This proposed effort addresses the need for accurate and reliable cloud droplet size distribution measurements with a sufficient data rate to characterize spatial variations in cloud formations. Currently used instrumentation for airborne measurements of clouds have been found to have limitations in terms of accuracy for both cloud droplet size and liquid water content (LWC) measurements. In addition, the limited sampling cross-section of these instruments requires unacceptable sampling times to acquire size distributions and thus limit the measurements of spatial variations in the clouds. The phase Doppler interferometry (PDI) method has been independently demonstrated to provide accurate high- resolution measurements of droplet size and velocity. When applied from tethered balloons or drones, the method can also measure turbulence fluctuation droplet response to the microscale turbulence. Development of the phase Doppler method to be able to function with much larger sampling cross sections will provide a much higher data rate, and consequently, size distribution and LWC measurements over a shorter flight path length. However, as with existing instrumentation, PDI is also susceptible to droplet coincidence measurement errors which occurs when more than one droplet is present in the sample volume at the same time. Increasing the sample volume size of the phase Doppler method will exacerbate this problem. Innovative means are described in this proposal to cope with this problem and to generate reliable measurements even under conditions of significant coincident events. The key advantage of the PDI method is that the signals have unique sinusoidal character which can be easily detected in the presence of noise and can be used with higher level signal processing with the discrete Fourier transform (DFT) to reliably detect and measure the frequency and phase of the signals. Under coincident conditions, advanced signal processing using signal amplitude, frequency, phase, and signal-to-noise ratio allow parsing of the individual signals to obtain their size and velocity. Thus, coincident events can be reconciled to provide reliable and accurate measurements of the individual droplet sizes and velocities. Under this proposed effort, the signal parsing method will be tested on a breadboard system and compared to measurements with our standard PDI instruments. In addition to the parsing method to mitigate coincident errors, the instrument will include selectable apertures in the receiver to allow the sample volume size to be adapted to the prevailing cloud droplet number density conditions. Although the cloud measurement instrumentation market is relatively limited, there are numerous other applications for spray measurements in dilute spray environments. Currently, the PDI method is being used to characterize droplets generated by speaking and while sneezing which is of concern for spreading the COVID-19 virus. These droplet clouds are very dilute and require much larger sampling volume to improve the efficiency of the measurements. The PDI instrument is also used for measuring antiviral sprays used for decontamination of building interiors. In the area of dense sprays, the signal parsing approach will be very useful in measuring dense sprays associated with gas turbine and automotive fuel injection systems. Thus, this development will be highly useful in terms of advancing spray measurement instrumentation in multiple industries, several of which are solving environmentally-related human health issues.
