Yingli Yu

The aerosol scattering coefficient is an essential parameter for estimating aerosol direct radiative forcing and can be measured by nephelometers. Nephelometers are problematic due to small errors of nonideal Lambetian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model and taking Aurora 3000 as an example, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all three wavelengths and under all of the measured relative humidity conditions, with more than 85 % of the corrected values having less than 2 % error. The correction method obtains a scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.

The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.