Preprints
https://doi.org/10.5194/amt-2022-73
https://doi.org/10.5194/amt-2022-73
 
09 Mar 2022
09 Mar 2022
Status: this preprint is currently under review for the journal AMT.

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

Rachel Robey1 and Julie K. Lundquist2,3 Rachel Robey and Julie K. Lundquist
  • 1Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
  • 3National Renewable Energy Laboratory, Golden, Colorado, USA

Abstract. Wind lidar are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindcubeV2 lidar performing profiling doppler-beam-swinging (DBS) scans in quasi-stationary large-eddy simulations (LES) of convective and stable boundary layers. Error trends vary with the stability regime, time-averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors, driven by the large vertical velocities in convective plumes and exacerbated by the high elevation angle of the scanning beams. The violations of the assumption of horizontal homogeneity due to filtered turbulent velocity variances dominate the error variance, with the vertical velocity variations of particular importance. Range gate weighting contributes little to the variability of the error, but induces an underestimating bias into the horizontal velocity near the surface shear layer. Error in the horizontal wind speed and direction computed from wind components is sensitive to the background wind speed but has negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the u and v velocity estimates, the reported wind speed is subject to a systematic positive bias. Vector time-averaged measurements can improve the behavior of the error distribution with a predictable effectiveness related to the number of decorrelated samples in the time window. The approach in decomposing the error mechanisms with the help of the LES flow field extends to more complex measurement scenarios and scans.

Rachel Robey and Julie K. Lundquist

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on amt-2022-73', Andrew Black, 22 Mar 2022
  • RC1: 'Comment on amt-2022-73', Anonymous Referee #1, 27 Mar 2022
  • RC2: 'Comment on amt-2022-73', Anonymous Referee #2, 11 Apr 2022
  • EC1: 'Additional comments by Andrew H. Black on amt-2022-73', Ulla Wandinger, 28 Apr 2022

Rachel Robey and Julie K. Lundquist

Data sets

Supporting virtual lidar and LES files for "Behavior and Mechanisms of Lidar Error in Varying Stability Regimes" Rachel Robey, Julie K. Lundquist https://doi.org/10.5281/zenodo.6112629

Model code and software

Virtual Lidar Python Rachel Robey https://gitlab.com/raro0632/virtual-lidar

Rachel Robey and Julie K. Lundquist

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Short summary
Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.