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- ItemEvaluation of the Lidar/Radiometer Inversion Code (LIRIC) to determine microphysical properties of volcanic and desert dust(München : European Geopyhsical Union, 2013) Wagner, J.; Ansmann, A.; Wandinger, U.; Seifert, P.; Schwarz, A.; Tesche, M.; Chaikovsky, A.; Dubovik, O.The Lidar/Radiometer Inversion Code (LIRIC) combines the multiwavelength lidar technique with sun/sky photometry and allows us to retrieve vertical profiles of particle optical and microphysical properties separately for fine-mode and coarse-mode particles. After a brief presentation of the theoretical background, we evaluate the potential of LIRIC to retrieve the optical and microphysical properties of irregularly shaped dust particles. The method is applied to two very different aerosol scenarios: a strong Saharan dust outbreak towards central Europe and an Eyjafjallajökull volcanic dust event. LIRIC profiles of particle mass concentrations for the coarse-mode as well as for the non-spherical particle fraction are compared with results for the non-spherical particle fraction as obtained with the polarization-lidar-based POLIPHON method. Similar comparisons for fine-mode and spherical particle fractions are presented also. Acceptable agreement between the different dust mass concentration profiles is obtained. LIRIC profiles of optical properties such as particle backscatter coefficient, lidar ratio, Ångström exponent, and particle depolarization ratio are compared with direct Raman lidar observations. Systematic deviations between the LIRIC retrieval products and the Raman lidar measurements of the desert dust lidar ratio, depolarization ratio, and spectral dependencies of particle backscatter and lidar ratio point to the applied spheroidal-particle model as main source for these uncertainties in the LIRIC results.
- ItemA methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals(München : European Geopyhsical Union, 2015) Binietoglou, I.; Basart, S.; Alados-Arboledas, L.; Amiridis, V.; Argyrouli, A.; Baars, H.; Baldasano, J.M.; Balis, D.; Belegante, L.; Bravo-Aranda, J.A.; Burlizzi, P.; Carrasco, V.; Chaikovsky, A.; Comerón, A.; D'Amico, G.; Filioglou, M.; Granados-Muñoz, M.J.; Haefele, A.; Hervo, M.; Iarlori, M.; Kokkalis, P.; Lange, D.; Mamouri, R.E.; Mattis, I.; Molero, F.; Montoux, N.; Muñoz, A.; Muñoz Porcar, C.; Navas-Guzmán, F.; Nicolae, D.; Nisantzi, A.; Papagiannopoulos, N.; Papayannis, A.; Pereira, S.; Preißler, J.; Pujadas, M.; Rizi, V.; Rocadenbosch, F.; Sellegri, K.; Simeonov, V.; Tsaknakis, G.; Wagner, F.; Pappalardo, G.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.