Analysis of LIDAR Measurements using Nonparametric Kernel Regression Methods
Torgny Lindström, Ulla Holst, Petter Weibring and Hans Edner
Centre for Mathematical Sciences
Lund Institute of Technology,
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.
LIDAR, differential absorption, single shot measurements, local polynomial
regression, variance estimation, optimal local bandwidths.