Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7909-2021
https://doi.org/10.5194/amt-14-7909-2021
Research article
 | 
21 Dec 2021
Research article |  | 21 Dec 2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Markus D. Petters

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Cited articles

Agarwal, S., Mierle, K., and Others: Ceres Solver, available at: http://ceres-solver.org, 2020. a
Atwood, S. A., Kreidenweis, S. M., DeMott, P. J., Petters, M. D., Cornwell, G. C., Martin, A. C., and Moore, K. A.: Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model, Atmos. Chem. Phys., 19, 6931–6947, https://doi.org/10.5194/acp-19-6931-2019, 2019. a, b, c
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Short summary
Inverse methods infer physical properties from a measured instrument response. Measurement noise often interferes with the inversion. This work presents a general, domain-independent, accessible, and computationally efficient software implementation of a common class of statistical inversion methods. In addition, a new method to invert data from humidified tandem differential mobility analyzers is introduced. Results show that the approach is suitable for inversion of large-scale datasets.