Analysis of lidar measurements using nonparametric kernel regression methods

The lidar technique is an efficient tool for remote monitoring of the distribution of a number of atmospheric species. We study measurements of sulphur dioxide emitted from the Italian volcano Mt. Etna. This study is focused on the treatment of data and on the procedure to evaluate range-resolved concentrations. In order to make an in-depth analysis, the lidar system was prepared to store measurements of individual backscattered laser pulses. Utilizing these repeated measurements a comparison of three different methods to average the returned signals is made. In the evaluation process we use local polynomial regression to estimate the range-resolved concentrations. Here we calculate optimal bandwidths based on the empirical-bias bandwidth selector. We also compare two different variance estimators for the path-integrated curves: local polynomial variance estimation and variance estimation based on Taylor approximations. Results show that the method performs well. An advantage compared to previous methods for evaluation of lidar measurements is that an estimate of the mean squared error of the estimated concentration can be calculated.