• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.4
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• pp.844-853
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• 2005

Red tide(harmful algae) in the Korean Coastal Waters has a given a given damage to the fishery every year. However, the aim of our study understands the influence of meteorological factors (air and water temperature, precipitation sunshine, solar radiation, winds) relating to the mechanism of red tide occurrence and monitors red tide by satellite remote sensing, and analyzes the potential area for red tide occurrence by GIS. The meteorological factors have directly influenced on red tide formation. Thus, We want to predict and apply to red tide formation from statistical analyses on the relationships between red tide formation and meteorological factors. In future, it should be realized the near real time monitoring for red tide by the development of remote sensing technique and the construction of integrated model by the red tide information management system (the data base of red tide - meteorological informations. Finally our purpose is support to the prediction information for the possible red tide occurrence by coastal meteorological information and contribute to reduce the red tide disaster by the prediction technique for red tide.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.65-69
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• 2014

The Communication, Ocean and Meteorological Satellite(COMS) data processing system(CMDPS) has developed to support the meteorological observation and weather prediction by NMSC(National Meteorological Satellite Center) and it is generating the 16 kind of meteorological data(Level 2 product). Unfortunately, currently CMDPS has some problems in terms of the system maintenance and the integrated software efficiency, and the extension to support the next generation meteorological satellite data processing. To solve this problems, in this paper, we suggest the extensible component-based system architecture for COMS meteorological data processing with consideration of identified issues. Proposed system is adapted the component-based frameworks with extensible architecture. We expects that this system will be provide easy ways to develop new satellite data processing algorithms and to maintain the system.

• Atmosphere
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• v.30 no.2
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• pp.115-124
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• 2020

The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.724-736
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• 2019

Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines. However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes a new dynamic thermal rating technology based on global/regional assimilation and prediction system (GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves a lot of investment.

• Atmosphere
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• v.17 no.1
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• pp.1-15
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• 2007

A constraint based on climatology is introduced to the cost function of the three-dimensional variational assimilation (3dVar) to correct the error of the zonal mean wind structure in the global data assimilation system at Korea Meteorological Administration (KMA). The revised cost function compels the analysis fit to the chosen climatology while keeping the balance between the variables in the course of analysis. The constraint varies selectively with the vertical level and the horizontal scale of the motion. The zonally averaged wind field from European Centre for Medium-Range Weather Forecasts Re-Analysis 40 (ERA-40) is used as a climatology field in the constraint. The constraint controls only the zonally averaged stratospheric long waves with total wave number less than 20 to fix the error of the large scale wind field in the stratosphere. The constrained 3dVar successfully suppresses the erroneous westerly in the stratospheric analysis promptly, and has been applied on the operational global 3dVar system at KMA.

• Atmosphere
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• v.27 no.4
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• pp.411-422
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• 2017

The aim of this research is to review recent trends in weather and climate research in North Korea. We selected North Korean journal 'Weather and Hydrology' for the last 10 years (2007-2016), and identified trends in research subject, researchers, and affiliations. Furthermore, we analyzed the major achievements and trends by research sector. Our main results are same as follows. The largest number of researches on 'modernization and informatization on prediction' have been carried out in North Korea's recent meteorological and climatological research. This could be implicated that the scope of national science policy directly affected the promotion of specific research field. Especially, North Korea was evaluated to be concentrating its efforts on numerical model research and development. The numerical model which enables very short-term (6 hours) rainfall forecast which using ensemble Kalman filter data assimilation method (4D EnKF) was developed. In addition, development of automatic weather system and improvement of the data transfer system were promoted. However, the result reveals that the automated real-time data transfer system was not fully equipped yet. These results could be used as a basic data for meteorological cooperation between South and North Korea.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.188-197
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• 2022

We develop a coastal wave forecasting system by using the unstructured grid based on sea wind data of Global Data Assimilation and Prediction System. The verification is performed to examine the performance and accuracy of the wave model. Since the conventional grid has limited wave forecasting on complex coastlines and bathymetry, the unstructured grid system is applied for precise numerical simulation, and applicability for operational support is evaluated. Both grid systems show similar prediction trends in offshore and coastal areas, and the difference in prediction errors according to the grid system is not large. In addition, the applicability of the operational wave forecasting system is confirmed by dramatically reducing the model execution time of the unstructured grid under the same conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.28-34
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• 2007

Recently, with the rapid growth of industry, environmental condition became worse. In addition to outdoor insulators in seashore are polluted due to salty wind. Also this pollution causes the flashover and failure of electric equipments. Especially the salt contaminant is one of the most representative pollutants, and known as the main source of the accident by contamination. As well known, the pollution has a close relation with meteorological factors such as wind velocity, wind direction, temperature, relative humidity, precipitation and so on. In this paper we have statistically analyzed the correlation between the pollution and the meteorological factors. The multiple regression analysis was used for the statistical analysis; daily measured equivalent salt deposit density(dependent variable) and the weather condition data(independent variable) were used. Also we have developed an expert program to predict the pollution deposit. A new prediction system using this program called SPPP(salt pollution prediction program) has been used to model accurately the relationship between ESDD with the meteorological factors.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.65-65
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• 2005

• Advances in environmental research
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• v.7 no.2
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• pp.109-120
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• 2018

In the present study research air quality analyses for $PM_{10}$, were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The averages concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of $PM_{10}$ occurs generally in the night while the least concentration was found at the afternoon. Monthly concentrations of $PM_{10}$ showed highest value in August, while least value was found in January. The seasonal concentrations showed the least amounts in autumn while the highest amounts in summer. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions SPSS software. RMSE test showed that among different prediction models, stepwise model is the best option.