Characterizing Uncertainty in Boundary-Layer Wind Measurements from UWOW, a Ground-Based Mobile Doppler Lidar

Mobile Doppler lidar systems provide new opportunities to observe spatially heterogeneous boundary-layer flows, but introduce additional uncertainties related to platform motion, scan geometry, and data quality. In this contribution, we present UWOW (University of Virginia Wind Observatory on Wheels), a mobile Doppler lidar system designed for boundary layer wind profiling, and assess its performance using both field observations and controlled numerical simulations.

UWOW integrates a HALO Photonics StreamLine XR Doppler lidar with a GPS and a VectorNav inertial navigation system (INS) mounted on a mobile trailer. The system performs scanning Doppler lidar measurements while in motion, enabling wind profile retrievals from approximately 100 to 3000 m above ground with ~30 m vertical resolution. Wind speed and direction are derived by combining radial velocity measurements with platform motion and attitude information (heading, pitch, and roll) from the INS through a multi-beam retrieval approach.

We first evaluate UWOW performance using observations collected during the Sundowner Wind Experiment near Santa Barbara, California. These data are used to assess the impact of platform motion, attitude corrections, scan geometry, and signal-to-noise ratio (SNR) on retrieved wind profiles. In particular, we examine how SNR-based filtering can reduce outliers and improve the robustness of the retrieval under real-world conditions.

To further quantify uncertainties, we conduct controlled experiments using output from the Weather Research and Forecasting (WRF) model. Synthetic Doppler lidar observations are generated by sampling WRF wind fields along realistic UWOW trajectories and scan patterns, while prescribing representative platform speeds and attitude variations. Applying the same retrieval algorithms to these synthetic datasets enables direct comparison with the known model wind fields and provides a quantitative assessment of retrieval errors.

This combined observational and modeling framework provides a systematic characterization of UWOW measurement uncertainty and highlights the sensitivity of retrieved winds to platform motion and data filtering choices. The results demonstrate the capability of mobile Doppler lidar systems to resolve boundary layer wind variability in complex terrain, while identifying key factors required for accurate and reliable wind measurements.