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
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
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
2020, Polyakov, A., Y. Virolainen, A. Poberovskiy, M. Makarova and Y. Timofeyev, Atmospheric HCFC-22 total columns near St. Petersburg: stabilization with start of a decrease, International Journal of Remote Sensing, 41(11), 4365-4371, https://doi.org/10.1080/01431161.2020.1717668
Tags: FTIR, HCFC-22, Trends
2020, Sterckx, S., Ian Brown, Andreas Kääb, Maarten Krol, Rosemary Morrow, Pepijn Veefkind, K. Folkert Boersma, Martine De Mazière, Nigel Fox & Peter Thorne, Towards a European Cal/Val service for earth observation, International Journal of Remote Sensing, 41:12, 4496-4511, https://doi.org/10.1080/01431161.2020.1718240
Tags: FTIR, Validation
2020, Di Girolamo, P., B. De Rosa, C. Flamant, D. Summa, O. Bousquet, P. Chazette, J. Totems, M. Cacciani, Water vapour mixing ratio and temperature inter-comparison results in framework of the Hydrological Cycle in the Mediterranean Experiment—Special Observation Period 1, Bulletin of Atmospheric Science and Technology, 1, 113–153, https://doi.org/10.1007/s42865-020-00008-3
Tags: H2O, Lidar, Temperature
2020, Di Girolamo, P., Assessment of the potential role of atmospheric particulate pollution and airborne transmission in intensifying the first wave pandemic impact of SARS-CoV-2/COVID-19 in Northern Italy, Bulletin of Atmospheric Science and Technology, 1, 515–550, https://doi.org/10.1007/s42865-020-00024-3
Tags: Lidar, Aerosol, Health
2018, Geddes, A., et al., Python-based dynamic scheduling assistant for atmospheric measurements by Bruker instruments using OPUS, Applied Optics, 57(4), 689-691
Tags: Algorithm, FTIR
2016, Sica, R.J., A. Haefele, Retrieval of water vapor mixing ratio from a multiple channel Raman-scatter lidar using an optimal estimation method, Applied Optics, 55, 763-777
Tags: H2O, Lidar
2015, Sica, R., Haefele, A., Retrieval of temperature from a multiple-channel Rayleigh-scatter lidar using an optimal estimation method, Applied Optics, 54, 1872–1889
Tags: Lidar, Temperature