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Aerosol Fine Mode Fraction Retrievals for the Marine Boundary Layer From Airborne Lidar.

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Title: Aerosol Fine Mode Fraction Retrievals for the Marine Boundary Layer From Airborne Lidar.
Authors: Saide, Pablo E.1,2 (AUTHOR) saide@atmos.ucla.edu, Christopoulos, Julianna1 (AUTHOR), Ferrare, Richard A.3 (AUTHOR), Hair, Johnathan W.3 (AUTHOR), Shingler, Taylor3 (AUTHOR), Fenn, Marta A.4 (AUTHOR), Scarino, Amy Jo4 (AUTHOR), Burton, Sharon P.3 (AUTHOR), Nehrir, Amin R.3 (AUTHOR), Barton‐Grimley, Rory A.3 (AUTHOR), Collister, Brian L.3 (AUTHOR), Moore, Richard H.3 (AUTHOR), Ziemba, Luke D.3 (AUTHOR), Shook, Michael A.3 (AUTHOR), Schlosser, Joseph3,5 (AUTHOR), Crosbie, Ewan4 (AUTHOR), Voigt, Christiane6,7 (AUTHOR), Kirschler, Simon6,7 (AUTHOR), DiGangi, Joshua P.3 (AUTHOR), Diskin, Glenn S.3 (AUTHOR)
Source: Journal of Geophysical Research. Atmospheres. 10/28/2025, Vol. 130 Issue 20, p1-17. 17p.
Subject Terms: *Aerosols, *Aerosol analysis, LIDAR, Optical properties, Remote sensing, Atmospheric boundary layer, Multiple regression analysis
Abstract: Separating contributions of the fine and coarse modes is important for characterizing aerosols and assessing their impacts. This work develops retrievals of fine mode fraction (FMF) from lidar observables for the marine boundary layer (MBL) using data collected during the ACTIVATE field campaign. First, we calculate multiwavelength backscatter and extinction and derived metrics for spherical particles derived from measured size distributions (combining in situ aerosol and cloud probes) and hygroscopicity estimates. The calculations show reasonable skill when compared to airborne High Spectral Resolution Lidar—generation 2 (HSRL‐2) retrievals, displaying low biases and explaining up to 87% of the variability in backscattering. While slopes are generally close to 1:1 for lidar ratios and Angstrom exponents (AEs), the variability within HSRL‐2 data is only well captured for lidar ratios (50%–67%). Having established that the calculated optical properties are representative of remotely sensed ones in the marine environment, they are used together with in situ aircraft particle size data to train multilinear regression models to estimate FMF proxies (extinction FMF, PM1/PM10 and PM2.5/PM10 ratios). When tested with HSRL‐2 observations as inputs, these models can represent up to 67%–78% of the variability of the observed FMF proxies with biases at high FMFs that depend on the accuracy of the coarse mode aerosol size measurements. The regression retrievals are tested for lidar transects and show expected gradients due to continental influence on the MBL and differential hygroscopicity of fine versus coarse mode aerosol with height. These results are encouraging for their application for various lidar systems. Plain Language Summary: Aerosols are particles suspended in the air and thus one of their characteristics is that they can be of different sizes. In this work, we develop ways to estimate the relative abundance of fine (∼below 1–2.5 μm in diameter) versus coarse particles (defined as fine mode fraction) using active remote sensing (lidar) observations. For that, we utilize field campaign measurements where lidar and in situ measurements from two aircraft were coordinated in time and space so they sample similar air masses. Since the lidar provides retrievals of how aerosols interact with visible light (aerosol optical properties), we first evaluate how a model that uses detailed information of aerosol size and composition from the in situ aircraft can represent these retrievals. After finding reasonable performance, we then calibrate a linear regression model to predict fine mode fraction based on optical property calculations. This new retrieval was then driven by lidar observations and evaluated against in situ fine mode fraction observations finding satisfactory performance. We also found the fine mode fraction retrievals reproduce expected changes happening between the continent and the remote ocean. These retrievals can be used in other lidars with similar characteristics. Key Points: Retrievals for fine mode aerosol fraction proxies are developed using ACTIVATE campaign dataOptical property calculations driven by in situ data show reasonable representation of extinction and backscattering‐based lidar observablesMultilinear regressions trained with in situ data show good representation of fine mode fraction estimates [ABSTRACT FROM AUTHOR]
Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Aerosol Fine Mode Fraction Retrievals for the Marine Boundary Layer From Airborne Lidar.
