• Journal of the Korean earth science society
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• v.42 no.2
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• pp.135-148
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• 2021

The purpose of this study was to evaluate the urban heat island (UHI) intensity and the corresponding surface temperature forecast obtained using the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration (KMA) against the AWS observation. The observed UHI intensity in Seoul increases during spring and winter, while it decreases during summer. It is found that the diurnal variability of the UHI intensity peaks at dawn but reaches a minimum in the afternoon. The LDAPS overestimates the UHI intensity in summer but underestimates it in winter. In particular, the model tends to overestimate the UHI intensity during the daytime in summer but underestimate it during the nighttime in winter. Moreover, surface temperature errors decrease in summer but increase in winter. The underestimation of the winter UHI intensity appears to be associated with weak forecasting of urban temperature in winter. However, the overestimated summer UHI intensity results from the underestimation of the suburban temperature forecast in summer. In order to improve the predictability of the UHI intensity, an urban canopy model (MORUSES) that considers urban effects was combined with LDAPS and used for simulation for the summer of 2017. The surface temperature forecast for the city was improved significantly by adopting MORUSES, and there were remarkable improvements in urban surface temperature morning forecasts. The urban canopy model produced an improvement effect that weakened the intensity of the UHI, which showed an overestimation during summer.

• Journal of Korea Water Resources Association
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• v.43 no.5
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• pp.471-482
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• 2010

Rainfall events in the hydrologic circulation are closely related with various meteorological factors. Therefore, in this research, correlation relationship was analyzed between sea surface temperature of typical meteorological factor and monthly rainfall on Korean peninsula. The cluster analysis was performed monthly average rainfall data, longitude and latitude observed by rainfall observatory in Korea. Results from cluster analysis using monthly rainfall data in South Korea were divided into 4 regions. The principal components of monthly rainfall data were extracted from rainfall stations separated cluster regions. A correlation analysis was performed with extracted principal components and sea surface temperatures. At the results of correlation analysis, positive correlation coefficients were larger than negative correlation coefficients. In addition, The 3 month of principal components on monthly rainfall predicted by locally weighted polynomial regression using observed data of sea surface temperature where biggest correlation coefficients have. The result of forecasting through the locally weighted polynomial regression was revealed differences in accuracy. But, this methods in the research can be analyzed for forecasting about monthly rainfall data. Therefore, continuous research need through hydrological meteorological factors like a sea surface temperature about forecasting of the rainfall events.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.6
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• pp.745-756
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• 2003

An analysis of wind environments using computational fluid dynamics and an evaluation of wind resources using measurement data obtained from meteorological observation sites at Homi-Cape, Pohang have been carrid out for siting a wind farm. It was shown that a numerical simulation using computational fluid dynamics would provide reliable wind resource map in complex terrain with land-sea breeze condition. As a result of this investigation, Homi-Cape wind farm with 11.25 ㎿ capacity has been designed for maximum power generation and 25.7 GWh electricity production is predicted.

• Journal of Korean Society for Atmospheric Environment
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• v.5 no.2
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• pp.97-105
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• 1989

Recently, many studies on air quality prediction models have been performed to develope new ones. The purpose of the study is to obtain a method to predict $SO_2$ concentration simply in urban area using hour-to-hour meteorological data such as the wind speed, the incoming solar radiation, and the cloud coverages. The relationships between with speed and $SO_2$ concentrations are plotted in flgures. Predicted concentration curves are obtained for equation C=b/(1+au).

• Journal of the Korean Solar Energy Society
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• v.38 no.1
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• pp.1-11
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• 2018

In this work, we propose weather prediction models to estimate hourly outdoor temperatures and solar irradiance in the next day using forecasting information. Hourly weather data predicted by the proposed models are useful for setting system operating strategies for the next day. The outside temperature prediction model considers 3-hourly temperatures forecasted by Korea Meteorological Administration. Hourly data are obtained by a simple interpolation scheme. The solar irradiance prediction is achieved by constructing a dataset with the observed cloudiness and correspondent solar irradiance during the last two weeks and then by matching the forecasted cloud factor for the next day with the solar irradiance values in the dataset. To verify the usefulness of the weather prediction models in predicting a short-term building load, the predicted data are inputted to a TRNSYS building model, and results are compared with a reference case. Results show that the test case can meet the acceptance error level defined by the ASHRAE guideline showing 8.8% in CVRMSE in spite of some inaccurate predictions for hourly weather data.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.249-256
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• 2011

