Synergy processing of diverse ground-based remote sensing and in situ data using GRASP algorithm: applications to radiometer, lidar and radiosonde observations
Stenchikov, Georgiy L.
Wienhold, Frank G.
KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberURF/1/2180-01
Permanent link to this recordhttp://hdl.handle.net/10754/665880
MetadataShow full item record
AbstractAbstract. The exploration of aerosol retrieval synergies from diverse combinations of ground-based passive sun-photometric measurements with co-located active lidar ground-based and radiosonde observations using versatile 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 night time were inverted simultaneously under the assumption of temporal continuity of aerosol properties. Such 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 night-time 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 close-by sun-photometric observations provides sufficient constraints for reliable interpretation of both day- and night- time 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 set-ups used for processing both synergy and stand-alone observation data sets are discussed and inter-compared.
CitationLopatin, A., Dubovik, O., Fuertes, D., Stenchikov, G., Lapyonok, T., Veselovskii, I., … Parajuli, S. (2020). Synergy processing of diverse ground-based remote sensing and in situ data using GRASP algorithm: applications to radiometer, lidar and radiosonde observations. doi:10.5194/amt-2020-422
SponsorsThe research reported in this publication was partially supported by funding from King Abdullah University of Science and Technology (KAUST) through the Competitive Research Grant (URF/1/2180-01-01) “Combined Radiative and Air Quality Effects of Anthropogenic Air Pollution and Dust over the Arabian Peninsula”. We are also thankful to KAUST Core Lab and IT department for supporting the data collection and archiving. Authors also pay the tribute to exhausting and, sometimes, sleepless labor of the teams that make operation of field campaigns possible, notably the teams of AERONET, MPL, IRD (Institut de Recherche pour le Développement) in Dakar and LOA (Laboratoire d’Optique Atmosphérique) in Lille for SHADOW campaigns and Jean-Paul Vernier for the operation of radiosonde flights during KAUST campaigns. O. Dubovik and T. Lapyonok were supported by the Labex CaPPA project, which is funded by the French National Research Agency under the contract “ANR-11-LABX0005-01”.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/