Scan strategies for wind profiling with Doppler lidar – An LES-based evaluation

Abstract

Abstract. Lidar scan techniques for wind profiling rely on the assumption of a horizontally homogeneous wind field and stationarity for the duration of the scan. As this condition is mostly violated in reality, detailed knowledge of the resulting measurement error is required. The objective of this study is to quantify and compare the expected error associated with Doppler-lidar wind profiling for different scan strategies and meteorological conditions by performing virtual measurements implemented in a large-eddy simulation (LES) model. Various factors influencing the lidar retrieval error are analyzed through comparison of the wind measured by the virtual lidar with the ‘true’ value generated by the LES. These factors include averaging interval length, zenith angle configuration, scan technique and instrument orientation. For the first time, ensemble simulations are used to determine the statistically expected uncertainty of the lidar error. The analysis reveals a root-mean-square deviation (RMSD) of less than 1 m s−1 for 10 min averages of wind speed measurements in a moderately convective boundary layer, while RMSD exceeds 2 m s−1 in strongly convective conditions. Unlike instrument orientation and scanning scheme, the zenith angle configuration proved to have significant effect on the retrieval error. Horizontal wind speed error is reduced when a larger zenith angle configuration is used, but is increased for measurements of vertical wind. Results suggest that the scan strategy has a relevant effect on the lidar retrieval error and that instrument configuration should be chosen depending on the quantity of interest and the flow conditions in which the measurement is performed.

Behavior and Mechanisms of Doppler Wind Lidar Error in Varying Stability Regimes

“…The quantification of the error aligns with values found in field studies (e.g. Courtney et al (2008)) and in similar estimates of virtual lidar performance in baseline convective conditions (Rahlves et al, 2021). To define the error, we compared virtual measurements against both a pointwise truth reference and the average over the scan volume.…”

“…Only recently has virtual lidar been employed for baseline studies of profiling wind lidar in favorable (flat, uniform, quasi-stationary) conditions. Rahlves et al (2021) used virtual lidar in PALM LES to compare the bulk performance of various scan types (DBS and VAD at varying cone angles) across a suite of convective conditions.…”

“…In this first demonstration of the virtual lidar tool, we consider a specific case of the Leosphere WindcubeV2 profiling lidar (Figure 1) measuring mean winds in ideal simulations of stable and convective conditions over flat terrain. As in Rahlves et al (2021), the configuration allows for a baseline assessment of the lidar performance by omitting external sources of inhomogeneities, like complex terrain or wind turbines, and isolates the system error arising from complex but statistically stationary turbulent boundary layer flow. Depending on the quantity of interest, Rahlves et al (2021) found that the configuration choices (scan type, cone angle, averaging length) have distinct effects on the lidar retrieval error.…”

“…As in Rahlves et al (2021), the configuration allows for a baseline assessment of the lidar performance by omitting external sources of inhomogeneities, like complex terrain or wind turbines, and isolates the system error arising from complex but statistically stationary turbulent boundary layer flow. Depending on the quantity of interest, Rahlves et al (2021) found that the configuration choices (scan type, cone angle, averaging length) have distinct effects on the lidar retrieval error. Additionally, profiling in strongly convective conditions, absent other sources of inhomogeneity, the lidar exhibited markedly larger errors than in more moderate convection.…”

“…Our work extends that study for a single DBS profiling scan to include a range gate weighting in the lidar model, a further stable stability regime, and the disaggregation of the vertical profile heights. The idea of using ensembles to gauge the uncertainty of the error (Rahlves et al, 2021) is expanded to using larger ensembles to characterize an error distribution particular to the flow conditions and scan geometry. Further, we present an analytic treatment of the observation system error to explain the dominant error mechanisms and trends, supporting the findings of the empirical results.…”