• Atmosphere
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• v.32 no.3
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• pp.179-189
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• 2022

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.

• Journal of the Korean earth science society
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• v.33 no.1
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• pp.39-48
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• 2012

In this study, a statistical estimation of probable precipitation and an analysis of its return period in Busan were performed using long-term precipitation data (1973-2007) collected from the Busan Regional Meteorological Administration. These analyses were based on the method of probability weighted moments for parameter estimation, the goodness-of-fit test of chi-square ($x^2$) and the probability plot correlation coefficient (PPCC), and the generalized logistics (GLO) for optimum probability distribution. Moreover, the spatial distributions with the determination of probable precipitation were also investigated using precipitation data observed at 15 Automatic Weather Stations (AWS) in the target area. The return periods for the probable precipitation of 245.2 and 280.6 mm/6 hr with GLO distributions in Busan were estimated to be about 100 and 200 years, respectively. In addition, the high probable precipitation for 1-, 3-, 6-, and 12-hour durations was mostly distributed around Dongrae-gu site, all coastal sites in Busan, Busanjin and Yangsan sites, and the southeastern coastal and Ungsang sites, respectively.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.257-268
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• 2015

The objective of this study is to assess the dual-polarization radar for flood forecasting. First, radar rainfall has temporal and spatial errors, so estimated radar rainfall was compared with ground observation rainfall to assess accuracy improvement, especially, considering the radar range of observation and increase of the rainfall intensity. The results of this study showed that the error for estimated dual-polarization radar rainfall was less than single-polarization radar rainfall. And in this study, dual-polarization radar rainfall for flood forecasting was assessed using MAP (Mean Areal Precipitation) and SURR (Sejong University Rainfall Runoff) model in Namkang dam watershed. The results of MAP are more accurate using dual-polarization radar. And the results of runoff using dual-polarization radar rainfall showed that peak flow error was reduced approximately 12~63%, runoff volumes error was reduced by approximately 30~42%, and also the root mean square error decreased compared to the result of runoff using single-polarization radar rainfall. The results revealed that dual-polarization radar will contribute to improving the accuracy of the flood forecasting.

• Journal of Environmental Science International
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• v.29 no.3
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• pp.257-272
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• 2020

This study investigates the temporal and spatial variations of marine meterological elements (air temperature (Temp), Sea Surface Temperature (SST), and Significant Wave Height (SWH)) in seven coastal waters of South Korea, using hourly data observed at marine meteorological buoys (10 sites), Automatic Weather System on lighthouse (lighthouse AWS) (9 sites), and AWS (20 sites) during 2013-2017. We also compared the characteristics of Temp, SST, and air-sea temperature difference (Temp-SST) between sea fog and non-sea-fog events. In general, annual mean values of Temp and SST in most of the coastal waters were highest (especially in the southern part of Jeju Island) in 2016, due to heat waves, and lowest (especially in the middle of the West Sea) in 2013 or 2014. The SWH did not vary significantly by year. Wind patterns varied according to coastal waters, but their yearly variations for each coastal water were similar. The maximum monthly/seasonal mean values of Temp and SST occurred in summer (especially in August), and the minimum values in winter (January for Temp and February for SST). Monthly/seasonal mean SWH was highest in winter (especially in December) and lowest in summer (June), while the monthly/seasonal variations in wind speed over most of the coastal waters (except for the southern part of Jeju Island) were similar to those of SWH. In addition, sea fog during spring and summer was likely to be in the form of advection fog, possibly because of the high Temp and low SST (especially clear SST cooling in the eastern part of South Sea in summer), while autumn sea fog varied between different coastal waters (either advection fog or steam fog). The SST (and Temp-SST) during sea fog events in all coastal waters was lower (and more variable) than during non-sea-fog events, and was up to -5.7℃ for SST (up to 5.8℃ for Temp-SST).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.963-969
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• 2021

The wind data measured from local meteorological masts is used to evaluate wind speed distribution and energy production in the specified site for wind farm However, wind data measured from meteorological masts often contain missing information or insufficient desired height or data length, making it difficult to perform wind turbine control and performance simulation. Therefore, long-term continuous wind data is very important to assess the annual energy production and the capacity factor for wind turbines or wind farms. In addition, if seasonal influences are distinct, such as on the Korean Peninsula, wind data with seasonal characteristics should be considered. This study presents methodologies for generating synthetic wind that take into account fluctuations in both wind speed and direction using the hidden Markov model, which is a statistical method. The wind data for statistical processing are measured at Maldo island in the Kokunnsan-gundo, Jeonbuk Province using the Automatic Weather System (AWS) of the Korea Meteorological Administration. The synthetic wind generated using the hidden Markov model will be validated by comparing statistical variables, wind energy density, seasonal mean speed, and prevailing wind direction with measurement data.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Journal of the Korean earth science society
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• v.34 no.7
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• pp.693-710
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• 2013

