Reducing the gap between predicted and measured urban microclimate through handling parameter uncertainty and model form uncertainty

Physics-based models, particularly urban canopy models (UCM), have been widely used to investigate urban microclimate conditions of various urban areas. UCMs have been developed to simulate the interaction between buildings, roads, and vegetation, including the surface energy balance, to predict urban climate. Despite its developments, deployment of UCM for city-scale urban simulations presents major challenges as follows: (1) UCM requires many uncertain input parameters, referred to as parameter uncertainty, due to the lack of data availability; (2) UCM is based on the concept of urban canyon that assumes homogeneous urban settings with one representative building geometry, and this assumption pertains to model-form uncertainty.

Few studies attempted to address the parameter uncertainty via calibration of uncertain input parameters. Other some studies have compared the prediction accuracy of multiple UCM types or integration of UCMs with different models. Yet, the effect of homogeneous urban canyon assumption on the model discrepancy have not been sufficiently explored and there is no knowledge base to identify key urban factors, unrepresented in UCMs, relating to model discrepancy.

To address the research gap, this work aims to improve the prediction accuracy of an existing UCM model for wind speed prediction by handling both parameter uncertainty and model-form uncertainty under various urban contexts. First, an existing UCM, specifically the vertical city weather generator (VCWG) model, was used to predict wind speed at 248 weather stations in Seoul, Republic of Korea.

Our previous study demonstrated that the root-mean-square error (RMSE) of VCWG wind speed model at 248 weather stations ranged between 0.14 and 20.14 m/s [1]. Second, we calibrated three important uncertain parameters selected on the basis of sensitivity analysis results, namely turbulence coefficient during unstable conditions, pressure gradient coefficient, and aerodynamic roughness length.

Third, to address model form uncertainty, we identified urban factors that are not represented in the VCWG model, such as urban heterogeneity, topography, and blue infrastructure. Fourth, we develop statistical model(s) associated with unrepresented urban factors to quantify the model-form uncertainty and, ultimately, reduce the gap between the model and reality. It is noteworthy that the 248 weather stations used were deployed in different vertical layers: urban canopy-level, roughness sublayer-level, and inertial sublayer-level, which allows to improve UCM prediction in different vertical heights.

This study will deliver a calibrated VCWG model that is able to accurately predict actual wind speed and provide a knowledge base regarding the possible causes of model discrepancies.