Water Ice and Liquid Water Droplet Aerosol Characterization with Digital Holography

Clouds can consist of liquid water drops, ice particles, or very often a mixture of both. The proportion of water as ice versus water as liquid has a significant impact on how clouds form precipitation and modulate solar radiation. There have been many airborne field campaigns that have sampled cloud particles, but the instrumentation has not been sensitive enough to determine the difference between ice and liquid drops when the ice particle is in a sphere-like shape during the early stages of ice formation. This project will use a new methodology to observe particles on the micrometer scale to determine their phase. This work has implications for numerical weather models and forecasting precipitation-related hazards. The award will also provide opportunities for students in holography, optics, and aerosol sciences, giving them workforce-relevant skills. Digital In-line Holography (DIH) uses a laser beam and an image sensor to observe particles by measuring the interference pattern caused by the particle and using a process called reconstruction to derive an image of the particles. This work will use a new method called Holographic Ice-Droplet Aerosol Characterization (HI-DAC) to differentiate between small ice and liquid water droplets in a way that traditional DIH techniques are not able to. This method applies the idea that the morphological evolution of a droplet as it freezes will encode signatures of the phase change in the hologram’s interference pattern. Specifically, the research team will show how the structure of an optical phenomenon known as the photonic-jet caustic can provide a signature for the ice phase. They will then examine the symmetry of the hologram itself to show further sensitivity to the particle phase. The work includes simulations and laboratory tests. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2534659 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Matthew Berg | Institution: Kansas State University, MANHATTAN, KS | Award Amount: $580,761 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2534659 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2534659.html