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The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements.

Mesoscale climate models provide indispensable tools to understand land‐atmosphere interactions over urban regions. However, uncertainties in urban canopy parameters (UCPs) and parameterization schemes lead to degraded representation of the drag effect in complex built terrains. In particular, for the widely applied single‐layer urban canopy model (SLUCM) coupled with the Weather Research and Forecasting (WRF) model, near‐surface horizontal wind speed is known to be overestimated systematically. In this study, idealized large eddy simulations (LES) and WRF‐SLUCM simulations are conducted to study the separate effect of UCPs and aerodynamic parameterization on atmospheric boundary layer processes and rainfall variabilities in Phoenix, Arizona. For LES that explicitly resolves surface geometry, significant differences between three‐dimensional (3D) versus two‐dimensional (2D) representation of urban morphology are found in the surface layer and above. When surface drag is parameterized following SLUCM, surface morphologies have little impacts on the mean momentum transfer. WRF‐SLUCM simulation results, incorporated with 3D urban morphology data, indicate that simply refining the frontal area index will reduce the surface drag, which further amplifies the systematic positive bias of SLUCM in predicting horizontal wind speed. Replacing the drag parameterization in SLUCM by LES‐based aerodynamic parameters has evident impacts on near‐surface wind speed. The impact of urban roughness representation becomes the most evident during rainfall periods, due to the important role of surface drag in dictating moisture convergence. Our study underlines that apart from intensive efforts in obtaining detailed UCPs, it is also critical to enhance the urban momentum exchange parameterization schemes.