Measurement of Small Ice Particles from Aircraft

The ability to accurately measure cloud particles from aircraft-mounted instruments continues to be a significant challenge. Small ice particles present a particularly difficult and uncertain measurement and large differences have been observed between balloon-borne and aircraft measurements of small ice concentrations. In the research community, there is an increased interest in small ice due to its importance related to radiative transfer, ice nucleation, ice multiplication, and cloud electrification. MMM scientists have been working to improve the measurement of small ice by comparing established airborne particle measurement technologies with newer technologies such as holography. Studies in conditions where small ice is not expected to exist (see figure) show that the new technologies often perform better, but at a significant cost in both computing and personnel effort.

A flight below a cirrus cloud with particles falling from above.  Traditional instruments employ high speed line scanning  technology that results in many out-of-focus particles, leading to reports of high small ice concentration.  Holographic instruments enable instant capture of a well-defined volume where particles can be refocused, resulting in improved artifact detection and lower small ice concentration (right panel).
Figure A: A flight below a cirrus cloud with particles falling from above. Traditional instruments employ high speed line scanning technology that results in many out-of-focus particles, leading to reports of high small ice concentration. Holographic instruments enable instant capture of a well-defined volume where particles can be refocused, resulting in improved artifact detection and lower small ice concentration (right panel).

NCAR scientists across several laboratories have been working together to advance the measurement of small ice particles. This includes improvements to mechanical, electronic, and optical upgrades for instrumentation (EOL), improvements to software and analysis processes (MMM) and improvements in processing speed through machine learning (joint CISL, EOL, and MMM).