It is well known that atmospheric environments, including both meteorology and air quality, significantly affect public health, such as chronic lung disease and cancer, and respiratory infections. In this study, we have analyzed correlations between the number of daily respiratory outpatients and the atmospheric environments data for about ten years for the city of Busan, South Korea. The respiratory problem patients data have been categorized into two health-vulnerable groups by age over 65(DayPA_O65) and under 20(DayPA_U20), each of which shows relatively higher correlations with air quality and meteorology, respectively. However, time series analysis with factor separation results in that DayPA_O65 and DayPA_U20 show a higher relation with variance components and daily irregular factors of atmospheric concentrations, respectively.

We investigate the amount of potential electricity energy generated by wind power in Busan metropolitan area, using the mesoscale meteorological model WRF (Weather Research & Forecasting), combined with small wind power generators. The WRF modeling has successfully simulated meteorological characteristics over the urban areas, and showed statistical significant to predict the amount of wind energy generation. The highest amount of wind power energy has been predicted at the coastal area, followed by at riverbank and upland, depending on predicted spatial distributions of wind speed. The electricity energy prediction method in this study is expected to be used for plans of wind farm constructions or the power supplies.

To estimate the benefit of high-resolution meteorological data for building energy estimation, a building energy analysis has been conducted over Busan metropolitan areas. The heating and cooling load has been calculated at seven observational sites by using temperature, wind and relative humidity data provided by WRF model combined with the inner building data produced by American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE). The building energy shows differences 2-3% in winter and 10-30% in summer depending on locations. This result implicates that high spatiotemporal resolution of meteorological model data is significantly important for building energy analysis.

EnergyPlus is a whole building energy simulation program that engineers, architects, and researchers use to model energy and water use in buildings. Modeling the performance of a building with EnergyPlus enables building professionals to optimize the building design to use less energy and water. This program provides energy analysis of building and needs weather data for simulation. Weather data is available for over 2,000 locations in a file format that can be read by EnergyPlus. However, only five locations are avaliable in Korea. This study intends to use AWS data for having high spatial resolution to simulate building energy. The result of this study shows the possibility of using AWS data for energy simulation of building.