High-Resolution Self-Calibrating Ground and UAS Based Cloud and Precipitation Sensors

Field measurements of precipitation and cloud microphysical properties are vital for improving the modeling capabilities of climate and environmental researchers. Precipitation measurements, obtained with a ground-based disdrometer, typically include drop size distribution DSD) and drop velocity, while cloud microphysical properties, such as water droplet and ice particle size, ice particle habit, and ice particle density, have been measured with a variety of sensors types. Although fieldable sensors exist to measure both precipitation and cloud microphysical properties, these require regular field calibration, which can reduce the reliability of the data while increasing the sensor down time and operating costs. Two sensors, based on particle shadow velocimetry, are proposed that would reduce or eliminate the need for regular field calibration. The first is a ground-based disdrometer for precipitation measurements, while the second is a cloud sensor capable of deployment on a variety of unmanned aerial platforms, including fixed or rotary-winged unmanned aerial vehicles, tethered balloons, or kites. The fundamental technology has been used in a variety of harsh environments, including in-flight. The proposal would optimize the technology for long-term, remote operation in a variety of climates. During Phase I, two prototypes one the ground-based disdrometer, the other an aerial-platform based cloud sensor) would be developed. Studies and bench tests would be performed to determine the optimal configuration. After construction, additional bench tests would be performed, along with environmental testing to ensure operation in a variety of harsh environments. Finally, field testing would be performed with a tethered balloon. Improving the amount, regularity, and quality of in-situ precipitation data along with cloud water and ice particle size and habit distributions would provide researchers with statistically significant data needed to improve their understanding specifically of mixed-phase clouds, and more generally of the rapid climate change experienced in the Artic [1], [2]. Current systems require regular field calibration, significantly increasing the cost, especially in remote locations. Reducing or eliminating these field calibration requirements will reduce the cost of this data, increasing the amount of data that can be obtained with a given instrument, and improve the data quality. Any investigation into precipitation properties and cloud microphysical properties, including various climate research efforts and even icing on aircraft surfaces, would benefit from the development of the proposed sensor.