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Extent

1 digital object.

Cite This Work

Dedrick, Jeramy L.; Saliba, Georges; Williams, Abigail S.; Russell, Lynn M.; Lubin, Dan (2022). Data from: Retrieval of the Sea Spray Aerosol Mode from Submicron Particle Size Distributions and Supermicron Scattering during LASIC. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0GT5NCR

Description

This collection provides code and data that are described in the publication "Retrieval of the Sea Spray Aerosol Mode from Submicron Particle Size Distributions and Supermicron Scattering during LASIC." Lognormal fitting parameters of the sea spray aerosol mode were retrieved by applying a Mie inversion of submicron particle size distributions from an Ultra-High Sensitivity Aerosol Spectrometer and supermicron scattering from a 3-wavelength integrating nephelometer during the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds campaign. As an external validation of the method, submicron size distributions from a Scanning Electrical Mobility Particle Sizer and 3-wavelength integrating nephelometer were used for comparison with a supermicron size distribution-constrained estimate of the sea spray mode from an Aerodynamic Particle Sizer during the NASA North Atlantic Aerosols and Marine Ecosystems Study. These datasets are provided as text files at 2-hourly resolution for clean marine periods of each campaign. Additionally, the retrieval codes and Mie scattering simulation codes used to produce these datasets are provided as MATLAB functions, scripts, and matrix files.

Creation Date

• 2020 to 2022

Date Issued

• 2022

Author

• Dedrick, Jeramy L.

Contributors

• Lubin, Dan
• Russell, Lynn M.
• Saliba, Georges
• Williams, Abigail, S.

Funding

This research was supported by the Director, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division of the U.S. Department of Energy under Contract DE-SC0021045.

Topics

• Marine aerosol
• Particle scattering
• Sea spray
• Sea spray aerosol (SSA)

Formats

View formats within this collection

• data
• text

Language

• English

Identifier

Identifier: Georges Saliba: https://orcid.org/0000-0002-4305-1305

Identifier: Jeramy L. Dedrick: https://orcid.org/0000-0003-3569-0235

Identifier: Lynn M. Russell: https://orcid.org/0000-0002-6108-2375

Related Resources

Primary associated publication

• Dedrick, J. L., Saliba, G., Williams, A. S., Russell, L. M., and Lubin, D. (2022). Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC, Atmos. Meas. Tech., 15, 4171–4194. https://doi.org/10.5194/amt-15-4171-2022

Source data

• Department of Energy Atmospheric Radiation Measurement Data Discovery: https://adc.arm.gov/discovery/
• National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory NAAMES Data Archive: https://saga.pmel.noaa.gov/data/download.php?cruise=NAAMES1
• Russell, Lynn M.; Chen, Chia-Li; Betha, Raghu; Price, Derek J.; Lewis, Savannah (2018). NAAMES1 Research Cruise Aerosol Measurements (2015). In Aerosol Particle Chemical and Physical Measurements on the 2015, 2016, 2017, and 2018 North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) Research Cruises. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0736P3J

Software

• GitHub repository of codes used in this work by author Jeramy Dedrick: https://github.com/jdedrick95/get_sea_spray_mode

Reference

• Mätzler, Christian. (2002). Matlab Functions for Mie Scattering and Absorption. Research Report No. 2002-08. June 2002. 13. 125-128. https://omlc.org/software/mie/maetzlermie/Maetzler2002.pdf
• Saliba, G., Chen, C., Lewis, S., Russell, L., Rivellini, L., Lee, A., Quinn, P., Bates, T., Haentjens, N., Boss, E., Karp-Boss, L., Baetge, N., Carlson, C., and Behrenfeld, M.: Factors driving the seasonal and hourly variability of sea-spray aerosol number in the North Atlantic, Proceedings of the National Academy of Sciences of the United States of America, Oct 2019, 116, 20309-20314 https://doi.org/10.1073/pnas.1907574116