Search Results
EARLINET Single Calculus Chain – overview on methodology and strategy
2015, D'Amico, Giuseppe, Amodeo, A., Baars, H., Binietoglou, I., Freudenthaler, V., Mattis, I., Wandinger, U., Pappalardo, G.
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.
Organic aerosol concentration and composition over Europe: Insights from comparison of regional model predictions with aerosol mass spectrometer factor analysis
2014, Fountoukis, C., Megaritis, A.G., Skyllakou, K., Charalampidis, P.E., Pilinis, C., van der Gon, H.A.C. Denier, Crippa, M., Canonaco, F., Mohr, C., Prévôt, A.S.H., Allan, J.D., Poulain, L., Petäjä, T., Tiitta, P., Carbone, S., Kiendler-Scharr, A., Nemitz, E., O'Dowd, C., Swietlicki, E., Pandis, S.N.
A detailed three-dimensional regional chemical transport model (Particulate Matter Comprehensive Air Quality Model with Extensions, PMCAMx) was applied over Europe, focusing on the formation and chemical transformation of organic matter. Three periods representative of different seasons were simulated, corresponding to intensive field campaigns. An extensive set of AMS measurements was used to evaluate the model and, using factor-analysis results, gain more insight into the sources and transformations of organic aerosol (OA). Overall, the agreement between predictions and measurements for OA concentration is encouraging, with the model reproducing two-thirds of the data (daily average mass concentrations) within a factor of 2. Oxygenated OA (OOA) is predicted to contribute 93% to total OA during May, 87% during winter and 96% during autumn, with the rest consisting of fresh primary OA (POA). Predicted OOA concentrations compare well with the observed OOA values for all periods, with an average fractional error of 0.53 and a bias equal to −0.07 (mean error = 0.9 μg m−3, mean bias = −0.2 μg m−3). The model systematically underpredicts fresh POA at most sites during late spring and autumn (mean bias up to −0.8 μg m−3). Based on results from a source apportionment algorithm running in parallel with PMCAMx, most of the POA originates from biomass burning (fires and residential wood combustion), and therefore biomass burning OA is most likely underestimated in the emission inventory. The sensitivity of POA predictions to the corresponding emissions' volatility distribution is discussed. The model performs well at all sites when the Positive Matrix Factorization (PMF)-estimated low-volatility OOA is compared against the OA with saturation concentrations of the OA surrogate species C* ≤ 0.1 μg m−3 and semivolatile OOA against the OA with C* > 0.1 μg m−3.
Detection of convective initiation using Meteosat SEVIRI: Implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM
2013, Merk, D., Zinner, T.
In this paper a new detection scheme for convective initiation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting CI with geostationary satellite data. It uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared (IR) criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one high-resolution visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims to identify the typical development of quickly developing convective cells in an early stage. The different criteria include time trends of the 10.8 IR channel, and IR channel differences, as well as their time trends. To provide the trend fields an optical-flow-based method is used: the pyramidal matching algorithm, which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM, and is verified for seven days which comprise different weather situations in central Europe. Contrasted with the original early-stage detection scheme of Cb-TRAM, skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for the synoptic class "cold air" masses.
Adaption of the MODIS aerosol retrieval algorithm using airborne spectral surface reflectance measurements over urban areas: A case study
2015, Jäkel, E., Mey, B., Levy, R., Gu, X., Yu, T., Li, Z., Althausen, D., Heese, B., Wendisch, M.
