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. In situ observations from research aircraft and instrumentedground sites are important contributions to developing our collectiveunderstanding of clouds and are used to inform and validate numericalweather and climate models. Unfortunately, biases in these datasets may bepresent, which can limit their value. In this paper, we discuss artefactswhich may bias data from a widely used family of instrumentation in the fieldof cloud physics, optical array probes (OAPs). Using laboratory and syntheticdatasets, we demonstrate how greyscale analysis can be used to filter data,constraining the sample volume of the OAP and improving data quality,particularly at small sizes where OAP data are considered unreliable. Weapply the new methodology to ambient data from two contrasting case studies:one warm cloud and one cirrus cloud. In both cases the new methodologyreduces the concentration of small particles (&lt;60 µm) byapproximately an order of magnitude. This significantly improves agreementwith a Mie-scattering spectrometer for the liquid case and with a holographicimaging probe for the cirrus case. Based on these results, we make specificrecommendations to instrument manufacturers, instrument operators and dataprocessors about the optimal use of greyscale OAPs. The data from monoscaleOAPs are unreliable and should not be used for particle diameters belowapproximately 100 µm.

Revisiting particle sizing using greyscale optical array probes: evaluation using laboratory experiments and synthetic data