Publication Citation
Abel, S., et al. (2019), Open cells can decrease the mixing of free-tropospheric biomass burning aerosol into the south-east Atlantic boundary layer, Atmos. Chem. Phys., doi:10.5194/acp-2019-738 (submitted).
Adebiyi, A., and P. Zuidema (2018), Low Cloud Cover Sensitivity to Biomass-Burning Aerosols and Meteorology over the Southeast Atlantic, J. Climate, 31, 4329-4346, doi:10.1175/JCLI-D-17-0406.1.
Adebiyi, A., et al. (2020), Mid-level clouds are frequent above the southeast Atlantic stratocumulus clouds, Atmos. Chem. Phys., 1-28, doi:10.5194/acp-2020-324.
Burton, S., et al. (2018), Calibration of a high spectral resolution lidar using a Michelson interferometer, with data examples from ORACLES, Appl. Opt., 57, 6061-6075, doi:10.1364/AO.57.006061.
Cochrane, S., et al. (2019), Above-cloud aerosol radiative effects based on ORACLES 2016 and ORACLES 2017 aircraft experiments, Atmos. Meas. Tech., 12, 6505-6528, doi:10.5194/amt-12-6505-2019.
Cochrane, S., et al. (2020), The Dependence of Aerosol Radiative Effects on Spectral Aerosol Properties Derived from Aircraft Measurements: Results from the ORACLES 2016 and ORACLES 2017 Experiments, Atmos. Chem. Phys. (manuscript in preparation).
Das, S., N. Harshvardhan, and P. R. Colarco (2020), The influence of elevated smoke layers on stratocumulus clouds over the SE Atlantic in the NASA Goddard Earth Observing System (GEOS) model, J. Geophys. Res., 125, 1-20, doi:https://doi.org/10.1029/2019JD031209.
diamond, M. (2020), Substantial Cloud Brightening from Shipping in Subtropical Low Clouds, AGU Advances, doi:https://doi.org/10.1029/2019AV000111.
Diamond, M., et al. (2018), Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean, Atmos. Chem. Phys., 18, 14623-14636, doi:10.5194/acp-18-14623-2018.
Diamond, M., et al. (2020), Substantial Cloud Brightening From Shipping in Subtropical Low Clouds, AGU Advances, 1, 1-28, doi:10.1029/2019AV000111.
Dzambo, A., et al. (2019), The Observed Structure and Precipitation Characteristics of Southeast Atlantic Stratocumulus from Airborne Radar during ORACLES 2016-17, J. Appl. Meteor. Climat., 58, 2197-2215, doi:https://doi.org/10.1175/JAMC-D-19-0032.1.
Herman, R. L., et al. (2019), Comparison of Optimal Estimation HDO/H2O Retrievals from AIRS with ORACLES measurements, doi:https://doi.org/10.5194/amt-2019-195 (submitted).
Holben, B., et al. (2018), An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks, Atmos. Chem. Phys., 18, 655-671, doi:10.5194/acp-18-655-2018.
Jethva, H., O. Torres, and C. Ahn (2018), A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm, Atmos. Meas. Tech., 11, 5837-5864, doi:10.5194/amt-11-5837-2018.
Kacarab, M., et al. (2020), Biomass Burning Aerosol as a Modulator of Droplet Number in the Southeast Atlantic Region, Atmos. Chem. Phys., 20, 3029-3040, doi:10.5194/acp-20-3029-2020.
LeBlanc, S. (2018), samuelleblanc/fp: Moving Lines: NASA airborne research flight planning tool release (Version v1.21), Zenodo., doi:10.5281/zenodo.1478126.
LeBlanc, S., et al. (2020), Above-cloud aerosol optical depth from airborne observations in the southeast Atlantic, Atmos. Chem. Phys., 20, 1565-1590, doi:10.5194/acp-20-1565-2020.
Mallet, M., et al. (2019), Simulation of the transport, vertical distribution, optical properties and radiative impact of smoke aerosols with the ALADIN regional climate model during the ORACLES-2016 and LASIC experiments, Atmos. Chem. Phys., 19, 4963-4990, doi:10.5194/acp-19-4963-2019.
Mallet, M., et al. (2020), Direct and semi-direct radiative forcing of biomass burning aerosols over the Southeast Atlantic (SEA) and its sensitivity to absorbing properties: a regional climate modeling study, Atmos. Chem. Phys., acp-2020-317 (manuscript in preparation).
Miller, D. J., et al. (2019), Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm, doi:https://doi.org/10.5194/amt-2019-327 (submitted).

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