11 Mar 2022
11 Mar 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

Correcting for filter-based aerosol light absorption biases at ARM’s SGP site using Photoacoustic data and Machine Learning

Joshin Kumar1, Theo Paik1, Nishit Shetty1, Patrick Sheridan2, Allison Aiken3, Manvendra Dubey3, and Rajan Chakrabarty1 Joshin Kumar et al.
  • 1Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington 5 University in St. Louis, St. Louis, MO 63130, USA
  • 2NOAA Global Monitoring Laboratory, Boulder, CO 80305, USA
  • 3Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545 USA

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP) that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts which are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). Since previously-developed correction algorithms have a fixed analytical form, fundamentally, they are unable to predict particle phase absorption coefficients with a high degree of accuracy universally: different corrections are required for rural and urban sites across the world. In this study, we analyzed three months of high-resolution ambient data collected using a 3-wavelength photoacoustic spectrometer (PASS) and PSAP on the same inlet; both instruments were operated at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely the Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)-Bond (1999), as well as a Random Forest Regression (RFR) machine learning algorithm to infer particle phase Babs values from PSAP data and estimate their accuracy in comparsion to the refernce Babs values measured synchronously by the PASS. The wavelength averaged Root Mean Square Error (RMSE) values of Babs predicted using the RFR algorithm are improved by an order of magnitude in comparison to those predicted by the Virkulla (2010) and average correction algorithms. A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately two fold errors when compared to the RFR algorithm. To further improve the accuracy of our proposed RFR algorithm, we trained and tested the model on dataset of laboratory-generated combustion aerosols. The RFR model used as inputs the size distribution, uncorrected Tricolor Absorption Photometer (TAP)-measured Babs, and nephelometer-measured scattering coefficient Bscat and predicted particle-phase Babs values within 5% of the reference Babs measured by a PASS. Machine learning approaches offers a promising path to correct for biases in long term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

Joshin Kumar et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-42', Anonymous Referee #1, 08 Apr 2022
  • RC2: 'Comment on amt-2022-42', Anonymous Referee #2, 09 Apr 2022
  • RC3: 'Comment on amt-2022-42', Anonymous Referee #3, 11 Apr 2022

Joshin Kumar et al.

Joshin Kumar et al.


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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 US Department of Energy's long-term climate monitoring SGP site is measured using the Particle Soot Absorption Photometer (PSAP), which suffers from artifacts and biases difficult to quantify. We show that machine learning offers a promising path forward to correct for biases in long-term absorption dataset at the SGP site and similar Class I areas.