Optimal Estimation Retrievals of Aerosol Microphysical Properties from High Spectral Resolution Lidar (HSRL) and Polarimeter Data

Abstract:

Knowledge of the vertical profile, composition, concentration, and size distribution of aerosols is required to quantify the impacts of aerosols on human health, global and regional climate, clouds and precipitation, and ocean ecosystems. We will describe an Optimal Estimation (OE) retrieval method that will use three wavelengths of aerosol backscattering (3β) and two wavelengths of aerosol extinction (2α). We will also describe how to use the OE framework to retrieve vertical profiles simultaneously using altitude resolved HSRL data. Finally, we will describe how to include additional measurements (e.g. polarimeter or Sun photometer) for improved aerosol microphysical property retrievals. In a traditional aerosol retrieval algorithm, one solves for aerosol size distributions under various parameter space (rmin, rmax, real and imaginary refractive index) using Tikhonov (or other) regularization and then selects physically and mathematically meaningful solutions from hundreds of thousand retrievals. In an attempt to speed up the retrieval and to provide retrieval error estimates, the OE method solves for all related aerosol microphysical parameters (e.g. number concentrations, particle size distribution, real and imaginary part of refractive indices) simultaneously in a maximum-likelihood sense by fitting the observed data. Other quantities such as effective particle radius, surface area concentration, volume concentration, and single scattering albedo are also derived from the retrieved size distribution and the number concentrations. We will show preliminary results using both simulated data and airborne measurements from HSRL-2. Coincident airborne in-situ and surface remote sensing datasets will be used to evaluate the performance of the new OE algorithm.