A Regional Aerosol Model for the Middle Urals Based on CALIPSO Measurements

The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinde...

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Bibliographic Details
Published in:Atmosphere
Main Authors: Ekaterina S. Nagovitsyna, Sergey K. Dzholumbetov, Alexander A. Karasev, Vassily A. Poddubny
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2023
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Online Access:https://doi.org/10.3390/atmos15010048
https://doaj.org/article/399f85c61f414ce589c565419b9aecc8
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Summary:The present work aims to develop a regional Middle Urals Aerosol model (MUrA model) based on the joint analysis of long-term ground-based photometric measurements of the Aerosol Robotic NETwork (AERONET) and the results of lidar measurements of the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite relying on information on the air trajectories at different altitudes calculated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) software package. The MUrA model contains parameters of normalized volume size distributions (NVSDs) characterizing the tropospheric aerosol subtypes detected by the CALIPSO satellite. When comparing the MUrA model with the global CALIPSO Aerosol Model (CAMel), we found significant differences in NVSDs for elevated smoke and clean continental aerosol types. NVSDs for dust and polluted continental/smoke aerosol types in the global and regional models differ much less. The total volumes of aerosol particles along the atmospheric column reconstructed from satellite measurements of the attenuation coefficient at a wavelength of 532 nm based on the regional MUrA model and global CAMel are compared with the AERONET inversion data. The mean bias error for the regional model is 0.016 μm 3 /μm 2 , and 0.043 μm 3 /μm 2 for the global model.