Beamforming techniques are widely used today in aeroacoustic wind tunnels to identify wind noise sources generated by interaction between incoming flow and the test object. In this study, a planar spiral microphone array with 120 channels was set out-of-flow at 1:1 aeroacoustic wind tunnel of Shanghai Automotive Wind Tunnel Center (SAWTC) to test exterior wind noise sources of a production car. Simultaneously, 2 reference microphones were set in vehicle interior to record potential sound source signal near the left side view mirror triangle and the signal of driver’s ear position synchronously. In addition, a spherical array with 48 channels was set inside the vehicle to identify interior noise sources synchronously as well. With different correlation methods and an advanced algorithm CLEAN-SC, the ranking of contributions of vehicle exterior wind noise sources to interested interior noise locations was accomplished. The results demonstrate that the advanced deconvolution algorithm CLEAN-SC has significant improvement against limitations of spatial resolution and dynamic range of conventional Beamforming technique. It has great potential for vehicle wind noise transmission path analysis and wind noise optimization work in the wind tunnel. In addition, Correlation analysis result of interior and exterior noise sources using virtual and real reference microphones was compared and discussed as well.