The numerical and experimental techniques are widely used for urban air flow studies. As spatiotemporal scales increase, these models face an extended range of limitations, from the experimental setup sizing/equipment constraints to expensive physics-based computational modeling. Reduced order models (ROMs) are introduced as an alternative or a complementary tool to current practices for identifying urban airflow characteristics. This paper investigates the implementation of a group of ROMs on an opensource experimental dataset. These models follow two major steps dimensionality reduction and airflow feature computation. The results show a significant reduction in the computational cost with similar accuracy achieved when studying urban airflow characteristics to those from experimental testing. These models can be further used in urban climate analysis.