Christensen, M.W., et al. (2022), Opportunistic experiments to constrain aerosol effective radiative forcing, Atmos. Chem. Phys., doi:10.5194/acp-22-641-2022.
ACTIVATE
Gryspeerdt, E., et al. (2022), The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., doi:10.5194/amt-2021-371.
Tornow, F., et al. (2022), Dilution of Boundary Layer Cloud Condensation Nucleus Concentrations by Free Tropospheric Entrainment During Marine Cold Air Outbreaks, Geophys. Res. Lett., 49, e2022GL09844, doi:10.1029/2022GL098444.
Gonzalez, M., et al. (2022), Relationships between supermicrometer particle concentrations and cloud water sea salt and dust concentrations: analysis of MONARC and ACTIVATE data, Environmental Science: Atmospheres, doi:10.1039/d2ea00049k.
Schlosser, J.S., et al. (2022), Polarimeter + Lidar–Derived Aerosol Particle Number Concentration, Front. Remote Sens., 3, 885332, doi:10.3389/frsen.2022.885332.
Painemal, D., et al. (2020), Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations, Atmos. Chem. Phys., 20, 7167-7177, doi:10.5194/acp-20-7167-2020.
Ouyed, A., et al. (2021), Two-Stage Artificial Intelligence Algorithm for Calculating Moisture-Tracking Atmospheric Motion Vectors, J. Appl. Meteor. Climat., 60, 1671-1684, doi:10.1175/JAMC-D-21-0070.1.
Hilario, M.R.A., et al. (2022), Particulate Oxalate-To-Sulfate Ratio as an Aqueous Processing Marker: Similarity Across Field Campaigns and Limitations, Geophys. Res. Lett..
Li, X., et al. (2022), Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part I: Case Setup and Sensitivities to Large-Scale Forcings, J. Atmos. Sci., 79, 73-100, doi:10.1175/JAS-D-21-0123.1.
