A novel method to improve temperature forecast in data-scarce urban environments with application to the Urban Heat Island in Beirut

Abstract

The urban heat island (UHI) effect has been the subject of much research due to its adverse effects on health, energy, and the environment. The objective of this work is to investigate the key parameters contributing to the urban heat island effect in Beirut city and to improve the temperature forecast in the city by taking these parameters into account. This is accomplished by coupling the single – layer urban canopy model (UCM) with the Weather Research and Forecasting model (WRF), where the urban parameters in UCM are calculated or fine – tuned to minimize the difference between the measured and the forecasted temperature. Urban parameters that were tuned included the roof albedo, wall albedo, roof thermal conductivity, in addition to the anthropogenic heat. To get optimum values for these parameters, simulations with different values for each parameter were performed during the four seasons and compared with observations from stations that are distributed across the city using a novel method that has not been used before to the best of our knowledge. Comparison is based on the mean, standard deviation, mean bias, root mean square error, and correlation coefficient. We also present an assessment of the UHI in Beirut based on some metrics of urban complexity.