economical and sustainable ship routing has emerged due to the enforced regulations on environmental issues. Despite the development of weather forecasting technology, maritime accidents by rough waves have continued to occur due to incorrect weather forecasts. In this study, onboard measurements are conducted to observe the acutal situation on merchant ships in operation encountering rough waves. The types of measured data include information related to navigation (Ship's position, speed, bearing, rudder angle) and engine (engine revolutions, power, shaft thrust, fuel consumption), weather conditions (wind, waves), and ship motions (roll, pitch, and yaw). These ship experiments was conducted to 28,000 DWT bulk carrier, 63,000 DWT bulk carrier, 20,000 TEU container ship, and 12,000 TEU container ship. The actual ship experiment of each ship is intended to acquire various types of data and utilize them for multi-objective studies related to ship operation. Additionally, in order to confirm the sea conditions, the directional wave spectrum was reproduced using a wave simulation model. Through data collection from ship experiments and wave simulations, various studies could be proceeding such as the measurement for accurate wave information by marine radar and analysis for cargo collapse accidents. In addition, it is expected to be utilized in various themes from the perspective of safety and efficiency in ship operation.

• Journal of the Korean earth science society
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• v.36 no.6
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• pp.567-579
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• 2015

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7 and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately $0.3^{\circ}C$, $0.2m\;s^{-1}$, and 2.2%, respectively after incorporating the FDDA.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.