Quantifying the cooling effects of community-driven heat mitigation initiatives with PALM: Sensitivity to model resolution and configuration

Urban heat challenges caused by global climate change and urban heat islands have driven the need for effective heat mitigation strategies. It is important to evaluate these strategies prior to implementation (e.g., through numerical modeling) to provide science-based guidance for policy decisions. These heat strategies usually occur at small (e.g., within a neighborhood) scales, which poses a challenge for physically based numerical modeling. This study uses the Parallelized Large-Eddy Simulation Model (PALM) to simulate neighborhood-scale urban climate interventions (short grass, street trees, and their combination) proposed by a local environmental justice community. Specifically, we simulate a heat wave day (July 24, 2022) with the initial and boundary conditions provided by the Weather Research and Forecasting (WRF) model. The analysis focuses on the sensitivity of model results to model resolution (both horizontal and vertical), as well as model configuration (e.g., the effects of using model nesting and spin-up). Results indicate that at a 1-meter above the ground (but still within the urban canyon), the combination of short grass and street trees can lower air temperatures by approximately 0.5°C, primarily by altering the surface energy budget. With nesting, atmospheric temperatures more closely reflect local surface conditions; however, neither nesting nor spin-up significantly affects cooling outcomes within the neighborhood. These findings underscore the importance of fine-scale modeling for evaluating urban heat mitigation strategies and advancing our understanding of how their effectiveness is affected by model resolutions and configurations.