• Journal of Environmental Science International
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• v.5 no.4
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• pp.399-410
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• 1996

Pusan is the largest coastal city with a population of about four mi18ion in Korea. Because of increased and confused traffic, photochemical air pollution become a major urban environmental problem recently. The photo-chemical air pollution weather forecasting method preciser than existing air pollution forecast method has been developed to forecast ozone episode days with meteorological conditions using the data measured at 7 air quality continuous monitoring stations from lune to September using 2 years (1994, 1995). The method developed in present study showed higher percentage correct and skill score than existing air pollution forecasting in KMA ( Korea Meteorological Administration).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2349-2353
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• 2009

Due to high oil price and global warming of the earth, investments for renewable energy have been increased a lot continuously. Specially, wind power has been received a great attention in the world. In order to construct a new wind farm, forecasting of wind power generation is essential for a feasibility test. This paper investigates wind velocity measurement data of Gosan weather station which located in Hankyung of Jeju island. This paper presents results of estimation of wind power generation using digital weather forecast provided from Korea meteorological administration, and the accuracy of the wind power forecasting by comparison between forecasted data and actual wind power data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.619-622
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• 2015

Recently, there are growing fluctuations of productivity and price caused by severe weather conditions in the agriculture. Yield forecasting methods have been studied to solve the problems. This paper predicted yield per area, production area, and elements of weather based on the linear equation. A yield is calculated by multiplying the production area times the yield per area that is compensated using the weighted sum of the elements of weather. In experiments, proposed method shows that a forecasting precision is the more than 90%.

• Magazine of the Korean Society of Agricultural Engineers
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• v.41 no.6
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• pp.25-32
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• 1999

The purpose of this study is to analyse the accuracy of weather forecasts of temperature, precipitation probability , and sky condition and to evaluate the applicability of weather forecasts for the estimation of potential evapotranspiration for irrigation scheduling. Five weather station s were selected to compare forecasted and measured climatcal data. The error between forecasted and measured temperature was calculated and discussed. The accuracy of temperature forecast using relative frequency of the error was calculated . The temperature forecasting showed considerably high accuracy. Average sunshine hours for forecasted sky conditions were calculated and showed reasonable quality. From the reliability graphs, the forecasting precipation probabililty was reliable. Potential evapotranspirations were calculated and compared using forecast and measured temperatures. The weather forecast is considered usable for irrigation scheculing.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.37-48
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• 2010

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of $240\;m{\times}240\;m$ based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

• Journal of The Korean Society of Agricultural Engineers
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• v.50 no.3
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• pp.71-79
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• 2008

Generally the price of agricultural products has much different characteristics from that of manufacturing products. If products have the limitation of long-term storage and the short period of cultivation, the price of products can be more unstable. Moreover, the price forecasting is very difficult because it doesn't follow any cycle or trend. However price can be regarded as risk instead of uncertainty if we can calculate the probability of price. Reliability analysis techniques are used for forecasting the price change of Chinese cabbage. This study aims to show the usability of reliability analysis for price forecasting. A price-forecasting model was developed based on weather data of the first 10 days of the full cultivating cycle (80 days) 70 days and the average price and standard deviation of wholesale market prices from 1996 to 2001 and applied to forecast the boom price, or the orice which is over the tolerance of market prices, of upland Chinese cabbage in 2002 and 2003. Applied results showed the possibility of boom price forecasting using reliability analysis techniques.

• Journal of Environmental Science International
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• v.26 no.10
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• pp.1167-1180
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• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• Journal of Institute of Convergence Technology
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• v.9 no.1
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• pp.31-36
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• 2019

After 30 years of KKP model analysis and extended 30 years of accuracy analysis, the unique correlation and various problems between measured horizontal surface insolation and measured weather data are found in this paper. The KKP model's 10yrs daily total horizontal surface insolation forecasting was averaged about 97.7% on average, and the forecasting accuracy at peak times per day was about 92.1%, which is highly applicable regardless of location and weather conditions nationwide. The daily total solar radiation forecasting accuracy of the modified KKP cloud model was 98.9%, similar to the KKP model, and 93.0% of the forecasting accuracy at the peak time per day. And the results of evaluating the accuracy of calculation for 30 years of KKP model were cloud model 107.6% and cloud model 95.1%. During the accuracy analysis evaluation, this study found that inaccuracies in measurement data of cloud cover should be clearly assessed by the Meteorological Administration.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1023-1034
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• 2008

The size and frequency of the natural disaster related to the severe storms are increased for recent decades in all over the globe. The damage from natural disasters such as typhoon, storm and local severe rainfall is very serious in Korea since they are concentrated on summer season. These phenomena will be more frequent in the future because of the impact of climate change related to increment of $CO_2$ concentration and the global warming. To reduce the damage from severe storms, a short-range precipitation forecasting model using a weather radar was developed. The study was conducted as following four tasks: conversion three-dimensional radar data to two-dimensional CAPPI(Constant Altitude Plan Position Indicator) efficiently, prediction of motion direction and velocity of a weather system, estimation of two-dimensional rainfall using operational calibration. Results demonstrated that two-dimensional estimation using weather radar is useful to analyze the spatial characteristics of local storms. If the precipitation forecasting system is linked to the flood prediction system, it should contribute the flood management and the mitigation of flood damages.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.7-14
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• 2023

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people's life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing 'heavy snow' in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.