MODIS (MOderate-resolution Imaging Spectroradiometer) retrievals of aerosol optical depth (AOD) are biased over urban areas, primarily because the reflectance characteristics of urban surfaces are different than that assumed by the retrieval algorithm. Specifically, the operational "dark-target" retrieval is tuned towards vegetated (dark) surfaces and assumes a spectral relationship to estimate the surface reflectance in blue and red wavelengths. From airborne measurements of surface reflectance over the city of Zhongshan, China, were collected that could replace the assumptions within the MODIS retrieval algorithm. The subsequent impact was tested upon two versions of the operational algorithm, Collections 5 and 6 (C5 and C6). AOD retrieval results of the operational and modified algorithms were compared for a specific case study over Zhongshan to show minor differences between them all. However, the Zhongshan-based spectral surface relationship was applied to a much larger urban sample, specifically to the MODIS data taken over Beijing between 2010 and 2014. These results were compared directly to ground-based AERONET (AErosol RObotic NETwork) measurements of AOD. A significant reduction of the differences between the AOD retrieved by the modified algorithms and AERONET was found, whereby the mean difference decreased from 0.27±0.14 for the operational C5 and 0.19±0.12 for the operational C6 to 0.10±0.15 and -0.02±0.17 by using the modified C5 and C6 retrievals. Since the modified algorithms assume a higher contribution by the surface to the total measured reflectance from MODIS, consequently the overestimation of AOD by the operational methods is reduced. Furthermore, the sensitivity of the MODIS AOD retrieval with respect to different surface types was investigated. Radiative transfer simulations were performed to model reflectances at top of atmosphere for predefined aerosol properties. The reflectance data were used as input for the retrieval methods. It was shown that the operational MODIS AOD retrieval over land reproduces the AOD reference input of 0.85 for dark surface types (retrieved AOD = 0.87 (C5)). An overestimation of AOD = 0.99 is found for urban surfaces, whereas the modified C5 algorithm shows a good performance with a retrieved value of AOD = 0.86.
EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data
2016, D'Amico, Giuseppe, Amodeo, Aldo, Mattis, Ina, Freudenthaler, Volker, Pappalardo, Gelsomina
In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EARLINET 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 EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.
A new method to determine the mixing state of light absorbing carbonaceous using the measured aerosol optical properties and number size distributions
2012, Ma, N., Zhao, C.S., Müller, T., Cheng, Y.F., Liu, P.F., Deng, Z.Z., Xu, W.Y., Ran, L., Nekat, B., van Pinxteren, D., Gnauk, T., Müller, K., Herrmann, H., Yan, P., Zhou, X.J., Wiedensohler, A.
In this paper, the mixing state of light absorbing carbonaceous (LAC) was investigated with a two-parameter aerosol optical model and in situ aerosol measurements at a regional site in the North China Plain (NCP). A closure study between the hemispheric backscattering fraction (HBF) measured by an integrating nephelometer and that calculated with a modified Mie model was conducted. A new method was proposed to retrieve the ratio of the externally mixed LAC mass to the total mass of LAC (rext-LAC) based on the assumption that the ambient aerosol particles were externally mixed and consisted of a pure LAC material and a core-shell morphology in which the core is LAC and the shell is a less absorbing material. A Monte Carlo simulation was applied to estimate the overall influences of input parameters of the algorithm to the retrieved rext-LAC. The diurnal variation of rext-LAC was analyzed and the PartMC-MOSAIC model was used to simulate the variation of the aerosol mixing state. Results show that, for internally mixed particles, the assumption of core-shell mixture is more appropriate than that of homogenous mixture which has been widely used in aerosol optical calculations. A significant diurnal pattern of the retrieved rext-LAC was found, with high values during the daytime and low values at night. The consistency between the retrieved rext-LAC and the model results indicates that the diurnal variation of LAC mixing state is mainly caused by the diurnal evolution of the mixing layer.
The "dual-spot" Aethalometer: An improved measurement of aerosol black carbon with real-time loading compensation
2015, Drinovec, L., Močnik, G., Zotter, P., Prévôt, A.S.H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., Hansen, A.D.A.
Aerosol black carbon is a unique primary tracer for combustion emissions. It affects the optical properties of the atmosphere and is recognized as the second most important anthropogenic forcing agent for climate change. It is the primary tracer for adverse health effects caused by air pollution. For the accurate determination of mass equivalent black carbon concentrations in the air and for source apportionment of the concentrations, optical measurements by filter-based absorption photometers must take into account the "filter loading effect". We present a new real-time loading effect compensation algorithm based on a two parallel spot measurement of optical absorption. This algorithm has been incorporated into the new Aethalometer model AE33. Intercomparison studies show excellent reproducibility of the AE33 measurements and very good agreement with post-processed data obtained using earlier Aethalometer models and other filter-based absorption photometers. The real-time loading effect compensation algorithm provides the high-quality data necessary for real-time source apportionment and for determination of the temporal variation of the compensation parameter k.
Potential source regions and processes of aerosol in the summer Arctic
2015, Heintzenberg, J., Leck, C., Tunved, P.
