In order to solve the problem that atmospheric lidar detection is easily interfered by noise and the signal-to-noise ratio (SNR) of distant signals drops rapidly, according to the long sequence characteristics of the lidar detection, an improved variable weighted Kalman filter method for lidar detection is proposed. A constant term is added to the variable weighted coefficient in the algorithm. Therefore, the changing weighted coefficient can be provided for the long sequence measurements values at different time in the improved variable weighted Kalman filter algorithm. The correction effect of the new measurement is enhanced and the influence of the old measurement on the optimal estimation is reduced in this algorithm. The algorithm is verified by the actual atmospheric lidar measurements in different weathers. Compared with the other three Kalman filtering algorithms, under cloudy weather, the SNR of lidar detections are improved by nearly 4. 9, 3. 7 and 2. 5 dB, respectively. The inversion error of aerosol extinction coefficient is reduced by 57%, 26% and 4% respectively. In cloudless day, the signal to noise ratio of lidar echo signals are improved by nearly 5. 5, 4. 4 and 3. 4 dB respectively. The inversion error of aerosol extinction coefficient is reduced by 53%, 25% and 3% respectively. The inversion accuracy of atmospheric aerosol optical properties are improved using this algorithm. An effective method for fine detection of aerosol microphysical parameters and practical application of lidar is provided.