Simulating Intra-day Extreme Temperature Variation and Efficiency of Cooling Strategy with WRF and satellite data-a case of Dhaka

Intensification of the urban heat island (UHI) effect due to rapid urbanization poses significant health risks to urban residents during heatwaves. Therefore, quantitative assessment of cooling strategies has thus become essential. This study employed Weather Research and Forecasting (WRF) model to simulate air temperatures directly related to human perception. To improve simulation accuracy, remote sensing data were integrated into the WRF framework to dynamically update land cover and urban canopy parameters. Subsequently, hourly meteorological data were utilized to validate the simulation accuracy. Based on the optimal simulation case LCZBH, we evaluated six cooling strategies about green roofs and cool roofs. The cooling effectiveness was analyzed during different time periods and across various Local Climate Zones (LCZs). The results showed that (1) replacing MODIS with LCZ data and incorporating localized building height data reduced the simulation errors of air temperature in WRF; (2) Temperature differences across LCZ types in the baseline were mainly observed in the afternoon and nighttime, with relatively higher temperatures in LCZ2, LCZ4, and LCZ10, and lower temperatures in LCZ9; (3) Increasing roof reflectivity was more effective for urban cooling than enhancing green roof coverage; (4) For the same cooling strategy, higher air temperatures corresponded to greater cooling potential. Specifically, cooling potential was higher during daytime than nighttime, and greater in high-temperature zones like LCZ2 and LCZ10 compared to low-temperature zones (e.g., LCZ9). The accuracy of WRF-simulated temperatures can be improved by integrating remote sensing data. The findings contributed to a better understanding of the spatial and temporal variations in urban air temperatures and cooling potential and supported the selection and implementation of cooling measures by governments effectively.