The reliable measurement of pressures on low-rise buildings in the atmospheric boundary layer (ABL) flow remains a challenge, as has been shown by the large discrepancies among results obtained in different wind tunnel facilities or even in the same wind tunnel. Two major causes of the discrepancies are the difficulty of simulating large-scale, low-frequency turbulent fluctuations uniformly across laboratories and the small scale of models in typical civil engineering wind tunnels. To address these issues, it was proposed that a simplified flow be used in laboratory simulations, rather than a conventional ABL flow. In the simplified flow the reference mean wind speed is larger than the mean wind speed of the ABL flow, and the low-frequency fluctuations present in the ABL flow are suppressed; that is, the peak energy of the missing lowfrequency fluctuations is supplied in the simplified flow by the increment in the mean wind speed, which may be regarded as a flow fluctuation with zero frequency. High-frequency turbulent fluctuations, which typically affect flow reattachment, are approximately the same in the ABL and the simplified flow. Because, over small distances, low-frequency fluctuations are highly coherent spatially for small low-rise buildings with dimensions of up to approximately 20 m (e.g., single-family residential homes), the peak aerodynamic effects of the two flows may be hypothesized to be approximately the same. Preliminary experimental results obtained in University of Western Ontario's ABL wind tunnel facility and Florida International University's small-scale Wall of Wind facility are shown to support this hypothesis. The use of the proposed simplified flow is currently being tested by the authors for application to computational wind engineering (CWE) applications. Such use eliminates the need to simulate the lower frequency fluctuations of the boundary layer flow and thus makes it possible to achieve practical CWE calculations, and it is advantageous in experiments from the points of view of measurement accuracy, model scaling, repeatability of the simulations, and computational efficiency.