Earlinet single calculus chain: new products overview

The Single Calculus Chain (SCC) is an automatic and flexible tool to analyze raw lidar data using EARLINET quality assured retrieval algorithms. It has been already demonstrated the SCC can retrieve reliable aerosol backscatter and extinction coefficient profiles for different lidar systems. In this paper we provide an overview of new SCC products like particle linear depolarization ratio, cloud masking, aerosol layering allowing relevant improvements in the atmospheric aerosol characterization.Peer ReviewedPostprint (published version

EARLINET Single Calculus Chain – overview on methodology and strategy

In this paper we describe the EARLINET Single Calculus Chain (SCC), a tool for the automatic analysis of lidar measurements. The development of this tool started in the framework of EARLINET-ASOS (European Aerosol Research Lidar Network – Advanced Sustainable Observation System); it was extended within ACTRIS (Aerosol, Clouds and Trace gases Research InfraStructure Network), and it is continuing within ACTRIS-2. The main idea was to develop a data processing chain that allows all EARLINET stations to retrieve, in a fully automatic way, the aerosol backscatter and extinction profiles starting from the raw lidar data of the lidar systems they operate. The calculus subsystem of the SCC is composed of two modules: a pre-processor module which handles the raw lidar data and corrects them for instrumental effects and an optical processing module for the retrieval of aerosol optical products from the pre-processed data. All input parameters needed to perform the lidar analysis are stored in a database to keep track of all changes which may occur for any EARLINET lidar system over the time. The two calculus modules are coordinated and synchronized by an additional module (daemon) which makes the whole analysis process fully automatic. The end user can interact with the SCC via a user-friendly web interface. All SCC modules are developed using open-source and freely available software packages. The final products retrieved by the SCC fulfill all requirements of the EARLINET quality assurance programs on both instrumental and algorithm levels. Moreover, the manpower needed to provide aerosol optical products is greatly reduced and thus the near-real-time availability of lidar data is improved. The high-quality of the SCC products is proven by the good agreement between the SCC analysis, and the corresponding independent manual retrievals. Finally, the ability of the SCC to provide high-quality aerosol optical products is demonstrated for an EARLINET intense observation period

What is the benefit of ceilometers for aerosol remote sensing? An answer from EARLINET

With the establishment of ceilometer networks by national weather services, a discussion commenced to which extent these simple backscatter lidars can be used for aerosol research. Though primarily designed for the detection of clouds it was shown that at least observations of the vertical structure of the boundary layer might be possible. However, an assessment of the potential of ceilometers for the quantitative retrieval of aerosol properties is still missing. In this paper we discuss different retrieval methods to derive the aerosol backscatter coefficient beta(p),with special focus on the calibration of the ceilometers. Different options based on forward and backward integration methods are compared with respect to their accuracy and applicability. It is shown that advanced lidar systems such as those being operated in the framework of the European Aerosol Research Lidar Network (EARLINET) are excellent tools for the calibration, and thus beta(p) retrievals based on forward integration can readily be implemented and used for real-time applications. Furthermore, we discuss uncertainties introduced by incomplete overlap, the unknown lidar ratio, and water vapor absorption. The latter is relevant for the very large number of ceilometers operating in the spectral range around lambda = 905-910 nm. The accuracy of the retrieved beta(p) mainly depends on the accuracy of the calibration and the long-term stability of the ceilometer. Under favorable conditions, a relative error of beta(p) on the order of 10% seems feasible. In the case of water vapor absorption, corrections assuming a realistic water vapor distribution and laser spectrum are indispensable;otherwise errors on the order of 20% could occur. From case studies it is shown that ceilometers can be used for the reliable detection of elevated aerosol layers below 5 km, and can contribute to the validation of chemistry transport models, e. g.,the height of the boundary layer. However, the exploitation of ceilometer measurements is still in its infancy, so more studies are urgently needed to consolidate the present state of knowledge, which is based on a limited number of case studies

Development of a dynamic dust source map for NMME-DREAM v1.0 model based on MODIS Normalized Difference Vegetation Index (NDVI) over the Arabian Peninsula

We developed a time-dependent dust source map for the NMME Dust Regional Atmospheric Model (DREAM v1.0) based on the satellite MODIS Normalized Difference Vegetation Index (NDVI). Areas with NDVI&thinsp;&lt;0.1 are classified as active dust sources. The updated modeling system is tested for dust emission capabilities over SW Asia using a mesoscale model grid increment of 0.1∘×0.1∘ for a period of 1 year (2016). Our results indicate significant deviations in simulated aerosol optical depths (AODs) compared to the static dust source approach and general increase in dust loads over the selected domain. Comparison with MODIS AOD indicates a more realistic spatial distribution of dust in the dynamic source simulations compared to the static dust sources approach. The modeled AOD bias is improved from −0.140 to 0.083 for the case of dust events (i.e., for AOD&thinsp;&gt;0.25) and from −0.933 to −0.424 for dust episodes with AOD&thinsp;&gt;1. This new development can be easily applied to other time periods, models, and different areas worldwide for a local fine tuning of the parameterization and assessment of its performance.</p

Comparison and complementary use of in situ and remote sensing aerosol measurements in the Athens Metropolitan Area

