Algorithm to retrieve aerosol optical properties using lidar measurements on board the EarthCARE satellite

Abstract

Abstract. Algorithms were developed to produce ATLID (Atmospheric Lidar) L2 aerosol products using ATLID L1 data. The algorithms estimated the following four products: (1) Layer identifiers such as aerosols, clouds, clear-skies, or surfaces (feature masks) were estimated by the combined use of vertically variable criteria and spatial continuity methods developed for the CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) analysis. (2) Aerosol optical properties such as extinction coefficient, backscatter coefficient, depolarization ratio, and lidar ratio at 355 nm were estimated by our developed optimization method using the Gauss-Newton method combined with the line search method developed for ground-based measurements. (3) Six aerosol types, namely smoke, pollution, marine, pristine, dusty-mixture, and dust were identified based on a two-dimensional diagram of the lidar ratio and depolarization ratio at 355 nm developed by cluster-analysis using the AERONET (AErosol RObotic NETwork) dataset with ground-based lidar data. (4) The planetary boundary layer height was determined using the improved wavelet covariance transform method for the ATLID analysis. We evaluated the algorithm’s performance using simulated ATLID L1 data generated by Joint-Simulator (Joint Simulator for Satellite Sensors), incorporating aerosol and cloud distributions from numerical models. Results from applying the algorithms to the simulated ATLID L1 data with realistic signal noise added for aerosol or cloud predominant cases revealed: (1) misidentification of aerosol and cloud layers by the feature mask algorithm was relatively low, approximately 10 %; (2) the retrieval errors of aerosol optical properties were 0.08 × 10-7 ± 1.12 × 10-7 m-1sr-1 (2 ± 34 % in relative error) for backscatter coefficient and 0.01 ± 0.07 (4 ± 27 %) for depolarization ratio; (3) aerosol type classification was generally performed well, with a 37 % of misclassification for dust. These results indicate that the algorithm’s capability to provide valuable insights into the global distribution of aerosols and clouds, facilitating assessments of their climate impact through atmospheric radiation processes.