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
Joshin Kumar
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Theo Paik
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Nishit J. Shetty
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
Patrick Sheridan
NOAA Global Monitoring Laboratory, Boulder, CO 80305, USA
Center for Aerosol Science and Engineering, Department of Energy,
Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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Total article views: 5,259 (including HTML, PDF, and XML)
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Total article views: 4,436 (including HTML, PDF, and XML)
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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.
Accurate long-term measurement of aerosol light absorption is vital for assessing direct aerosol...
