Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations

Abstract

The exploration of aerosol retrieval synergies from diverse combinations of ground-based passive Sun-photometric measurements with collocated active lidar ground-based and radiosonde observations using versatile Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm is presented. Several potentially fruitful aspects of observation synergy were considered. First, a set of passive and active ground-based observations collected during both day- and nighttime was inverted simultaneously under the assumption of temporal continuity of aerosol properties. Such an approach explores the complementarity of the information in different observations and results in a robust and consistent processing of all observations. For example, the interpretation of the nighttime active observations usually suffers from the lack of information about aerosol particles sizes, shapes and complex refractive index. In the realized synergy retrievals, the information propagating from the nearby Sun-photometric observations provides sufficient constraints for reliable interpretation of both day- and nighttime lidar observations. Second, the synergetic processing of such complementary observations with enhanced information content allows for optimizing the aerosol model used in the retrieval. Specifically, the external mixture of several aerosol components with predetermined sizes, shapes and composition has been identified as an efficient approach for achieving reliable retrieval of aerosol properties in several situations. This approach allows for achieving consistent and accurate aerosol retrievals from processing stand-alone advanced lidar observations with reduced information content about aerosol columnar properties. Third, the potential of synergy processing of the ground-based Sun-photometric and lidar observations, with the in situ backscatter sonde measurements was explored using the data from KAUST.15 and KAUST.16 field campaigns held at King Abdullah University of Science and Technology (KAUST) in the August of 2015 and 2016. The inclusion of radiosonde data has been demonstrated to provide significant additional constraints to validate and improve the accuracy and scope of aerosol profiling. The results of all retrieval setups used for processing both synergy and stand-alone observation data sets are discussed and intercompared.