WaggleDop: Autonomous event characterization and adaptive scanning using Doppler LiDAR systems

Abstract. Adaptive scanning plays a key role in balancing observational strategies to address diverse scientific objectives while maximizing data collection for weather phenomena of interest. Current adaptive scanning capabilities for pulsed Doppler LiDARs (model Streamline XR), aimed at probing the streamwise velocity of atmospheric boundary layer flows, are limited to predefined scans based on wind directions derived from short-term wind profiles measured by the same instrument. As a result, high data rejection rates characterize the post-campaign phase due to excessive wind direction misalignment. To expand the Streamline LiDAR’s adaptive scanning capabilities, we designed a novel WaggleDop instrument. WaggleDop is an edge computing-enabled system for adaptive scan that supports external inputs and multi-LiDAR configurations via cloud-native infrastructure. In this paper, we test the WaggleDop-LiDAR setup by studying 20 low level jet events and 19 wind reversal events resolved during two U.S. Department of Energy (DOE)-funded field campaigns. We demonstrate that WaggleDop successfully captured these events through adaptive scanning strategies that could not be implemented via classic standalone LiDAR setup, resulting in a substantial enhancement in data availability of up to 90 %. The results shown here demonstrate the importance of more sophisticated scan adaptations to probe complex atmospheric boundary layer flows.