This study investigates the distribution of suspended particulates during the Asian dust period in Busan, Korea in the spring of 2009. Weather map and automatic weather system (AWS) data were used to analyze the synoptic weather conditions during the period. Particulate matter 10, laser particle counter data, satellite images and a backward trajectories model were used to analyze the aerosol particles distribution and their origins. In Case 1 (20 February 2009), when the $PM_{10}$ concentration increased, the aerosol volume distribution of small ($0.3-1.0{\mu}m$) particles decreased, while the concentration of large ($1.0-10.0{\mu}m$) particles increased. When the $PM_{10}$ concentration decreased, the aerosol volume distribution was observed to decrease as well. The prevailing winds changed from weak northerly winds to strong southwesterly winds when the concentration of the large particles increased. The correlation coefficient between the $PM_{10}$ concentration and aerosol volume distribution of large particles showed a high positive value of over 0.9. The results from the trajectory model show that the Asian dust originated in the Gobi desert and the Nei Mongol plateau. In Case 2 (25 April 2009), when the $PM_{10}$ concentration increased, the aerosol volume concentration of small ($0.3-0.5{\mu}m$) particles decreased, but the concentration of large ($0.5-10.0{\mu}m$) particles increased. The opposite was observed when the $PM_{10}$ concentration decreased. The prevailing winds changed from northeasterly winds to southwesterly and northeasterly winds. The correlation coefficient between the $PM_{10}$ concentration and aerosol volume distribution of large particles ($1.0-10.0{\mu}m$) showed a high positive value of about 0.9. The results from the trajectory model show that the Asian dust originated in Manchuria and the eastern coast of China.

• Journal of Environmental Science International
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• v.17 no.9
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• pp.1053-1068
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• 2008

This study aims to produce fundamental database for Environment Impact Assessment by monitoring vertical structure of the atmosphere due to the mountain valley wind in spring season. For this, we observed surface and upper meteorological elements in Sangin-dong, Daegu using the rawinsonde and automatic weather system(AWS). In Sangin-dong, the weather condition was largely affected by mountains when compared to city center. The air temperature was low during the night time and day break, and similar to that of city center during the day time. Relative humidity also showed similar trend; high during the night time and day break and similar to that of city center during the day time. Solar radiation was higher than the city, and the daily maximum temperature was observed later than the city. The synoptic wind during the measurement period was west wind. But during the day time, the west wind was joined by the prevailing wind to become stronger than the night time. During the night time and daybreak, the impact of mountain wind lowered the overall temperature, showing strong geographical influence. The vertical structure of the atmosphere in Dalbi valley, Sangin-dong had a sharp change in air temperature, relative humidity, potential temperature and equivalent potential temperature when measured at the upper part of the mixing layer height. The mixing depth was formed at maximum 1896m above the ground, and in the night time, the inversion layer was formed by radiational cooling and cold mountain wind.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.701-707
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• 2021

In this study, we examined the sea surface temperature (SST), air temperature (AT), and their time lag in response to an extreme cold wave in 2018 and a weak cold wave in 2019, cross-correlating these to the northern wind direction frequency. The data used in this study include SST observations of seven ocean buoys Real-time Information System for Aquaculture Environment provided by the National Institute of Fisheries Science and automatic weather station AT near them recorded every hour; null data was interpolated. A finite impulse response filter was used to identify the appropriate data period. In the extreme cold wave in 2018, the seven locations indicated low SST caused by moving cold air through the northern wind direction. A warm cold wave in 2019, the locations showed that the AT data was similar to the normal AT data, but the SST data did not change notably. During the extreme cold wave of 2018, data showed a high correlation coefficient of about 0.7 and a time lag of about 14 hours between AT and SST; during the weak cold wave of 2019, the correlation coefficient was 0.44-0.67 and time lag about 20 hours between AT and SST. This research will contribute to rapid response to such climate phenomena while minimizing aquaculture damage.