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Abstract

In situ observations from research aircraft and in-
strumented ground sites are important contributions to devel-
oping our collective understanding of clouds and are used to
inform and validate numerical weather and climate models.
Unfortunately, biases in these datasets may be present, which
can limit their value. In this paper, we discuss artefacts which
may bias data from a widely used family of instrumentation
in the field of cloud physics, optical array probes (OAPs).
Using laboratory and synthetic datasets, we demonstrate how
greyscale analysis can be used to filter data, constraining the
sample volume of the OAP and improving data quality, par-
ticularly at small sizes where OAP data are considered unre-
liable. We apply the new methodology to ambient data from
two contrasting case studies: one warm cloud and one cirrus
cloud. In both cases the new methodology reduces the con-
centration of small particles (<60 μm) by approximately an
order of magnitude. This significantly improves agreement
with a Mie-scattering spectrometer for the liquid case and
with a holographic imaging probe for the cirrus case. Based
on these results, we make specific recommendations to in-
strument manufacturers, instrument operators and data pro-
cessors about the optimal use of greyscale OAPs. The data
from monoscale OAPs are unreliable and should not be used
for particle diameters below approximately 100 μm.