Analysis of Lidar Measurements Using Nonparametric Kernel Regression Methods

The LIDAR (LIght Detection And Ranging) technique is an eecient tool for remote monitoring of the distribution of a number of atmospheric species. We study measurements of SO 2 emitted from the Italian vulcano Etna. In this study we focus on the treatment of data and on the procedure to evaluate range resolved concentrations. In order to make an in-depth analysis we prepared the lidar system to store measurements of individual backscattered laser pulses. Utilizing these repeated mesurements we compare three diierent methods to average the returned signals. 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 (EBBS). We also compare two diierent variance estimators for the path-integrated curves: local polynomial variance estimation and variance estimation based on Taylor approximations.