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To calculate aerosol extinction from Raman lidar data, it is necessary to evaluate the derivative of a molecular Raman signal with respect to range. The typical approach taken in the lidar community is to make an a priori assumption about the functional behavior of the data to calculate the derivative. It has previously been shown that the use of the chi-squared technique to determine the most likely functional behavior of the data prior to actually calculating the derivative eliminates the need for making a priori assumptions. Here that technique is validated through numerical simulation and by application to a significant body of Raman lidar measurements. In general, we show that the chi-squared approach for evaluating extinction yields lower extinction uncertainty than traditional techniques. We also use the technique to study the feasibility of developing a general characterization of the extinction uncertainty that could permit the uncertainty in Raman lidar aerosol extinction measurements to be estimated accurately without the need of the chi-squared technique.

A Mie backscattering model for spherical particles with off-center inclusion has been developed and tested. The program is capable of dealing with size parameter values up to approximately 1000, thus allowing one to simulate the optical behavior of a large variety of atmospheric aerosols, as well as cloud and precipitation particles. On the basis of this model, we simulated the optical properties of polydisperse composite atmospheric particles as observed by ground-based and airborne lidar systems. We have characterized optical properties in terms of host and inclusion radii, considering water particles with different composition inclusions. The performed modeling provides some insight into the so-called lidar bright- and dark-band phenomenon.