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.In the summer of 2014 in situ and remote sensing instruments were deployed in Athens, in order to study the concentration, physical properties, and chemical composition of aerosols. In this manuscript we aim to combine the measurements of collocated in situ and remote sensing instruments by comparison and complementary use, in order to increase the accuracy of predictions concerning climate change and human health. We also develop a new method in order to select days when a direct comparison on in situ and remote sensing instruments is possible. On selected days that displayed significant turbulence up to approximately 1000 m above ground level (agl), we acquired the aerosol extinction or scattering coefficient by in situ instruments using three approaches. In the first approach the aerosol extinction coefficient was acquired by adding a Nephelometer scattering coefficient in ambient conditions and an Aethalometer absorption coefficient. The correlation between the in situ and remote sensing instruments was good (coefficient of determination R2 equal to 0.69). In the second approach we acquired the aerosol refractive index by fitting dry Nephelometer and Aethalometer measurements with Mie algorithm calculations of the scattering and absorption coefficients for the size distribution up to a maximum diameter of 1000 nm obtained by in situ instruments. The correlation in this case was relatively good (R2 equal to 0.56). Our next step was to compare the extinction coefficient acquired by remote sensing instruments to the scattering coefficient calculated by Mie algorithm using the size distribution up to a maximum diameter of 1000 nm and the equivalent refractive index (ERICOR), which is acquired by the comparison of the size distributions obtained by a Scanning Mobility Particle Sizer (SMPS) and an Optical Particle Counter (OPC). The agreement between the in situ and remote sensing instruments in this case was not satisfactory (R2 equal to 0.35). The last comparison for the selected days was between the aerosol extinction Ångström exponent acquired by in situ and remote sensing instruments. The correlation was not satisfactory (R2 equal to 0.4), probably due to differences in the number size distributions present in the air volumes measured by in situ and remote sensing instruments. We also present a day that a Saharan dust event occurred in Athens in order to demonstrate the information we obtain through the synergy of in situ and remote sensing instruments on how regional aerosol is added to local aerosol, especially during pollution events due to long range transport.Peer reviewe

LIVAS: A 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET

We present LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud optical database, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The LIVAS database provides averaged profiles of aerosol optical properties for the potential spaceborne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global database is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent backscatter- and extinction-related Ångström exponents, derived from EARLINET (European Aerosol Research Lidar Network) ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversions are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO backscatter and extinction data corresponding to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud optical database based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for spaceborne lidar performance assessments. The final global data set includes 4-year (1 January 2008–31 December 2011) time-averaged CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) data on a uniform grid of 1° × 1° with the original high vertical resolution of CALIPSO in order to ensure realistic simulations of the atmospheric variability in lidar end-to-end simulations

Aerosol absorption profiling from the synergy of lidar and sun-photometry : The ACTRIS-2 campaigns in Germany, Greece and Cyprus

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).Aerosol absorption profiling is crucial for radiative transfer calculations and climate modelling. Here, we utilize the synergy of lidar with sun-photometer measurements to derive the absorption coefficient and single scattering albedo profiles during the ACTRIS-2 campaigns held in Germany, Greece and Cyprus. The remote sensing techniques are compared with in situ measurements in order to harmonize and validate the different methodologies and reduce the absorption profiling uncertainties.Peer reviewe

A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals

Systematic measurements of dust concentration profiles at a continental scale were recently made possible by the development of synergistic retrieval algorithms using combined lidar and sun photometer data and the establishment of robust remote-sensing networks in the framework of Aerosols, Clouds, and Trace gases Research InfraStructure Network (ACTRIS)/European Aerosol Research Lidar Network (EARLINET). We present a methodology for using these capabilities as a tool for examining the performance of dust transport models. The methodology includes considerations for the selection of a suitable data set and appropriate metrics for the exploration of the results. The approach is demonstrated for four regional dust transport models (BSC-DREAM8b v2, NMMB/BSC-DUST, DREAMABOL, DREAM8-NMME-MACC) using dust observations performed at 10 ACTRIS/EARLINET stations. The observations, which include coincident multi-wavelength lidar and sun photometer measurements, were processed with the Lidar-Radiometer Inversion Code (LIRIC) to retrieve aerosol concentration profiles. The methodology proposed here shows advantages when compared to traditional evaluation techniques that utilize separately the available measurements such as separating the contribution of dust from other aerosol types on the lidar profiles and avoiding model assumptions related to the conversion of concentration fields to aerosol extinction values. When compared to LIRIC retrievals, the simulated dust vertical structures were found to be in good agreement for all models with correlation values between 0.5 and 0.7 in the 1–6 km range, where most dust is typically observed. The absolute dust concentration was typically underestimated with mean bias values of -40 to -20 μg m−3 at 2 km, the altitude of maximum mean concentration. The reported differences among the models found in this comparison indicate the benefit of the systematic use of the proposed approach in future dust model evaluation studies

GARRLiC and LIRIC: strengths and limitations for the characterization of dust and marine particles along with their mixtures

The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marine-dominated and dust–marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles. The marine-dominated and the mixture cases are more challenging for both algorithms, although GARRLiC manages to provide more detailed microphysical retrievals compared to AERONET, while LIRIC effectively discriminates dust and marine particles in its concentration profile retrievals. The results are also compared with modelled dust and marine concentration profiles and surface in situ measurements.The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme ACTRIS-2 (grant agreement no. 654109). The work has been developed under the auspices of the ESA-ESTEC project “Characterization of Aerosol mixtures of Dust And Marine origin” contract no. IPL-PSO/FF/lf/14.489. The work was also supported by the European Research Council under the European Community’s Horizon 2020 research and innovation framework programme/ERC grant agreement 725698 (D-TECT). The publication was supported by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no. 602014, project ECARS (East European Centre for Atmospheric Remote Sensing). The authors acknowledge support through the Stavros Niarchos Foundation. BSC-DREAM8b simulations were performed on the Mare Nostrum supercomputer hosted by the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC).Peer ReviewedPostprint (published version