Monte Carlo method for the analysis of laser safety for a high-powered lidar system under different atmospheric conditions

A major concern of high-powered atmospheric lidar systems is eye safety. Atmospheric lidars are often run unattended in adverse weather conditions where scattering redirects laser energy from the main beam. These naturally varying “soft targets” (such as fog and precipitation) are not accounted for in American National Standards Institute (ANSI) standards but, through multiple scattering events, can potentially create adverse viewing conditions. This paper introduces a Monte Carlo method that uses scattering phase functions for fog and snow and applies multiple scattering analysis to map the energy density within a scattering volume around the primary beam. Careful attention is given to accurately describing the forward scattering portion of the phase function as it scatters a significant amount of the beam energy. This method is compared to ANSI standard hazard zone calculations to determine what effect scattering has on the size of the hazard zone. For direct beam viewing, hazard zone size estimates are within about 3% of the ANSI defined Nominal Ocular Hazard Distance (NOHD) for clear air but are approximately 56% smaller than the NOHD as optical density increases for scattering in fog and approximately 33% smaller for scattering in blowing snow. For indirect beam exposure, clear air gives the worst approximation to the ANSI defined Nominal Hazard Zone (NHZ), in error by approximately 93%; fog approaches the ANSI results, within 30% error, whereas blowing snow shows 70% error. Finally, scattering enhancement mechanisms are considered which relate to the definition of the scattering layer of interest and increase scattered energy density observed by approximately 4%. In all cases, the ANSI calculated NOHD and NHZ are larger than the hazard zones that include scattering but the size of the zones is inextricably linked to the type of scattering ignored in the standard NOHD and NHZ calculations.