Characterization of a Holographic Cloud Particle Imager (HCPI) for Unmanned Aircraft Systems (UASs)

Many current climate models assume a homogeneous and uncorrelated spatial distribution of the particles within clouds. In situ measurements point toward small-scale (mm to m) correlations between particles due to droplet inertia and turbulence, and adjusting climate models to account for the inhomogeneity of clouds would increase the accuracy of climate predictions. The spatial distribution of droplets in a cloud influences radiative transfer, collision and coalescence, and droplet growth by condensation. This work presents the characterization of a prototype holographic cloud particle imager (HCPI) and results from a flight test. The HCPI measures both the 3D spatial distribution and size distribution of cloud particles in the 14 μm to several millimeter size range. The 3D spatial distribution of particles can also help identify artifacts such as particle shattering by the probe. The planned flight test uses a manned aircraft, but the instrument will be reduced in size and weight to fit on an unmanned aircraft system (UAS), such as a TigerShark.

The instrument uses in-line holography, a common technique due to its simplicity and the resolution constraints of currently available imagers, to generate cloud particle holograms. Diffraction theory enables a numerical reconstruction of the particle positions and sizes within the sample volume. With this information, the "patchiness" of the particles relative to a Poisson distribution can be quantified with the pair correlation function. This patchiness effects the optical transport and energy balance, and other important parameters in climate models that include clouds.

The design and characterization of the prototype used on the manned test flight, and the resulting cloud particle holograms and spatial distributions will be presented, along with a discussion of the meaning of the results with respect to the particular clouds that were sampled. The radiative impact of droplet clustering in observed clouds will be estimated with a Monte-Carlo radiative transfer code and the implications for droplet growth processes may be addressed. There will also be a discussion of improvements that could be made for the next UAS-compatible prototype.