Investigation of a 1000 MW smoke plume by means of a 1.064 μm lidar—I. Lidar calibration procedure from in situ aerosol measurements and vertical lidar shots

Sulfur in particles in Arctic hazes derived from airborne in situ and lidar measurements

“…Quantitative application of lidar to aerosol studies depends on establishing relationships between the backscattering coefficient (15) measured by a lidar and the particle extinction coefficient, number concentration or mass. The backscattering coefficient due to particles (13p, in units of m-1 sr-1) for a given wavelength of light is described by [Collis and Russell, 1976 Ezcurra et al, 1985] have related 15p()•) to the number concentration of aerosol particles. However, derivation of particle number concentrations from 15p()•)is highly dependent on the shape of the particle size distribution and on the refractive index of the particles [Kent, 1978].…”

Evaluation of smoke dispersion from forest fire plumes using lidar experiments and modelling

“…Accordingly, the discrepancy between theory and experiment resulting from the theoretical part-inaccuracy of the numeric model (mostly, inherent limitations in description of turbulence and approximation of temperature/velocity equilibrium between the particulate and gas phases), errors in the experimental values of wind direction and velocity used in the calculations, and failure to take into account their fluctuations during the lidar measurements-is aggravated by significant uncertainties of the lidar signal inversion discussed earlier. Even expensive large-scale experiments involving lidar calibration with the help of direct airborne particle measurements yield relative errors of about 50% or worse (see, for example, [53]). Thus the fact that the predicted particle concentration distributions are of the same order as the experimental ones, testifies that the numerical model may be used for the semiqualitative assessment of forest-fire smoke disper- sion and, in particular, for simulation of training patterns for artificial-intelligence systems of smoke signatures in lidar data sets.…”