RAAVEN data processing for TORUS and TORUS-LItE

Abstract. RAAVEN (Robust Autonomous Airborne Vehicle - Endurant and Nimble) uncrewed aircraft systems (UAS) were deployed in and around supercell thunderstorms during the Targeted Observation by Radars and UAS of Supercells (TORUS) and TORUS Left Flank Intensive Experiment (TORUS-LItE) field campaigns. On-board sensors measured temperature, humidity, pressure, and wind. Despite extensive predeployment testing, the demanding environments where data collection occurred presented numerous challenges to data quality. In this article, extensive quality control procedures adopted for these data are described.  Many of these procedures aim to quantify data-quality uncertainty, in lieu of correcting questionable data.  Procedures address the dependency of estimated wind on aircraft manoeuvring, periodically faulty sensors, questionable data induced by sensor wetting in rain, and sensor hysteresis and bias. Bulk data statistics are also presented, in part to assert data quality but also to highlight unique qualities of UAS data collected during TORUS and TORUS-LItE.