Abstract:The Korean peninsula has complex and diverse weather phenomena, and the Korea Meteorological Administration has been working on various numerical models to produce better forecasting data. The Unified Model Local Data Assimilation and Prediction System is a limited-area working model with a horizontal resolution of 1.5 km for estimating local-scale weather forecasts on the Korean peninsula. However, in order to numerically predict the detailed temperature characteristics of the urban space, in which surface characteristics change rapidly in a small spatial area, a city temperature prediction model with higher resolution spatial decomposition capabilities is required. As an alternative to this, a building-scale temperature model was developed, and a 25 m air temperature resolution was determined for the Seoul area. The spatial information was processed using statistical methods, such as linear regression models and machine learning. By comparing the accuracy of the estimated air temperatures with observational data during the summer, the machine learning was improved. In addition, horizontal and vertical characteristics of the urban space were better represented, and the air temperature was better resolved spatially. Air temperature information can be used to manage the response to heat-waves and tropical nights in administrative districts of urban areas.
With the continuous increase in computing capabilities, large-eddy simulation (LES) has recently gained popularity in applications related to flow, turbulence, and dispersion in the urban atmospheric boundary layer (ABL). Herein, we perform high-resolution building-scale LES over the Seoul, South Korea, city area to investigate the impact of inflow turbulence on the resulting turbulent flow field in the urban ABL. To that end, LES using the cell perturbation method for inflow turbulence generation is compared to a case where no turbulence fluctuations in the incoming ABL are present (unperturbed case). Validation of the model results using wind speed and wind direction observations at 3 m above ground level reveals minimal differences irrespective of the presence of incoming ABL turbulence. This is due to the high density of building structures present at the surface level that create shear instabilities in the flow field and therefore induce local turbulence production. In the unperturbed case, turbulent fluctuations are found to slowly propagate in the vertical direction with increasing fetch from the inflow boundaries, creating an internal boundary layer that separates the turbulent region near the building structures and the nonturbulent flow aloft that occupies the rest of the ABL. Analysis of turbulence quantities including energy spectra, velocity correlations, and passive scalar fluxes reveals significant underpredictions that rapidly grow with increasing height within the ABL. These results demonstrate the need for realistic inflow turbulence in building-resolving LES modeling to ensure proper interactions within the ABL.
The effects of different averaging operators and atmospheric stability on the turbulent fluxes are investigated using the vertical velocity, air temperature, carbon dioxide concentration, and absolute humidity data measured at 10 Hz by a 3-dimensional sonic anemometer and an open-path CO 2 /H 2 O infrared gas analyzer installed at a height of 18.5 m on the rooftop of the Jungnang KT building located at a typical residential area in Seoul, Korea. For this purpose, 7 different averaging operators including block average, linear regression, and moving averages during 100 s, 300 s, 600 s, 900 s, and 1800 s are considered and the data quality control procedure such as physical limit check and spike removal is also applied. It is found that as the averaging interval becomes shorter, turbulent fluxes computed by the moving average become smaller and the ratios of turbulent fluxes computed by the 100 s moving average to the fluxes by the 1800 s moving average under unstable stability are smaller than those under neutral stability. The turbulent fluxes computed by the linear regression are 85~92% of those computed by the 1800 s moving average and nearly the same as those computed by 900 s moving average, implying that the adequate selection of an averaging operator and its interval will be very important to estimate more accurate turbulent fluxes at urban area.
It is needed to preserve good effects and to prevent bad influences on local climate in urban and environmental planning. This study seeks to develop climate analysis maps
This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas.
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