EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

Abstract

Abstract. In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EAR-LINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations.During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented.The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product.Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EAR-LINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time preprocessing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.

Statistical analysis of the long-range transport of the 2015 Calbuco volcanic plume from ground-based and space-borne observations

“…The Gobabeb site, in the Namib Desert, is far less sensitive to the effect of urban pollution than the Durban and Pretoria sites. It can be observed that all of these African sites exhibit an increase in the AOD values during the austral spring season, which is well known to be the biomass burning season (Eck et al, 2003;Garstang et al, 1996;Das et al, 2015;Piketh et al, 1999;Kumar et al, 2017). The daily AOD measurements performed between 15 April and 1 December 2015 were compared with background values in order to highlight the passage of the volcanic plume.…”

The unprecedented 2017–2018 stratospheric smoke event: decay phase and aerosol properties observed with the EARLINET

“…Another process leading to a cross adiabatic movement (by diabatic heating) is related to the so-called self-lifting effect (Boers et al, 2010;Siddaway and Petelina, 2011;de Laat et al, 2012). Absorption of shortwave solar radiation heats the smoke layers and creates buoyancy that can then result in an ascent of the aerosol layer over several kilometers altitude within 1-2 d (Siddaway and Petelina, 2011;de Laat et al, 2012). Such a heating is seasonally dependent.…”

“…They had a nonspherical shape after 7-10 d of travel towards Europe Hu et al, 2019) and even after months as will be shown here. Self-lifting effects caused by sunlight absorption and warming of the ambient air (Boers et al, 2010;Siddaway and Petelina, 2011;de Laat et al, 2012) then led to a further significant ascent of the soot-containing layers. The aerosol optical thickness (AOT) in the UTLS region must have exceeded values of 2-3 at 500 nm wavelength so that strong absorption in the visible spectrum and warming of the smoke layers occurred and enabled the fire smoke plumes to ascend by about 2-3 km d −1 during the first days after injection, as was observed with the spaceborne lidar aboard CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) (Khaykin et al, 2018).…”

An EARLINET Early Warning System for atmospheric aerosol aviation hazards

“…In particuar, low resolution (in both time and space) uncalibrated pre-processed products provided by the SCC EARLINET Lidar Pre-processor (ELPP) module (D'Amico et al, 2016) and aerosol optical properties vertical profiles provided by the SCC EARLINET Lidar data Analyzer (ELDA) module (Mattis et al, 2016) are made available. Recently a new version of the SCC has been released providing also standardized high-resolution pre-processed lidar products.…”

“…During the last years, EARLINET has increased strongly its observing capacity with the addition of new stations and system upgrade, namely, the installation of depolarization channels. Besides, the further development of the Single Calculus Chain (D'Amico et al, 2015(D'Amico et al, , 2016Mattis et al, 2016) under the ACTRIS (Aerosol, Clouds and Trace gases Research InfraStructure Network) umbrella eliminated the inconsistencies in the retrieval procedures and in the signal error calculation, and automated the data evaluation, and now allows for the NRT data processing and the generation of tailored products network-wide. EAR-LINET has already demonstrated the networks' NRT capabilities as well as assisted modelling studies in NRT evaluation and 2 https://doi.org/10.5194/acp-2020-178 Preprint.…”