| Issue |
EPJ Web Conf.
Volume 237, 2020
The 29th International Laser Radar Conference (ILRC 29)
|
|
|---|---|---|
| Article Number | 08023 | |
| Number of page(s) | 4 | |
| Section | Lidar Data Analysis and Models | |
| DOI | https://doi.org/10.1051/epjconf/202023708023 | |
| Published online | 07 July 2020 | |
https://doi.org/10.1051/epjconf/202023708023
Towards an Algorithm for Near Real Time Profiling of Aerosol Species, Trace Gases, and Clouds Based on the Synergy of Remote Sensing Instruments
1 Laboratory of Atmospheric optics, Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Greece
2 Aosta Valley Regional Environmental Protection Agency (ARPA), Saint-Christophe, Italy
* Email: nsiomos@physics.auth.gr
Published online: 7 July 2020
In this manuscript we present the concept of a novel algorithmic chain that aims to a dataset of unprecedented detail in the vertical distribution of multiple atmospheric components in near real time conditions. The analysis will be based on the following remote sensing instruments: a depolarization Raman lidar, a visible and a thermal all-sky camera, a Brewer spectrophotometer, and up to three mini DOAS/MAX-DOAS systems. Based on both individual and synergistic processing of the data collected, novel products will be made available in near real time conditions to the end users. Columnar aerosol information from the spectrophotometers will be combined with lidar data to retrieve vertical profiles of individual aerosol species. Cloud layers will be detected and classified based mainly on the synergy of the lidar and the sky cameras and a realistic 3D representation of cloud conditions around the measurement site will be produced. Lidar profiles will be implemented as a priori information for radiative transfer purposes, that are necessary in order to obtain high quality trace gases profiles from the DOAS/MAX-DOAS spectrophotometer. Fast synergistic data processing will ensure that the algorithm can be applied for near real time public data dissemination in the future.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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