Abstract: Among the basic steps of low-altitude wind shear detection for airborne weather radar, the wind speed estimate accuracy is the most important affecting factors. In this paper, a novel method of wind speed estimation based on compressive sensing is proposed to solve the problem of performance degradation in low signal-to-noise ratio and few pulses. According to the sparsity of radar echoes, the Doppler vector is used to design a redundant dictionary for the sparse representation of the signal. The signal compression processing is achieved by using the observation matrix, and then the reconstruction algorithm is used to recover the sparse signal and acquire the accurate estimate of the wind speed. Experimental results show that the proposed method can achieve the accurate wind speed estimation and improve the spectral resolution in low signal-to-noise ratio and few pulses, which means it can identify the wind shear and clutter spectrum even in the adjacent areas.