On the parametrization of optical particle counter response including instrument-induced broadening of size spectra and a self-consistent evaluation of calibration measurements
- 1Ludwig Maximilian University (LMU), Meteorological Institute, Munich, Germany
- 2German Aerospace Center (DLR), Institute of Atmospheric Physics, Oberpfaffenhofen, Germany
- 3University of Vienna (UNIVIE), Aerosol Physics and Environmental Physics, Vienna, Austria
Abstract. Optical particle counters (OPCs) are common tools for the in situ measurement of aerosol particle number size distributions. As the actual quantity measured by OPCs is the intensity of light scattered by individual particles, it is necessary to translate the distribution of detected scattering signals into the desired information, i.e., the distribution of particle sizes. A crucial part in this challenge is the modeling of OPC response and the calibration of the instrument – in other words, establishing the relation between instrument-specific particle scattering cross-section and measured signal amplitude. To date, existing methods lack a comprehensive parametrization of OPC response, particularly regarding the instrument-induced broadening of signal amplitude distributions. This deficiency can lead to significant size distribution biases. We introduce an advanced OPC response model including a simple parametrization of the broadening effect and a self-consistent way to evaluate calibration measurements using a Markov chain Monte Carlo (MCMC) method. We further outline how to consistently derive particle number size distributions with realistic uncertainty estimates within this new framework. Based on measurements of particle standards for two OPCs, the Grimm model 1.129 (SkyOPC) and the DMT Passive Cavity Aerosol Spectrometer Probe (PCASP), we demonstrate that residuals between measured and modeled response can be substantially reduced when using the new approach instead of existing methods. More importantly, for the investigated set of measurements only the new approach yields results that conform with the true size distributions within the range of model uncertainty. The presented innovations will help improving the accuracy of OPC-derived size distributions and the assessment of their precision.