Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 15
Articles | Volume 15, issue 15
https://doi.org/10.5194/amt-15-4569-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4569-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 15
Research article
 | 
12 Aug 2022
Research article |  | 12 Aug 2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Joshin Kumar, Theo Paik, Nishit J. Shetty, Patrick Sheridan, Allison C. Aiken, Manvendra K. Dubey, and Rajan K. Chakrabarty

Data sets

Photoacoustic Soot Spectrometer (AOSPASS3W). 2015-06-27 to 2015-09-25 Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1190011

ACSM, corrected for composition-dependent collection efficiency (ACSMCDCE). 2015-06-27 to 2015-09-25 Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1763029

Particle Soot Absorption Photometer (AOSPSAP3W). 2015-06-27 to 2017-09-25 Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1333829

Nephelometer (AOSNEPHDRY). 2015-06-27 to 2015-09-25 Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1258791

Field Campaign Data: Semi-Continuous OCEC SGP 2013 Robert Cary https://adc.arm.gov/discovery/#/results/id::6561_ocec_microchem_scocec_aerosol_blkcarbonconc?showDetails=true

Wood and Kerosene Burn Dataset T. Paik, N. Shetty, and J. Kumar https://github.com/joshinkumar/Filter-correction-ML-code/blob/main/Lab%20Burn%20Dataset.zip

Model code and software

joshinkumar/Filter-correction-ML-code: Correcting for filter-based aerosol light absorption biases at Atmospheric Radiation Measurement's Southern Great Plains site using photoacoustic data and machine learning (v1.0) Joshin Kumar https://doi.org/10.5281/zenodo.6835036

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Short summary
Accurate long-term measurement of aerosol light absorption is vital for assessing direct aerosol radiative forcing. Light absorption by aerosols at the US Department of Energy long-term climate monitoring SGP site is measured using the Particle Soot Absorption Photometer (PSAP), which suffers from artifacts and biases difficult to quantify. Machine learning offers a promising path forward to correct for biases in the long-term absorption dataset at the SGP site and similar Class-I areas.
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