Causing by green house gas emission, global warming is being accelerated significantly. This global warming cause world climate to change quiet different than before and we call this phenomenon is Climate Change. Environmental Impact Assessment being implemented in Korea is to prevent predicted environmental impacts from deteriorating within the domestic information and situation. As the climate change is getting severe, new meteorological records can be occurred which is exceeded existing statistical data. According to KMA(Korea Meteorological Administration) data, maximum value of precipitation and temperature in many regions changed with new data within last decade. And these events accompanied with landslides and flooding, and these also affected on water quality in rivers and lakes. According to impacts by climate change, disasters and accidents from heavy rain are the most apprehensive parts. And water pollution caused by overflowed non-point sources during heavy rain fall, fugitive dust caused by long-term drought, and sea level rise and Tsunami may affect on seaside industrial complex should be worth consideration. In this review, necessity of mutual consideration with influences of climate change was considered adding on existing guideline.

• Journal of the Korean earth science society
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• v.26 no.7
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• pp.706-713
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• 2005

Subsurface oceanic data (Z20; Depth of $20^{\circ}C$ isotherm and WWV; Warm Water Volume) from the tropical Pacific Ocean from 1980 to 2004 were utilized to examine upper ocean variations in relation to E1 Nino. Time series analysis using EOF, composite, and cross-correlation methods indicated that there are significant time delays between subsurface oceanic parameters and the Nino3.4 SST. It implied that Z20 and WWV would be more reliable predictors of El Nino events. Based on analyzed results, we also constructed neural network model to predict the Nino3.4 SST from 1996 to 2004. The forecasting skills for the model using WWV were statistically higher than that using the trade wind except for short range forecasting less than 3 months. This model greatly predicted SST than any other previous statistical model, especially at lead times of 5 to 8 months.

• Journal of Environmental Impact Assessment
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• v.31 no.5
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• pp.271-285
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• 2022

The AERMET, the AERMOD meteorological preprocessing program, mainly used for environmental impact assessment and Integrated Environmental Permit System (IEPS) in Korea, has not considered the land covers characterasitics, and used only the past meteorological data format CD-144. In this study, two results of AERMET application considering CD-144 format and ISHD format, being used internationally, were compared. Also, the atmospheric dispersion characteristics were analyzed with consideration of land cover. In the case of considered the CD-144 format, the actual wind speed was not taken into account in the weak wind (0.6~0.9m/s) and other wind speed due to the unit conversion problem. The predicted concentration considering land cover data was up to 387% larger depending on the topographic and emission conditions than without consideration of land cover. In conclusion, when using meteorological preprocessing program in AERMOD modelling, AERMET, with ISHD format, land cover characterasitics in the area should be considered.

• Journal of Korea Water Resources Association
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• v.37 no.4
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• pp.315-328
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• 2004

Recent increase of green house gases may increase the frequency of meteorological extremes. In this study, using the index and meteorological data generated by the Markov chain model under the condition of GCM predictions, the possible width of variability of flood and drought occurrences were predicted. As results, we could find that the frequency of both floods and droughts would be increased to make the water resources planning and management more difficult. Thus, it is recommended to include the effect of climate change on water resources in the related policy making.

• Journal of Korean Society for Atmospheric Environment
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• v.17 no.4
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• pp.331-345
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• 2001

The increase of carbon monoxide in the ambient air due to the emissions from point sources without control was estimated using three -dimensional numerical models. The target area was Ulsan where one of the largest industrial complexes was located. As a typical example using numerical models for air quality impact analysis of criteria pollutants that will determine whether the air quality standards would be exceeded or not, the following approaches were suggested. They include: (1) investigation of pre-existing atmospheric conditions, (2) identification of major factors causing high concentrations, (3) selection of episode days. (4) preparation of three-dimensional meteorological data, (5) confirmation of agreement between measured and predicted concentrations in the emission conditions of episode days, and (6) estimation of the impact due to changes of the emission conditions. In the present work, daily meteorological conditions for the specific period were classified into four clusters of distinctive features, and the episode days were selected individually from each cluster. Emphasis was placed on the selection of episodes representing meteorological conditions conducive to high concentrations especially for point sources that were sensitive to the wind direction variations.