• Journal of Korean Society of Steel Construction
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• v.9 no.3 s.32
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• pp.431-440
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• 1997

The paper describes a theoretical model for the prediction of bridge temperatures from meteorological data measured at bridge site and local meteorological center together with existing finite element heat transfer theory and solar radiation transfer theory to determine the time dependent temperature distribution of bridge. In this analytical model, the most adequate equation for the calculation of solar radiation on the bridge surface, which is dominant in day time is described based on the results of several experimental studies for the solar energy. The validity of this model is tested against field data obtained from long term experimental program on Sadang Viaduct in Seoul. Also, this paper describes the linear correlation between design variables and meteorological data to establish analytical criteria for the prediction of the average temperature, which are responsible for the longitudinal deformation of the bridges and of the vertical differential temperature profiles. which are responsible for the bending deformations from the long term experimental results.

• Fashion & Textile Research Journal
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• v.19 no.4
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• pp.434-447
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• 2017

This study investigated the effects of influencing factors on the sales volume of apparel products. Based on previous studies, weekend effect, discount rate, and meteorological factors including daily average temperature, rainfall, sea level pressure, and fine dust were selected as independent variables to calculate their effects on sales quantity of apparel products. The daily sales data during 2015 - 2016 were collected from casual brands and outdoor brands which "A" apparel manufacturing company had operated. The actual data of "A" company were analyzed using SAS(R) 9.4 and SAS(R) Enterprise Miner 14.1. The results of this study were as follows: First, the influencing factors on total sales volume of apparel products were proved to be the weekend effect, discount rate, and fine dust. Second, the analysis of influencing factors on sales volume of apparel products according to season showed: 1) In casual brands, the average temperature had a significant influence on the sales volume of spring/summer products, and the sea level pressure affected the sales volume of summer/fall/winter products significantly. 2) In outdoor brands, the average temperature and the fine dust had a significant influence on the sales volume of all season's products. The sea level pressure affected the sales volume of summer/fall/ winter products significantly. The weekend effect and the discount effect affected the sales volume of apparel products partly. Third, the effect of rainfall was not proven significant, which was different from the results of past studies.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.587-607
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• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.11-18
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• 2016

The local wind systems in the Pohang region were categorized into wind sectors. Still, thorough knowledge of wind resource assessment, wind environment analysis, and atmospheric environmental impact assessment was required since the region has outstanding wind resources, it is located on the path of typhoon, and it has large-scale atmospheric pollution sources. To overcome the resolution limitation of meteorological dataset and problems of categorization criteria of the preceding studies, the high-resolution wind resource map of the Korea Institute of Energy Research was used as time-series meteorological data; the 2-step method of determining the clustering coefficient through hierarchical clustering analysis and subsequently categorizing the wind sectors through non-hierarchical K-means clustering analysis was adopted. The similarity of normalized time-series wind vector was proposed as the Euclidean distance. The meteor-statistical characteristics of the mean vector wind distribution and meteorological variables of each wind sector were compared. The comparison confirmed significant differences among wind sectors according to the terrain elevation, mean wind speed, Weibull shape parameter, etc.

• Atmosphere
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• v.31 no.2
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.605-622
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• 2021

Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.305-305
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• 2022

A better approach for assessing meteorological drought occurrences is increasingly important in mitigating and adapting to the impacts of climate change, as well as strategies for developing early warning systems. The present study defines meteorological droughts as a period with an abnormal precipitation deficit based on monthly precipitation data of 18 gauging stations for the Han River watershed in the past (1974-2015). This study utilizes a Bayesian parameter estimation approach to analyze the effects of climate change on future drought (2025-2065) in the Han River Basin using the Coupled Model Intercomparison Project Phase 6 (CMIP6) with four bias-corrected general circulation models (GCMs) under the Shared Socioeconomic Pathway (SSP)2-4.5 scenario. Given that drought is defined by several dependent variables, the evaluation of this phenomenon should be based on multivariate analysis. Two main characteristics of drought (severity and duration) were extracted from precipitation anomalies in the past and near-future periods using the copula function. Three parameters of the Archimedean family copulas, Frank, Clayton, and Gumbel copula, were selected to fit with drought severity and duration. The results reveal that the lower parts and middle of the Han River basin have faced severe drought conditions in the near future. Also, the bivariate analysis using copula showed that, according to both indicators, the study area would experience droughts with greater severity and duration in the future as compared with the historical period.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.