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dc.contributor.authorSiomos, Nikolaos
dc.contributor.authorBalis, Dimitris S.
dc.contributor.authorPoupkou, Anastasia
dc.contributor.authorLiora, Natalia
dc.contributor.authorSpyridon, Dimopoulos
dc.contributor.authorMelas, Dimitris
dc.contributor.authorGiannakaki, Eleni
dc.contributor.authorFilioglou, Maria
dc.contributor.authorBasart, Sara
dc.contributor.authorChaikovsky, Anatoli
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-07-24T14:10:34Z
dc.date.available2017-07-24T14:10:34Z
dc.date.issued2017-06-14
dc.identifier.citationSiomos, N. [et al.]. Investigating the quality of modeled aerosol profiles based on combined lidar and sunphotometer data. "Atmospheric Chemistry and Physics", 14 Juny 2017, vol. 17, p. 7003-7023.
dc.identifier.issn1680-7316
dc.identifier.urihttp://hdl.handle.net/2117/106763
dc.description.abstractIn this study we present an evaluation of the Comprehensive Air Quality Model with extensions (CAMx) for Thessaloniki using radiometric and lidar data. The aerosol mass concentration profiles of CAMx are compared against the PM2.5 and PM2. 5−10 concentration profiles retrieved by the Lidar-Radiometer Inversion Code (LIRIC). The CAMx model and the LIRIC algorithm results were compared in terms of mean mass concentration profiles, center of mass and integrated mass concentration in the boundary layer and the free troposphere. The mean mass concentration comparison resulted in profiles within the same order of magnitude and similar vertical structure for the PM2. 5 particles. The mean centers of mass values are also close, with a mean bias of 0.57 km. On the opposite side, there are larger differences for the PM2. 5−10 mode, both in the boundary layer and in the free troposphere. In order to grasp the reasons behind the discrepancies, we investigate the effect of aerosol sources that are not properly included in the model's emission inventory and in the boundary conditions such as the wildfires and the desert dust component. The identification of the cases that are affected by wildfires is performed using wind backward trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model in conjunction with satellite fire pixel data from MODerate-resolution Imaging Spectroradiometer (MODIS) Terra and Aqua global monthly fire location product MCD14ML. By removing those cases the correlation coefficient improves from 0.69 to 0.87 for the PM2. 5 integrated mass in the boundary layer and from 0.72 to 0.89 in the free troposphere. The PM2.5 center of mass fractional bias also decreases to 0.38 km. Concerning the analysis of the desert dust component, the simulations from the Dust Regional Atmospheric Model (BSC-DREAM8b) were deployed. When only the Saharan dust cases are taken into account, BSC-DREAM8b generally outperforms CAMx when compared with LIRIC, achieving a correlation of 0.91 and a mean bias of −29.1 % for the integrated mass in the free troposphere and a correlation of 0.57 for the center of mass. CAMx, on the other hand, underestimates the integrated mass in the free troposphere. Consequently, the accuracy of CAMx is limited concerning the transported Saharan dust cases. We conclude that the performance of CAMx appears to be best for the PM2.5 particles, both in the boundary layer and in the free troposphere. Sources of particles not properly taken into account by the model are confirmed to negatively affect its performance, especially for the PM2. 5−10 particles.
dc.description.sponsorshipThe authors would like to acknowledge the EU projects MACC-III (Monitoring Atmospheric Composition and Climate – III, grant agreement no. 633080) and MACC-II project (Monitoring Atmospheric Composition and Climate – Interim Implementation, grant agreement no. 283576). The simulated results presented in this research paper have been produced using the EGI and HellasGrid infrastructures. The ACTRIS-2 project from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654109 is gratefully acknowledged. The authors would also like to acknowledge the support provided by the Scientific Computing Center at Aristotle University of Thessaloniki throughout the progress of the work on air quality forecasting. BSC-DREAM8b simulations were performed on the Mare Nostrum supercomputer hosted by Barcelona Supercomputing Center-Centro Nacional de Supercomputacion (BSC-CNS). S. Basart wants to acknowledge the CICYT project (CGL2013-46736). Elina Giannakaki acknowledges the support of the Academy of Finland (project no. 270108).
dc.format.extent21 p.
dc.language.isoeng
dc.publisherEuropean Geosciences Union (EGU)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible
dc.subject.lcshAerosols--Measurement
dc.subject.lcshAir quality
dc.subject.lcshTroposphere--Measurement
dc.subject.otherComprehensive Air Quality Model
dc.subject.otherAerosol mass concentration
dc.subject.otherLidar-Radiometer Inversion Code (LIRIC)
dc.titleInvestigating the quality of modeled aerosol profiles based on combined lidar and sunphotometer data
dc.typeArticle
dc.subject.lemacTroposfera
dc.subject.lemacAire--Qualitat
dc.identifier.doi10.5194/acp-17-7003-2017
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.atmos-chem-phys.net/17/7003/2017/
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/633080/EU/Monitoring Atmospheric Composition and Climate -III/MACC-III
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/654109/EU/Aerosols, Clouds, and Trace gases Research InfraStructure/ACTRIS-2
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/CGL2013-46736
upcommons.citation.publishedtrue
upcommons.citation.publicationNameAtmospheric Chemistry and Physics
upcommons.citation.volume17
upcommons.citation.startingPage7003
upcommons.citation.endingPage7023


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