• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Journal of the Korean earth science society
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• v.36 no.4
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• pp.301-314
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• 2015

The purpose of this study was to investigate the impact of ground-based glaciogenic seeding on orographic clouds in the Daegwallyeong area on 13 March, 2013. The experiments was conducted by releasing silver iodide (AgI) under following conditions: surface temperature below $-4^{\circ}C$, wind direction between 45 and $130^{\circ}$, and wind speed less than $5ms^{-1}$. Two seeding rates, $38gh^{-1}$ (SR1) and $113gh^{-1}$ (SR2), were tested to obtain an appropriate AgI ratio for snowfall enhancement in the Daegwallyeong area. Numerical simulations were carried out by using the WRF (Weather Research and Forecast) model with AgI point-source module which predicted dispersion fields of AgI particles. The results indicated that the target orographic clouds contained adequate amount of supercooled liquid water and that the dispersion of AgI particles tended to move along the prevailing wind direction. To validate the seeding effects, the observation data from FM-120 and MPS as well as PARSIVEL disdrometer were analyzed. In this case study, glaciogenic seeding significantly increased the concentration of small ice particles below 1 mm in diameter. The observation results suggest that SR1 seeding be reasonable to use the ground-based seeding in the Daegwallyeong area.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1581-1586
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• 2016

In this paper, we propose a central disaster monitoring system of the forest fire. This system provides the safe-zone and detection to reduce the suppression efforts. In existing system, it has a few providing the predicting of wildfire spread model and speed through topography, weather, fuel factor. This paper focus on the forest fire diffusion model and predictions of the path identified to ensure the safe zone. Also we have considering the forest fire of moving direction and speed for fire suppression and monitering. The proposed algorithm could provide the technique to analyze the attribute information that temperature, wind, smoke measured over time. This proposed central observing monitoring system could provide the moving direction of spred out forecast wildfire. This observing and monitering system analyze and simulation for the moving speed and direction forest fire, it could be able to predict and training the forest fire fighters in a given environment.

• Journal of Environmental Science International
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• v.26 no.6
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• pp.741-750
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• 2017

A model coupling a meteorological predictive model and a vegetation photosynthesis and respiration model was used to simulate $CO_2$ concentrations over coastal basin areas, and modeling results were estimated with aircraft observations during a massive sampling campaign. Along with the flight tracks, the model captured the meteorological variables of potential temperature and wind speed with mean bias results of $0.8^{\circ}C$, and 0.2 m/s, respectively. These results were statistically robust, which allowed for further estimation of the model's performance for $CO_2$ simulations. Two high-resolution emission data sets were adopted to determine $CO_2$ concentrations, and the results show that the model underestimated by 1.8 ppm and 0.9 ppm at higher altitude over the study areas during daytime and nighttime, respectively, on average. Overall, it was concluded that the model's $CO_2$ performance was fairly good at higher altitude over the study areas during the study period.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.220-228
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• 2022

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

• Atmosphere
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• v.14 no.4
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• pp.3-18
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• 2004

Neglecting the vertical transport from the surface, most of the previous studies on the long-range transport of pollutants have only considered the horizontal transport caused by the free atmosphere wind. I used a three dimensional numerical model, MM5 (The fifth generation Penn State Univ./NCAR Mesoscale Model) for the simulation of vertical transport of pollutants and investigated the mechanism of the vertical transport of atmospheric pollutants between planetary boundary layer(PBL) and free atmosphere by fronts. From the three dimensional simulation of MM5, the amount of pollutants transport from PBL to free atmosphere is 48% within 18 hour after the development of front, 55% within 24 hour, and 53% within 30 hour. The ratios of the vertically transported pollutant for different seasons are 62%, 60%, 54%, and 43% for spring, summer, fall, and winter, respectively. The most active areas for the vertical transport are the center of low pressure and the warm sector located east side of cold front, in which the strong upward motion slanted northward occurs. The horizontal advection of pollutants at the upper level is stronger than at the lower level simply because of the stronger wind speed. The simulation results shows the well known plum shape distribution of pollutants. The high concentration area is located in the center and north of the low pressure system, while the second highest concentration area is in the warm sector. It is shown that the most important mechanism for the vertical transport is vertical advection, while the vertical diffusion process plays an important role in the redistribution of pollutants in the PBL.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.53-62
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• 2020

This study is to evaluate the accuracy of the meteorological information provided for the aircraft operating at low altitude. At first, it is necessary to identify crucial elements of weather information closely related to flight safety during low altitude flights. The study conducted a survey of pilots of low altitude aircraft, divided into pre-flight and in-flight phases, and reached an opinion that wind direction, wind speed, cloud coverage and ceiling and visibility are important items. Related to these items, we compared and calculated the accuracy of TAFs and METARs from Taean Airfield, Seosan Airport and Gunsan Airport because of their high number of domestic low-altitude flights. Accuracy analysis evaluated the accuracy of two numerical variables, Mean Absolute Error(MAE) and Root Mean Square Error(RMSE), and the cloud coverage which is categorical variable was calculated and compared by accuracy. For numeric variables, one-way ANOVA, which is a parameter-test, was approached to identify differences between actual forecast values and observations based on absolute errors for each item derived from the results of MAE and RMSE accuracy analyses. To determine the satisfaction of both normality assumptions and equivalence variability assumptions, the Shapiro-Wilk test was performed to verify that they do not have a normality distribution for numerical variables, and for the non-parametric test, Kruscal-Wallis test was conducted to determine whether or not they are satisfied.