• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.3
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• pp.209-215
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• 2007

To improve the accuracy of frost warning system for pear orchard in a complex terrain in Naju area, the daily minimum temperature forecasted by Korea Meteorological Administration (KMA) was interpolated using a regional climate model based on topoclimatic estimation and optimum scale interpolation from 2004 to 2005. Based on the validation experiments done for three pear orchards in the spring of 2004, the results showed a good agreement between the observed and predicted values, resulting in improved predictability compared to the forecast from Korea Meteorological Administration. The differences between the observed and the predicted temperatures were $-2.1{\sim}2.7^{\circ}C$ (on average $-0.4^{\circ}C$) in the valley, $-1.6{\sim}2.7^{\circ}C$ (on average $-0.4^{\circ}C$) in the riverside and $-1.1{\sim}3.5^{\circ}C$ (on average $0.6^{\circ}C$) in the hills. Notably, the errors have been reduced significantly for the valley and riverside areas that are more affected by the cold air drainage and more susceptible to frost damage than hills.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.34-61
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• 2017

The objective of this study is to assess the capability of meteorological technology in Korea, the United States, Japan, and the United Kingdom as of 2015 and compare them with the previous values for Korea, the United States, and Japan. For the comparison, the indicators and weights for the assessment similar to those used in previous studies are used and Gordon's rating model is applied here to evaluate the indicators and conduct a survey of weather experts. The survey was administered to 200 of experts in meteorology using the Delphi method. More specifically, we investigate four categories of observation, data processing, forecast, and climate. The overall results show that the United Kingdom has the highest capability of meteorological technology among the four countries. With the result of indicator evaluation on this study the United Kingdom has the highest capability of meteorological technologies compared with Korea, the United States, and Japan. The capability of meteorological technology in Korea is 88.5% of the United Kingdom, 89.9% of Japan, and 90.6% of the United States. The countries in order of score on survey evaluation are the United States, the United Kingdom, Japan, and Korea. Through the result of survey evaluation, the level of meteorological technology in Korea was 88.9% of the United States, 91.6% of the United Kingdom, and 92.2% of Japan.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.89-92
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• 2015

In this paper, we propose a method to reduce the fire suppression time. Our suggestions can secure a safe area according to the diffusion path and speed of the fire, forest fire prediction minimize casualties and property damage forests. The existing path prediction method wildfire spread predict the wildfire spread model and speed through topography, weather, fuel factor and the image information. In this case, however, occur to control a large mountain huge costs. Also Focus on the diffusion model predictions and the path identified by the problem arises that insufficient efforts to ensure the safe area. In this paper, we estimate the moving direction and speed of fire at a lower cost, and proposes an algorithm to ensure the safety zone for fire suppression. The proposed algorithm is a technique to analyze the attribute information that temperature, wind, smoke measured over time. According to our algorithm forecast wildfire moving direction and ensure the safety zone. By analyzing the moving speed and the moving direction of the simulated fire in a given environment is expected to be able to quickly reduce the damage to the forest fire fighters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.294-297
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• 2015

Recently, big data analysis which is based on machine learning has been gained a lot of attentions in various fields. Especially, agriculture is considered as one promising field that machine learning algorithm can be efficiently utilized and accordingly, lots of works have been done so far. However, most of the researches are focusing on the forecast of weather or analysis of genome, and machine learning algorithm for livestock management, especially which uses individual data of livestocks, e.g., temperature and movement, are not properly investigated yet. In this work, we propose fast abnormal livestock detection algorithm based on machine learning, more specifically expectation maximization, such that livestock which has problem can be efficiently and promptly found. In our proposed scheme, livestocks are divided into two clusters using expectation maximization based on their bionic data and the abnormal livestock can be detected by comparing the size of two clusters. Especially, we divide the case in which single livestock has problem and the case in which livestocks have epidemic such that fast response is enabled when epidemic case. Moreover, our algorithm does not need statistical information.

