The following papers are associated with the ORACLES mission.

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.
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.
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, doi:https://doi.org/10.5194/amt-2019-125 (submitted).
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.
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. (2019), Biomass Burning Aerosol as a Modulator of Droplet Number in the Southeast Atlantic Region, doi:https://10.5194/acp-2019-791 (submitted).
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. (2019), Above Cloud Aerosol Optical Depth from airborne observations in the South-East Atlantic, doi: https://doi.org/10.5194/acp-2019-43 (submitted).
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.
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).
Pennypacker, S., M. Diamond, and R. Wood (2019), Ultra-clean and smoky marine boundary layers frequently occur in the same season over the southeast Atlantic, Atmos. Chem. Phys., doi:10.5194/acp-2019-628.
Pistone, K., et al. (2019), Intercomparison of biomass burning aerosol optical properties from in situ and remote-sensing instruments in ORACLES-2016, Atmos. Chem. Phys., 19, 9181-9208, doi:10.5194/acp-19-9181-2019.
Sayer, A. M., et al. (2019), Two decades observing smoke above clouds in the south-eastern Atlantic Ocean: Deep Blue algorithm updates and validation with ORACLES field campaign data, Atmos. Meas. Tech., 12, 3595-3627, doi:10.5194/amt-12-3595-2019.
Segal-Rozenhaimer, M., et al. (2018), Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations, J. Quant. Spectrosc. Radiat. Transfer, 220, 39-51, doi:10.1016/j.jqsrt.2018.08.030.
Shinozuka, Y., et al. (2019), Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016, Atmos. Chem. Phys. Discuss., doi: https://doi.org/10.5194/acp-2019-678 (submitted).
Star, T., et al. (2018), 4STAR_codes: 4STAR processing codes, Zenodo, doi:10.5281/zenodo.1492912.

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