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  Data: <searchLink fieldCode="AR" term="%22Saide%2C+Pablo+E%2E%22">Saide, Pablo E.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> saide@atmos.ucla.edu</i><br /><searchLink fieldCode="AR" term="%22Christopoulos%2C+Julianna%22">Christopoulos, Julianna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ferrare%2C+Richard+A%2E%22">Ferrare, Richard A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hair%2C+Johnathan+W%2E%22">Hair, Johnathan W.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shingler%2C+Taylor%22">Shingler, Taylor</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fenn%2C+Marta+A%2E%22">Fenn, Marta A.</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Scarino%2C+Amy+Jo%22">Scarino, Amy Jo</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burton%2C+Sharon+P%2E%22">Burton, Sharon P.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nehrir%2C+Amin+R%2E%22">Nehrir, Amin R.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barton‐Grimley%2C+Rory+A%2E%22">Barton‐Grimley, Rory A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Collister%2C+Brian+L%2E%22">Collister, Brian L.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Moore%2C+Richard+H%2E%22">Moore, Richard H.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ziemba%2C+Luke+D%2E%22">Ziemba, Luke D.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shook%2C+Michael+A%2E%22">Shook, Michael A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schlosser%2C+Joseph%22">Schlosser, Joseph</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Crosbie%2C+Ewan%22">Crosbie, Ewan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Voigt%2C+Christiane%22">Voigt, Christiane</searchLink><relatesTo>6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kirschler%2C+Simon%22">Kirschler, Simon</searchLink><relatesTo>6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22DiGangi%2C+Joshua+P%2E%22">DiGangi, Joshua P.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Diskin%2C+Glenn+S%2E%22">Diskin, Glenn S.</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Atmospheres%22">Journal of Geophysical Research. Atmospheres</searchLink>. 10/28/2025, Vol. 130 Issue 20, p1-17. 17p.
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– Name: Abstract
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  Data: Separating contributions of the fine and coarse modes is important for characterizing aerosols and assessing their impacts. This work develops retrievals of fine mode fraction (FMF) from lidar observables for the marine boundary layer (MBL) using data collected during the ACTIVATE field campaign. First, we calculate multiwavelength backscatter and extinction and derived metrics for spherical particles derived from measured size distributions (combining in situ aerosol and cloud probes) and hygroscopicity estimates. The calculations show reasonable skill when compared to airborne High Spectral Resolution Lidar—generation 2 (HSRL‐2) retrievals, displaying low biases and explaining up to 87% of the variability in backscattering. While slopes are generally close to 1:1 for lidar ratios and Angstrom exponents (AEs), the variability within HSRL‐2 data is only well captured for lidar ratios (50%–67%). Having established that the calculated optical properties are representative of remotely sensed ones in the marine environment, they are used together with in situ aircraft particle size data to train multilinear regression models to estimate FMF proxies (extinction FMF, PM1/PM10 and PM2.5/PM10 ratios). When tested with HSRL‐2 observations as inputs, these models can represent up to 67%–78% of the variability of the observed FMF proxies with biases at high FMFs that depend on the accuracy of the coarse mode aerosol size measurements. The regression retrievals are tested for lidar transects and show expected gradients due to continental influence on the MBL and differential hygroscopicity of fine versus coarse mode aerosol with height. These results are encouraging for their application for various lidar systems. Plain Language Summary: Aerosols are particles suspended in the air and thus one of their characteristics is that they can be of different sizes. In this work, we develop ways to estimate the relative abundance of fine (∼below 1–2.5 μm in diameter) versus coarse particles (defined as fine mode fraction) using active remote sensing (lidar) observations. For that, we utilize field campaign measurements where lidar and in situ measurements from two aircraft were coordinated in time and space so they sample similar air masses. Since the lidar provides retrievals of how aerosols interact with visible light (aerosol optical properties), we first evaluate how a model that uses detailed information of aerosol size and composition from the in situ aircraft can represent these retrievals. After finding reasonable performance, we then calibrate a linear regression model to predict fine mode fraction based on optical property calculations. This new retrieval was then driven by lidar observations and evaluated against in situ fine mode fraction observations finding satisfactory performance. We also found the fine mode fraction retrievals reproduce expected changes happening between the continent and the remote ocean. These retrievals can be used in other lidars with similar characteristics. Key Points: Retrievals for fine mode aerosol fraction proxies are developed using ACTIVATE campaign dataOptical property calculations driven by in situ data show reasonable representation of extinction and backscattering‐based lidar observablesMultilinear regressions trained with in situ data show good representation of fine mode fraction estimates [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Geophysical Research. Atmospheres is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=188926103
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        Value: 10.1029/2025JD044477
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