Sub-micrometer particle size distributions measured during four summer cruises of the Swedish icebreaker Oden 1991, 1996, 2001, and 2008 were combined with dimethyl sulfide gas data, back trajectories, and daily maps of pack ice cover in order to investigate source areas and aerosol formation processes of the boundary layer aerosol in the central Arctic. With a clustering algorithm, potential aerosol source areas were explored. Clustering of particle size distributions together with back trajectories delineated five potential source regions and three different aerosol types that covered most of the Arctic Basin: marine, newly formed and aged particles over the pack ice. Most of the pack ice area with < 15% of open water under the trajectories exhibited the aged aerosol type with only one major mode around 40 nm. For newly formed particles to occur, two conditions had to be fulfilled over the pack ice: the air had spent 10 days while traveling over ever more contiguous ice and had traveled over less than 30% open water during the last 5 days. Additionally, the air had experienced more open water (at least twice as much as in the cases of aged aerosol) during the last 4 days before arrival in heavy ice conditions at Oden. Thus we hypothesize that these two conditions were essential factors for the formation of ultrafine particles over the central Arctic pack ice. In a comparison the Oden data with summer size distribution data from Alert, Nunavut, and Mt. Zeppelin, Spitsbergen, we confirmed the Oden findings with respect to particle sources over the central Arctic. Future more frequent broken-ice or open water patches in summer will spur biological activity in surface water promoting the formation of biological particles. Thereby low clouds and fogs and subsequently the surface energy balance and ice melt may be affected.
Assessment of lidar depolarization uncertainty by means of a polarimetric lidar simulator
2016, Bravo-Aranda, Juan Antonio, Belegante, Livio, Freudenthaler, Volker, Alados-Arboledas, Lucas, Nicolae, Doina, Granados-Muñoz, María José, Guerrero-Rascado, Juan Luis, Amodeo, Aldo, D'Amico, Giusseppe, Engelmann, Ronny, Pappalardo, Gelsomina, Kokkalis, Panos, Mamouri, Rodanthy, Papayannis, Alex, Navas-Guzmán, Francisco, Olmo, Francisco José, Wandinger, Ulla, Amato, Francesco, Haeffelin, Martial
Lidar depolarization measurements distinguish between spherical and non-spherical aerosol particles based on the change of the polarization state between the emitted and received signal. The particle shape information in combination with other aerosol optical properties allows the characterization of different aerosol types and the retrieval of aerosol particle microphysical properties. Regarding the microphysical inversions, the lidar depolarization technique is becoming a key method since particle shape information can be used by algorithms based on spheres and spheroids, optimizing the retrieval procedure. Thus, the identification of the depolarization error sources and the quantification of their effects are crucial. This work presents a new tool to assess the systematic error of the volume linear depolarization ratio (δ), combining the Stokes–Müller formalism and the complete sampling of the error space using the lidar model presented in Freudenthaler (2016a). This tool is applied to a synthetic lidar system and to several EARLINET lidars with depolarization capabilities at 355 or 532 nm. The lidar systems show relative errors of δ larger than 100 % for δ values around molecular linear depolarization ratios (∼ 0.004 and up to ∼ 10 % for δ = 0.45). However, one system shows only relative errors of 25 and 0.22 % for δ = 0.004 and δ = 0.45, respectively, and gives an example of how a proper identification and reduction of the main error sources can drastically reduce the systematic errors of δ. In this regard, we provide some indications of how to reduce the systematic errors.
Lidar-Radiometer Inversion Code (LIRIC) for the retrieval of vertical aerosol properties from combined lidar/radiometer data: Development and distribution in EARLINET
2016, Chaikovsky, Anatoli, Dubovik, Oleg, Holben, Brent, Bril, Andrey, Goloub, Philippe, Tanré, Didier, Pappalardo, Gelsomina, Wandinger, Ulla, Chaikovskaya, Ludmila, Denisov, Sergey, Grudo, Jan, Lopatin, Anton, Karol, Yana, Lapyonok, Tatsiana, Amiridis, Vassilis, Ansmann, Albert, Apituley, Arnoud, Allados-Arboledas, Lucas, Binietoglou, Ioannis, Boselli, Antonella, D'Amico, Giuseppe, Freudenthaler, Volker, Giles, David, Granados-Muñoz, María José, Kokkalis, Panayotis, Nicolae, Doina, Oshchepkov, Sergey, Papayannis, Alex, Perrone, Maria Rita, Pietruczuk, Alexander, Rocadenbosch, Francesc, Sicard, Michaël, Slutsker, Ilya, Talianu, Camelia, De Tomasi, Ferdinando, Tsekeri, Alexandra, Wagner, Janet, Wang, Xuan
This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.