Articles | Volume 16, issue 7
https://doi.org/10.5194/amt-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2023
Research article |
| 17 Apr 2023
| 17 Apr 2023
Use of lidar aerosol extinction and backscatter coefficients to estimate cloud condensation nuclei (CCN) concentrations in the southeast Atlantic
School of Meteorology, University of Oklahoma, Norman, OK 73072,
United States
School of Meteorology, University of Oklahoma, Norman, OK 73072,
United States
Jens Redemann
School of Meteorology, University of Oklahoma, Norman, OK 73072,
United States
Feng Xu
School of Meteorology, University of Oklahoma, Norman, OK 73072,
United States
Sharon P. Burton
NASA Langley Research Center, Hampton, VA 23666, United States
Brian Cairns
NASA Goddard Institute for Space Studies, New York, NY 10025, United States
Ian Chang
School of Meteorology, University of Oklahoma, Norman, OK 73072,
United States
Richard A. Ferrare
NASA Langley Research Center, Hampton, VA 23666, United States
Chris A. Hostetler
NASA Langley Research Center, Hampton, VA 23666, United States
Pablo E. Saide
Department of Atmospheric and Oceanic Sciences, University of
California – Los Angeles, Los Angeles, CA 90095, United States
Institute of the Environment and Sustainability, University of
California – Los Angeles, Los Angeles, CA 90095, United States
Calvin Howes
Department of Atmospheric and Oceanic Sciences, University of
California – Los Angeles, Los Angeles, CA 90095, United States
Yohei Shinozuka
Bay Area Environmental Research Institute, Moffett Field, CA 94035, United States
Snorre Stamnes
NASA Langley Research Center, Hampton, VA 23666, United States
Mary Kacarab
School of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, GA 30332, United States
Amie Dobracki
Department of Atmospheric Sciences, University of Miami, Miami, FL 33146, United States
Jenny Wong
Department of Chemistry and Biochemistry, Mount Allison University,
Sackville, New Brunswick, E4L 1E2, Canada
Steffen Freitag
State Agency for Nature, Environment and Consumer Protection North
Rhine-Westphalia, 45659 Recklinghausen, Germany
Athanasios Nenes
Institute for Chemical Engineering Sciences, Foundation for Research and Technology, Hellas, 26504 Patras, Greece
School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique fédérale de Lausanne, 1015 Lausanne, Switzerland
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Cited
4 citations as recorded by crossref.
- Understanding Aerosol–Cloud Interactions through Lidar Techniques: A Review F. Cairo et al. 10.3390/rs16152788
- A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing J. Redemann & L. Gao 10.1038/s41467-024-52747-y
- In situ and satellite-based estimates of cloud properties and aerosol–cloud interactions over the southeast Atlantic Ocean S. Gupta et al. 10.5194/acp-22-12923-2022
- POLIPHON conversion factors for retrieving dust-related cloud condensation nuclei and ice-nucleating particle concentration profiles at oceanic sites Y. He et al. 10.5194/amt-16-1951-2023
2 citations as recorded by crossref.
2 citations as recorded by crossref.
- In situ and satellite-based estimates of cloud properties and aerosol–cloud interactions over the southeast Atlantic Ocean S. Gupta et al. 10.5194/acp-22-12923-2022
- POLIPHON conversion factors for retrieving dust-related cloud condensation nuclei and ice-nucleating particle concentration profiles at oceanic sites Y. He et al. 10.5194/amt-16-1951-2023
Latest update: 04 Nov 2024
Short summary
Small atmospheric particles, such as smoke from wildfires or pollutants from human activities, impact cloud properties, and clouds have a strong influence on climate. To better understand the distributions of these particles, we develop relationships to derive their concentrations from remote sensing measurements from an instrument called a lidar. Our method is reliable for smoke particles, and similar steps can be taken to develop relationships for other particle types.
Small atmospheric particles, such as smoke from wildfires or pollutants from human activities,...
