Retrievals of vertically resolved aerosol microphysical particle parameters with regularization from spaceborne Aerosol and Carbon dioxide Detection Lidar (ACDL)

Abstract. Using an improved regularization method, we attempt to derive microphysical parameters (effective radius reff, surface area concentration St, volume concentration Vt) of aerosol particle size distribution directly from the detection results of Aerosol and Carbon dioxide Detection Lidar (ACDL), which is the first spaceborne high spectral resolution lidar (HSRL) based iodine filter. The backscatter and extinction coefficients at 532, 1064, and 1572 nm are adopted for regularization algorithm. Preliminary simulations for different aerosol types with monomodal and bimodal lognormal size distributions demonstrate the algorithm performance of the 3α + 3β optical data combination. For monomodal aerosols, the retrieval errors are constrained within 15 % for reff, 30 % for St, and 35 % for Vt. In bimodal cases, errors increase to 18 %–35 % for reff, 35 % for St, and up to 60 % for Vt. Sensitivity analysis confirms that systematic errors of ±20 % in input optical data induce parameter uncertainties below 60 %. Case studies reveal four typical aerosols profiles: urban (reff ∼ 0.47 µm), smoke (reff ∼ 0.57 µm), dust (reff ∼ 0.61 µm), and marine (reff ∼ 0.83 µm). The inversion reff is compared with CALIPSO and LIVAS, which confirms high consistency for marine and dust, while urban and smoke retrievals show slightly larger. The inclusion of 1572 nm significantly enhances coarse-mode retrieval accuracy. The error statistics of the simulations and the actual comparison results show that the proposed inversion algorithm can reliably derive the particle size distribution parameters from the spaceborne multi-wavelength lidar ACDL. This work provides preliminary validation of ACDL's capability to retrieve vertically resolved global aerosol microphysical characterization.