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.
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
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.
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.
In the numerical weather model, surface properties can be defined by various parameters such as terrain height, landuse, surface albedo, soil moisture, surface emissivity, roughness length and so on. And these parameters need to be improved in the Seoul metropolitan area that established high-rise and complex buildings by urbanization at a recent time. The surface roughness length map is developed from digital elevation model (DEM) and it is implemented to the high-resolution numerical weather (WISE-WRF) model. Simulated results from WISE-WRF model are analyzed the relationship between meteorological variables to changes in the surface roughness length. Friction speed and wind speed are improved with various surface roughness in urban, these variables affected to temperature and relative humidity and hence the surface roughness length will affect to the precipitation and Planetary Boundary Layer (PBL) height. When surface variables by the WISE-WRF model are validated with Automatic Weather System (AWS) observations, NEW experiment is able to simulate more accurate than ORG experiment in temperature and wind speed. Especially, wind speed is overestimated over 2.5 m s −1 on some AWS stations in Seoul and surrounding area but it improved with positive correlation and Root Mean Square Error (RMSE) below 2.5 m s −1 in whole area. There are close relationship between surface roughness length and wind speed, and the change of surface variables lead to the change of location and duration of precipitation. As a result, the accuracy of WISE-WRF model is improved with the new surface roughness length retrieved from DEM, and its surface roughness length is important role in the high-resolution WISE-WRF model. By the way, the result in this study need various validation from retrieved the surface roughness length to numerical weather model simulations with observation data.
ABSTRACT:The thermal environment in urban areas is substantially influenced by local surface properties and their modification through human activities. The effects of urban climate on human health are one of the motivations for the development of various urban climate analysis tools. Climate Analysis Seoul (CAS) is a tool that incorporates a meso-scale atmospheric model and conceptual geographic information system (GIS)-based models to provide gridded air temperature deviations based on high-resolution land cover information. The CAS output was evaluated using air temperatures observed at 18 automatic weather stations (AWS) located in the detail region (DR) Eunpyeong. Correlation analyses were performed to reveal the influence of different land cover characteristics and CAS output variables on measured air temperature deviations. Based on the results of the correlation analyses in the study region (SR) Seoul, a regression model (total air temperature distribution, TD ′ ) was developed. It predicts spatially distributed air temperatures based on morphological parameters and an observed reference temperature. Using the TD ′ model, maps of daily maximum air temperatures were produced for the entire area of Seoul with a horizontal resolution of 25 m, and of 5 m for the DR, the latter allows to assess the impacts of different building and vegetation structures on air temperature by resolving buildings. The TD ′ model was evaluated using measurements at eight AWS. The TD ′ model slightly overestimated daily maximum air temperatures in 2007 and 2011, but performed better for 2011, mainly because land cover data represent the final state of the urban development. The TD ′ model appears to be a suitable tool for estimating air temperature distributions at building-resolving resolutions. It can be used to assess changes in the thermal environment and heat-related hazards through urban development plans already ahead of their realization.KEY WORDS urban climate analysis; urban planning; urban heat island; urban multi-scale temperature distribution
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