Publications

2023, Farhani, G., Martucci, G., Roberts, T., Haefele, A., Sica, R.J., A Bayesian neural network approach for tropospheric temperature retrievals from a lidar instrument, International Journal of Remote Sensing, 44:5, 1611-1627, http://doi.org/10.1080/01431161.2023.2187723
Tags: Algorithm, Lidar, Temperature

2023, Wizenberg, T., K. Strong, D.B.A. Jones, E. Lutsch, E. Mahieu, B. Franco, and L. Clarisse, Exceptional wildfire enhancements of PAN, C2H4, CH3OH, and HCOOH over the Canadian high Arctic during August 2017, Journal of Geophysical Research: Atmospheres, 128, e2022JD038052, https://doi.org/10.1029/2022JD038052
Tags: C2H4, CH3OH, Fire, FTIR, HCOOH

2023, Chang, K.L., O.R. Cooper, G. Rodriguez, L.T. Iraci, E.L. Yates, M.S. Johnson, A. Gaudel, D.A. Jaffe, N. Bernays, H. Clark, P. Effertz, T. Leblanc, I. Petropavlovskikh, B. Sauvage, D.W. Tarasick , Diverging ozone trends above western North America: boundary layer decreases vs. free tropospheric increases, Journal of Geophysical Research: Atmospheres, 128, e2022JD038090, https://doi.org/10.1029/2022JD038090
Tags: Dobson, Lidar, Ozone, Sonde, Trends

2022, Summa, D., F. Madonna, N. Franco, B. De Rosa, and P. Di Girolamo , Inter-comparison of atmospheric boundary layer (ABL) height estimates from different profiling sensors and models in the framework of HyMeX-SOP1, Atmospheric Measurement Techniques, 15, 4153–4170, https://doi.org/10.5194/amt-15-4153-2022
Tags: Lidar, Model

2022, Kotsakis, A., John T. Sullivan, Thomas F. Hanisco, Robert J. Swap, Vanessa Caicedo, Timothy A. Berkoff, Guillaume Gronoff et al., Sensitivity of total column NO2 at a marine site within the Chesapeake Bay during OWLETS-2, Atmospheric Environment, 277, 119063
Tags: Lidar, NO2

2022, Yang, Z., B. Demoz, R. Delgado, A. Tangborn, P. Lee, and J.T. Sullivan, The Dynamical Role of the Chesapeake Bay on the Local Ozone Pollution Using Mesoscale Modeling—A Case Study, Atmosphere, 13(5), 641
Tags: Lidar, Model, Ozone

2022, Koukouli, M.-E., Pseftogkas A, Karagkiozidis D, Skoulidou I, Drosoglou T, Balis D, Bais A, Melas D, Hatzianastassiou N., Air Quality in Two Northern Greek Cities Revealed by Their Tropospheric NO2 Levels, Atmosphere, 13(5):840, https://doi.org/10.3390/atmos13050840
Tags: NO2, UVVis

2022, Flamant, C., P. Chazette, O. Caumont, P. Di Girolamo, A. Behrendt, M. Sicard, J. Totems, D. Lange, N. Fourrié, P. Brousseau, C. Augros, A. Baron, M. Cacciani, A. Comerón, B. De Rosa, V. Ducrocq, P. Genau, L. Labatut, C. Muñoz-Porcar, A. Rodríguez-Gómez, D. Summa, R. Thundathil, and V. Wulfmeyer , A network of water vapor Raman lidars for improving heavy precipitation forecasting in southern France: introducing the WaLiNeAs initiative, Bulletin of Atmospheric Science and Technology, 2, 10 , https://doi.org/10.1007/s42865-021-00037-6
Tags: H2O, Lidar

2022, Di Girolamo, P., F. Pini, G. Piras, The effect of COVID-19 on the distribution of PM10 pollution classes of vehicles: Comparison between 2020 and 2018, Science of the Total Environment, 811, 152036
Tags: Aerosol, Lidar

2022, Trieu, T.T.N., I. Morino, O. Uchino, Y. Tsutsumi, T. Sakai, T. Nagai, A. Yamazaki, H. Okumura, K. Arai, K. Shiomi, D.F. Pollard, B. Liley , Influences of aerosols and thin cirrus clouds on GOSAT XCO2 and XCH4 using Total Carbon Column Observing Network, sky radiometer, and lidar data, International Journal of Remote Sensing, 43:5, 1770-1799, https://doi.org/10.1080/01431161.2022.2038395
Tags: Aerosol, Clouds, FTIR, Lidar, Satellite, UVVis, XCH4, XCO2