• Journal of the Korean earth science society
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• v.32 no.4
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• pp.360-372
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• 2011

In order to clarify the advection and dispersion characteristics of volcanic tephra to be emitted from the Mt. Baekdu, several numerical experiments were carried out using three-dimensional atmospheric dynamic model, Weather and Research Forecast (WRF) and Laglangian particles dispersion model FLEXPART. Four different temporally averaged meteorological values including wind speed and direction were used, and their averaged intervals of meteorological values are 1 month, 10 days, and 3days, respectively. Real time simulation without temporal averaging is also established in this study. As averaging time of meteorological elements is longer, wind along the principle direction is stronger. On the other hands, the tangential direction wind tends to be clearer when the time become shorten. Similar tendency was shown in the distribution of volcanic tephra because the dispersion of particles floating in the atmosphere is strongly associated with wind pattern. Wind transporting the volcanic tephra is divided clearly into upper and lower region and almost ash arriving the Korean Peninsula is released under 2 km high above the ground. Since setting up the temporal averaging of meteorological values is one of the critical factors to determine the density of tephra in the air and their surface deposition, reasonable time for averaging meteorological values should be established before the numerical dispersion assessment of volcanic tephra.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.59.5-60
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• 2017

We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.159-173
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• 2017

The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

• Atmosphere
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• v.25 no.4
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• pp.639-657
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• 2015

There is an increasing need to improve the air quality over South Korea to protect public health from local and remote anthropogenic pollutant emissions that are in an increasing trend. Here, we evaluate the performance of the WRF-Chem (Weather Research and Forecasting-Chemistry) model in simulating near-surface air quality of major Korean cities, and investigate the impacts of time-varying chemical initial and lateral boundary conditions (IC/BCs) on the air quality simulation using a chemical downscaling technique. The model domain was configured over the East Asian region and anthropogenic MICS-Asia 2010 emissions and biogenic MEGAN-2 emissions were applied with RACM gaseous chemistry and MADE/SORGAM aerosol mechanism. Two simulations were conducted for a 30-days period on April 2010 with chemical IC/BCs from the WRF-Chem default chemical species profiles ('WRF experiment') and the MOZART-4 (Model for OZone And Related chemical Tracers version 4) ('WRF_MOZART experiment'), respectively. The WRF_MOZART experiment has showed a better performance to predict near-surface CO, $NO_2$, $SO_2$, and $O_3$ mixing ratios at 7 major Korean cities than the WRF experiment, showing lower mean bias error (MBE) and higher index of agreement (IOA). The quantitative impacts of the chemical IC/BCs have depended on atmospheric residence time of the pollutants as well as the relative difference of chemical mixing ratios between the WRF and WRF_MOZART experiments at the lateral boundaries. Specifically, the WRF_MOZART experiment has reduced MBE in CO and O3 mixing ratios by 60~80 ppb and 5~10 ppb over South Korea than those in the WRF-Chem default simulation, while it has a marginal impact on $NO_2$ and $SO_2$ mixing ratios. Without using MOZART-4 chemical IC, the WRF simulation has required approximately 6-days chemical spin-up time for the East Asian model domain. Overall, the results indicate that realistic chemical IC/BCs are prerequisite in the WRF-Chem simulation to improve a forecast skill of local air quality over South Korea, even in case the model domain is sufficiently large to represent anthropogenic emissions from China, Japan, and South Korea.

• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.247-255
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• 2016

Non-point pollutants from surface runoff during rainfall exert adverse effects on urban river water quality management. In particular, the first flush effect during the initial phase of rainfall can deliver significant amounts of pollutant loads to surface waters with extremely high concentrations. In this study, a sustainable first flush effect management system was developed by using settling and filtration that require no additional power or chemicals. A pilot scale experiment has shown that the removal of total suspended solid (TSS), total nitrogen (TN) and total phosphorus (TP) are in ranges of 84 - 95%, 31 - 46%, and 42 - 86%, respectively. An Integrated Stormwater Runoff Management System (ISTORMS) was also developed to efficiently manage the developed system by linking weather forecast, flow rate and water quality modeling of surface runoff and automatic monitoring systems in fields and in the system. This study can provide effective solutions for the management of urban river in terms of both quantity and quality.