Journal papers in 2022

  1. Abril-Gago, J., Guerrero-Rascado, J. L., Costa, M. J., Bravo-Aranda, J. A., Sicard, M., Bermejo-Pantaleón, D., Bortoli, D., Granados-Muñoz, M. J., Rodríguez-Gómez, A., Muñoz-Porcar, C., Comerón, A., Ortiz-Amezcua, P., Salgueiro, V., Jiménez-Martín, M. M., and Alados-Arboledas, L.: Statistical validation of Aeolus L2A particle backscatter coefficient retrievals over ACTRIS/EARLINET stations on the Iberian Peninsula, Atmos. Chem. Phys., 22, 1425–1451, doi.org/10.5194/acp-22-1425-2022, 2022

  2. De Marco, C., Boselli, A., D’Anna, A., Sannino, A., Sasso, G., Sirignano, M., Spinelli, N. and Wang, X., Mutiparametric Characterization of Atmospheric Particulate in a Heavy-Polluted Area of South Italy, Atmospheric and Climate Sciences, 12, 493-516. doi: 10.4236/acs.2022.122029, 2022.

  3. De Rosa, B.; Amato, F.; Amodeo, A.; D’Amico, G.; Dema, C.; Falconieri, A.; Giunta, A.; Gumà-Claramunt, P.; Kampouri, A.; Solomos, S.; Mytilinaios, M.; Papagiannopoulos, N.; Summa, D.; Veselovskii, I.; Mona, L. Characterization of Extremely Fresh Biomass Burning Aerosol by Means of Lidar Observations. Remote Sens. 14, 4984. doi.org/10.3390/rs14194984, 2022.

  4. Ehlers, F., Flament, T., Dabas, A., Trapon, D., Lacour, A., Baars, H., and Straume-Lindner, A. G.: Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation, Atmos. Meas. Tech., 15, 185–203, doi.org/10.5194/amt-15-185-2022, 2022.

  5. Escribano, J., Di Tomaso, E., Jorba, O., Klose, M., Gonçalves Ageitos, M., Macchia, F., Amiridis, V., Baars, H., Marinou, E., Proestakis, E., Urbanneck, C., Althausen, D., Bühl, J., Mamouri, R.-E., and Pérez García-Pando, C.: Assimilating spaceborne lidar dust extinction can improve dust forecasts, Atmos. Chem. Phys., 22, 535–560, doi.org/10.5194/acp-22-535-2022, 2022.

  6. Evgenieva, T.; Gurdev, L.; Toncheva, E.; Dreischuh, T. Optical and Microphysical Properties of the Aerosol Field over Sofia, Bulgaria, Based on AERONET Sun-Photometer Measurements. Atmosphere, 13, 884. doi.org/10.3390/atmos13060884, 2022.

  7. Fajardo-Zambrano, C.M.; Bravo-Aranda, J.A.; Granados-Muñoz, M.J.; Montilla-Rosero, E.; Casquero-Vera, J.A.; Rejano, F.; Castillo, S.; Alados-Arboledas, L. Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index. Remote Sens., 14, 1333. doi.org/10.3390/rs14061333, 2022.

  8. Fragola, M.; Arsieni, A.; Carelli, N.; Dattoli, S.; Maiellaro, S.; Perrone, M.R.; Romano, S. Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights. Int. J. Environ. Res. Public Health, 19, 2624. doi.org/10.3390/ijerph19052624, 2022.

  9. Ilic, L., Jovanovic, A., Kuzmanoski, M., Lazic, L., Madonna, F., Rosoldi, M., Mytilinaios, M., Marinou, E., and Nickovic, S., Mineralogy sensitive immersion freezing parameterization in DREAM. Journal of Geophysical Research: Atmospheres, 127, e2021JD035093. doi.org/10.1029/2021JD035093, 2022.

  10. Heese, B., Floutsi, A. A., Baars, H., Althausen, D., Hofer, J., Herzog, A., Mewes, S., Radenz, M., and Schechner, Y. Y.: The vertical aerosol type distribution above Israel – 2 years of lidar observations at the coastal city of Haifa, Atmos. Chem. Phys., 22, 1633–1648, doi.org/10.5194/acp-22-1633-2022, 2022.

  11. Mereuta, A., Ajtai, N., Radovici, A. T., Papagiannopoulos, N., Deaconu, L. T., Botezan, C. S., Stefanie, H. I., Nicolae, D., and Ozunu, A.: A novel method of identifying and analysing oil smoke plumes based on MODIS and CALIPSO satellite data, Atmos. Chem. Phys., 22, 5071–5098, doi.org/10.5194/acp-22-5071-2022, 2022.

  12. Perrone, M.R., Lorusso, A., Romano, S.: Diurnal and nocturnal aerosol properties by AERONET sun-sky-lunar photometer measurements along four years. Atmos. Res. 265, 105889, ISSN: 0169-8095. DOI: 10.1016/j.atmosres.2021.105889, 2022.

  13. Perrone, M.R., Paladini, F., Becagli, S. et al. Daytime and nighttime chemical and optical properties of fine and coarse particles at a central Mediterranean coastal site. Environ Sci Pollut Res 29, 43401–43420, doi.org/10.1007/s11356-021-18173-z, 2022.

  14. Peshev, Z.; Deleva, A.; Vulkova, L.; Dreischuh, T. Large-Scale Saharan Dust Episode in April 2019: Study of Desert Aerosol Loads over Sofia, Bulgaria, Using Remote Sensing, In Situ, and Modeling Resources. Atmosphere, 13, 981. doi.org/10.3390/atmos13060981, 2022.

  15. Pignatti, S.; Amodeo, A.; Carfora, M.F.; Casa, R.; Mona, L.; Palombo, A.; Pascucci, S.; Rosoldi, M.; Santini, F.; Laneve, G. PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy. Remote Sens., 14, 1985. doi.org/10.3390/rs14091985, 2022.

  16. Sannino, A., Amoruso, S., Damiano, R., Scollo, S., Sellitto, P., Boselli, A., Aerosol Optical and Microphysical Characterization of Atmospheric Aerosol in the Central Mediterranean during Simultaneous Volcanic Ash and Desert Dust Transport Events, Atmospheric Research, Volume 271,106099, https://doi.org/10.1016/j.atmosres.2022.106099, 2022.

  17. Shang, X., Baars, H., Stachlewska, I. S., Mattis, I., and Komppula, M.: Pollen observations at four EARLINET stations during the ACTRIS-COVID-19 campaign, Atmos. Chem. Phys., 22, 3931–3944, doi.org/10.5194/acp-22-3931-2022, 2022.

  18. Wang, Y.; Amodeo, A.; O’Connor, E.J.; Baars, H.; Bortoli, D.; Hu, Q.; Sun, D.; D’Amico, G. Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products. Remote Sens., 14, 2342. doi.org/10.3390/rs14102342, 2022.

  19. Zhang, H.; Wagner, F.; Saathoff, H.; Vogel, H.; Hoshyaripour, G.; Bachmann, V.; Förstner, J.; Leisner, T. Comparison of Scanning LiDAR with Other Remote Sensing Measurements and Transport Model Predictions for a Saharan Dust Case. Remote Sens., 14, 1693. doi.org/10.3390/rs14071693, 2022.