| Popis: |
Aerosol microphysical properties, including aerosol particle size distribution, complex refractive index and concentration properties, are key parameters evaluating the impact of aerosols on climate, meteorology, and human health. High Spectral Resolution Lidar (HSRL) is an efficient tool for probing the vertical optical properties of aerosol particles, including the aerosol backscatter coefficient (β) and extinction coefficient (α), at multiple wavelengths. To swiftly process vast data volumes, address the ill-posedness of retrieval problems, and suit simpler lidar systems, this study proposes an algorithm (modified algorithm) for retrieving microphysical property profiles from the HSRL optical data targeting fine-mode aerosols, building upon a previous algorithm (basic algorithm). The modified algorithm is based on a look-up table (LUT) approach, combined with the k-nearest neighbor (k-NN) and random forest (RF) algorithms, and it optimizes the decision tree generation strategy, incorporating a self-posed scheme. In numerical simulation tests for different lidar configurations, the modified algorithm reduced retrieval errors by 41%, 30%, and 32% compared to the basic algorithm for 3β + 2α, 3β + 1α, and 2β + 1α, respectively, with a remarkable improvement of stability. In two observation scenes of a field campaign, the median relative errors of the effective radius for 3β + 2α were 6% and −3%, and the median absolute errors of single-scattering albedo were 0.012 and 0.005. This method represents a further step toward the use of the LUT approach, with the potential to provide effective and efficient aerosol microphysical retrieval for simpler lidar systems, which could advance our understanding of aerosols’ climatic, meteorological, and health impacts. |
