Modeling of urban heat island using adjusted static database

Abstract

In this study, the Weather Research and Forecasting (WRF) model was applied to examine the spatial and temporal formation of urban heat island (UHI) phenomenon in Szeged, Hungary. In order to achieve a more accurate representation of complex urban surface properties in WRF, a modified static database (consists of land use and urban canopy parameters) had been developed using satellite images and building information. In the new database, the number of urban grids increased by 76% related to the default case. The urban landscape in WRF has become more complex after employing two urban land use classes instead of only one. The modification of the default parameters of a single layer urban scheme (i.e., Single Layer Urban Canopy Model -SLUCM) revealed that urban fractions decreased in all urban categories, while street widths increased resulting in narrower urban canyons. For testing the impact of the modifications on near-surface temperature estimation, a four-day heatwave period was selected from 2015. The model outputs had been evaluated against the observations of the local urban climate monitoring system (UCMS). WRF with the modified parameters simulated most of the features of UHI reasonably well. In most cases, biases with the simulations of the adjusted static database tended to be significantly lower than with the default parameters. Additionally, we picked out a longer time period (i.e., the summer of 2015) when the extreme values of near-surface air temperature and maxima of UHI intensities were evaluated on the basis of an urban and a rural site of UCMS. It was concluded that the maxima and minima of observed nearsurface air temperature were underestimated (overestimated) by about 1-3 °C at the urban (rural) site. The maxima of UHI intensities indicated cold biases on 86 of 91 days.

City Information Modeling: State of the Art

“…In terms of data sources, a variety of urban static data can be used to mine other related data, which is significantly valuable. For example, Molnár et al [21] established a modified static database to mine urban heat island data using satellite images and building information.…”