Analysis of Lidar Fields using Local Polynomial Regression
Torgny Lindström, Ulla Holst and Petter Weibring
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,
2004
ISSN 14039338

Abstract:

Lidar (LIght Detection And Ranging) is a laser based tool for remote measurement
of several atmospheric species of importance. We consider the analysis of
a field, consisting of several consecutive measurements, in which the
concentrations are proportional to the derivatives in the directions of the
light paths. Inference is based on local polynomial kernel regression, both
for estimation of the derivatives of the meanfunction and for estimation
of the variancefunction. Bivariate bandwidth matrices are selected using
the empiricalbias bandwidth selector (EBBS) adapted to allow for dependent
data and to support selection of bivariate bandwidths. The estimation procedure
is demonstrated on measurements of atomic mercury from the Solvay industries
mercury cell chloralkali plant in Rosignano Solvay, Italy.




Key words:

Air pollution; heteroscedastic observations; local bandwidth selection;
nonparametric; spatial dependence; variancefunction estimation.

