Aerosol extinction and backscatter Optimal Estimation retrieval for High Spectral Resolution Lidar

Abstract. High Spectral Resolution Lidars (HSRLs) have been successfully deployed from a variety of platforms: ground based, airborne, and now satellite. These lidars are uniquely valuable for characterizing atmospheric aerosol and clouds, benefiting from the ability to characterize vertical variability in more detail than any passive instruments, and, compared to elastic backscatter lidars, provide additional channels of measurements that permit the direct retrieval of particulate extinction. Although analytic solutions exist for deriving particulate backscatter, extinction, and linear depolarization ratio, in the case of extinction, the analytic technique greatly magnifies measurement noise. Low signal-to-noise measurements stress the traditional inversion methods. Accordingly, algorithms for the retrieval of HSRL backscatter and extinction are re-examined and optimized to reduce the noise propagation. Here we explore an Optimal Estimation methodology and compare it with an implementation of the direct differentiation method like that historically used for the processing of airborne HSRL data from NASA Langley Research Center.