Evaluation of canopy-layer air and mean radiant temperature simulations by a microclimate model over a tropical residential neighbourhood. (1st February 2017) - Roth, Matthias; Lim, Vanessa Huimin
Abstract: The performance of a 3D urban microclimate model (ENVI-met Version 3.1) is evaluated with data collected during mostly clear and calm conditions in a compact low-rise residential neighbourhood of tropical Singapore. Observations are obtained from seven canopy-layer air temperature, T a, sensors at 2 m above ground, including a fully equipped microclimate station measuring mean radiant temperature, MRT, at 1.1 m above ground. The model is capable of capturing the spatial variability across all stations during most of the eight simulation days. Spatially-averaged T a predictions are closer to the observations during wet (based on five simulation days) compared to dry (three days) periods. Daytime model performance for MRT is variable but peak values are well predicted. Systematic errors dominate most simulations. The present model evaluation metrics are smaller than reported in similar work, which is likely due to the more accurate determination of model input variables using locally measured soil relative humidity and leaf area density profiles. A modification to how the model calculates MRT also helps to improve its daytime performance. Finally, the model is used to predict the effect of five temperature mitigation/planning strategies. The varying results highlight the micro- and bioclimatic complexities inherent in a heterogeneous urban system, with no one scenario providing consistent cooling throughout both day- and nighttime. Overall the present results suggest that ENVI-met is a useful planning tool for assessing T a and daytime extremes in outdoor thermal comfort, but the model requires detailed local information for proper initialization and awareness of its limitations. Highlights: ENVI-met is evaluated over a residential neighbourhood in a tropical city. Canopy-layer temperature predictions are better compared to other, similar studies. Locally measured initialization parameters improve model performance. Model performance is better during wet compared to dry conditions. Model is capable of simulating maximum daytime MRT magnitudes.