Differential absorption lidar (DIAL) has proved to be an important tool for remote sensing of trace gases in the atmosphere. As DIAL systems are affected by various noise factors such as atmospheric turbulence, target speckle, detection noise and so on, the measured concentration is corrupted by noise, and cannot be estimated accurately. However, when observations, predictions, estimations, and various covariance of Kalman filter algorithm are decomposed into lower resolution levels, due to filtering effects of wavelet transform, noise can be restrained while behavior of concentration is exposed. In this paper, a novel multiresolution Kalman filter algorithm is applied to estimate the path-integrated concentration (CL) from DIAL time series data where measurements are available at only one resolution level, and uses the stationary wavelet transform (SWT) as a means for mapping data between different resolution levels. The algorithm was evaluated for a variety of synthetic lidar data created with a program designed to model the various noise sources, including atmospheric turbulence, reflective speckle, and detection noise, which affect lidar signals. The simulation results show that our algorithm is effective in improving the measurement accuracy of gas concentration in DIAL and performs better than Kalman filtering and SWT visually and quantitatively.