Warm climates pose challenges to building energy consumption and pedestrian comfort. Knowledge of the wind flow around buildings can help address these issues through improving natural ventilation, energy use, and outdoor thermal comfort. Computational fluid dynamics (CFD) simulations are widely used to predict wind flow around buildings, despite the large discrepancies that often occur between model predictions and actual measurements. Wind speed and direction exhibit a high degree of variability that adds uncertainties in modeling and measurements. Although some studies focus on methods to evaluate and minimize modeling uncertainties, sensor placement has been mostly based on subjective judgment and intuition; no systematic methodology is available to identify optimal sensor locations prior to field measurement. This work proposes a methodology for systematic sensor placement for situations when no measurement data are available and knowledge of the wind environment around buildings is limited. Sequential sensor placement algorithms and criteria are used to identify sensor configurations based on CFD simulation predictions at plausible locations. Optimal sensor configurations are compared for their ability to improve wind speed predictions at another location where no measurements are taken. The methodology is applied to two full-scale building systems of varying size. Results show that the methodology can be applied prior to field measurement to identify optimal configurations of a limited number of sensors that improve wind speed predictions at unmeasured locations. (C) 2015 American Society of Civil Engineers.