UC Santa Cruz

Clouds play a critical role in regulating Earth's climate. Despite their importance, cloud representation in climate models remains a challenge and the response of clouds to warming is a primary factor governing our estimates of climate sensitivity (Bony et al. 2017; Medeiros et al. 2014; Medeiros et al. 2008; Vial et al. 2013; Webb et al. 2006). This is partly because cloud drops can not be represented explicitly so parameterization schemes are used to represent clouds statistically. These schemes are often poorly constrained due to the complexity of cloud microphysical processes and a lack of observational measurements (see Morrison et al. 2020). Detailed and precise measurements of cloud properties are needed to better constrain parameterization schemes and improve our understanding of cloud microphysical processes.

A phase-Doppler interferometer (PDI) is an optical instrument (Bachalo 1980) used to measure individual cloud drop size and velocity from airborne platforms (see Chuang et al. 2008 for development of flight probe). Due to the measurement principle, this instrument overcomes many of the issues facing other existing cloud probes. In order to generate the drop concentrations and integrated quantities needed to better constrain models, the sample volume of a PDI must be accurately characterized. The goal of Chapters 2 and 3 is to develop new methods of characterizing PDI sample volume. In Chapter 2, we characterize probe volume diameter empirically from in situ measurements and present algorithms which account for deviations from theoretical behavior. We show that our methods may be successfully applied to aircraft observation and, using artificially-generated data, show that our methods are accurate to within 1%. In Chapter 3 we characterize probe volume width, which has typically been assumed to be a fixed known value, using laboratory measurements. We show that probe volume width is not a fixed value but depends on drop size. We apply our methods to recently collected aircraft measurements using a dual-range PDI. To our knowledge, this is the first time dual-range PDI flight data has been analyzed to assess agreement between each channel. We show good number concentration agreement in the overlap region between the two channels, providing confidence that our characterization methods are accurate.

Finally, in Chapter 4, we show the versatility of the PDI by introducing a new emerging application in which the instrument is used to measure particulate matter (PM) emissions from drift droplets at two cooling towers. We measure drift droplet emissions by suspended the PDI over each cooling tower and collecting measurements at various locations across each tower. From our measurements, we generate a size distributions representative of total tower emissions and convert these distributions to PM emissions rates. We find that methods outlined by the EPA (EPA 1995) to estimate PM emissions grossly overestimates true emissions. We also find that the majority of drift droplet emissions generate particles that have a diameter <2.5 µm (PM2.5), which are currently unregulated by the EPA. These findings suggest that EPA methods may require revision to both reflect for lower total emissions and account for unregulated PM2.5 emissions.