Articles | Volume 8, issue 9
Atmos. Meas. Tech., 8, 3577–3600, 2015

Special issue: EARLINET, the European Aerosol Research Lidar Network

Atmos. Meas. Tech., 8, 3577–3600, 2015
Research article
04 Sep 2015
Research article | 04 Sep 2015

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

I. Binietoglou1, S. Basart2, L. Alados-Arboledas4,3, V. Amiridis5, A. Argyrouli6, H. Baars7, J. M. Baldasano2, D. Balis8, L. Belegante1, J. A. Bravo-Aranda4,3, P. Burlizzi9, V. Carrasco10, A. Chaikovsky11, A. Comerón12, G. D'Amico13, M. Filioglou8, M. J. Granados-Muñoz4,3, J. L. Guerrero-Rascado4,3, L. Ilic14, P. Kokkalis6,5, A. Maurizi15, L. Mona13, F. Monti15, C. Muñoz-Porcar12, D. Nicolae1, A. Papayannis6, G. Pappalardo13, G. Pejanovic16, S. N. Pereira10, M. R. Perrone9, A. Pietruczuk17, M. Posyniak17, F. Rocadenbosch12,18, A. Rodríguez-Gómez12, M. Sicard12,18, N. Siomos8, A. Szkop17, E. Terradellas19, A. Tsekeri5, A. Vukovic16,20, U. Wandinger7, and J. Wagner7 I. Binietoglou et al.
  • 1National Institute of R&D for Optoelectronics, 409 Atomistilor Str., 77125, Magurele, Ilfov, Romania
  • 2Earth Sciences Department, Barcelona Supercomputing Center, Centro Nacional de Supercomputación (BSC-CNS), Barcelona, Spain
  • 3Department of Applied Physics, Universidad de Granada, Granada, Spain
  • 4Andalusian Institute for Earth System Research (IISTA – CEAMA), University of Granada, Granada, Spain
  • 5National Observatory of Athens, Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (NOA-IAASARS), Athens, Greece
  • 6National Technical University of Athens, Physics Department, Laser Remote Sensing Laboratory, Zografou, Greece
  • 7Leibniz Institute for Tropospheric Research, Leipzig, Germany
  • 8Aristotle University of Thessaloniki, Faculty of Sciences, School of Physics, Thessaloniki, Greece
  • 9Dipartemento di Fisica, Universitá di Lecce, Lecce, Italy
  • 10Èvora Geophysics Centre, Èvora, Portugal
  • 11Institute of Physics, National Academy of Sciences of Belarus, Minsk, Belarus
  • 12Department of Signal Theory and Communications, Remote Sensing Laboratory, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 13Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), Tito Scalo, Potenza, Italy
  • 14Institute of Physics, Belgrade, Serbia
  • 15Consiglio Nazionale delle Ricerche, Istituto di Scienze dell'Atmosfera e del Clima (CNR-ISAC), Bologna, Italy
  • 16South East European Virtual Climate Change Center (SEEVCCC), Republic Hydrometeorological Service of Serbia, Belgrade, Serbia
  • 17Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland
  • 18Institute of Space Studies of Catalonia (IEEC- CRAE), Barcelona, Spain
  • 19AEMET, Barcelona, Spain
  • 20Faculty of Agriculture, University of Belgrade, Belgrade, Serbia

Abstract. 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.