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> Publications for ACTIVATE
Publication Citation
Ferrare, R.
,
et al.
(2023),
Airborne HSRL-2 measurements of elevated aerosol depolarization associated with non-spherical sea salt
,
TYPE Original Research
, doi:10.3389/frsen.2023.1143944.
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.
Gryspeerdt, E.,
et al.
(2020),
Surprising similarities in model and observational aerosol radiative forcing estimates
,
Atmos. Chem. Phys., 20
, 613-623, doi:10.5194/acp-20-613-2020.
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.
Gryspeerdt, E.,
et al.
(2023),
Uncertainty in aerosol–cloud radiative forcing is driven by clean conditions
,
Atmos. Chem. Phys.
, doi:10.5194/acp-23-4115-2023.
Hilario, M.
,
et al.
(2022),
Particulate Oxalate-To-Sulfate Ratio as an Aqueous Processing Marker: Similarity Across Field Campaigns and Limitations
,
Geophys. Res. Lett.
.
Kirschler, S.,
et al.
(2022),
Seasonal updraft speeds change cloud droplet number concentrations in low-level clouds over the western North Atlantic
,
Atmos. Chem. Phys.
, doi:10.5194/acp-22-8299-2022.
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.
Li, X.,
et al.
(2023),
Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part II: Aerosol–Meteorology–Cloud Interaction
,
J. Atmos. Sci., 80
, 1025-1045, doi:10.1175/JAS-D-21-0324.1.
Ma, L.,
et al.
(2021),
Contrasting wet deposition composition between three diverse islands and coastal North American sites
,
Atmos. Environ., 244
, 117919, doi:10.1016/j.atmosenv.2020.117919.
MacDonald, A. B.,
et al.
(2020),
On the relationship between cloud water composition and cloud droplet number concentration
,
Atmos. Chem. Phys., 20
, 7645-7665, doi:10.5194/acp-20-7645-2020.
Mardi, A. H.,
et al.
(2019),
All Rights Reserved. Effects of Biomass Burning on Stratocumulus Droplet Characteristics, Drizzle Rate, and Composition
,
J. Geophys. Res., 124
, 12,301-12,318, doi:10.1029/2019JD031159.
Mardi, A. H.,
et al.
(2021),
Biomass Burning Over the United States East Coast and Western North Atlantic Ocean: Implications for Clouds and Air Quality
,
J. Geophys. Res., 126
, e2021JD034916, doi:10.1029/2021JD034916.
Nied, J.,
et al.
(2023),
A cloud detection neural network for above-aircraft clouds using airborne cameras
,
Frontiers in Remote Sensing, 4
, 10.3389/frsen.2023.1118745, doi:10.3389/frsen.2023.1118745.
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.
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.
Painemal, D.
,
et al.
(2021),
All Rights Reserved. An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast— Part 2: Circulation, Boundary Layer, and Clouds
,
J. Geophys. Res., 126
, e2020JD033423, doi:10.1029/2020JD033423.
Painemal, D.
,
et al.
(2023),
Wintertime Synoptic Patterns of Midlatitude Boundary Layer Clouds Over the Western North Atlantic: Climatology and Insights From In Situ ACTIVATE Observations
,
J. Geophys. Res., 128
, e2022JD037725, doi:10.1029/2022JD037725.
Painemal, D.
,
et al.
(2023),
Wintertime Synoptic Patterns of Midlatitude Boundary Layer Clouds Over the Western North Atlantic: Climatology and Insights From In Situ ACTIVATE Observations
,
J. Geophys. Res., 128
, e2022JD037725, doi:10.1029/2022JD037725.
Park, H. J.,
et al.
(2020),
Predicting Vertical Concentration Profiles in the Marine Atmospheric Boundary Layer With a Markov Chain Random Walk Model
,
J. Geophys. Res., 125
, e2020JD032731, doi:10.1029/2020JD032731.
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