Anemometer-free indoor calibration of Doppler lidar using fiber optics and Monte Carlo simulation
Abstract
Existing methods for calibrating and classifying Doppler lidars for wind energy applications are time-consuming and overestimate lidar measurement uncertainty. These shortcomings are due to the reference instruments: anemometers on meteorological masts. The input signals in field calibrations are real wind conditions, and include uncertainties associated with the met mast and the terrain. This research presents validation of a new method for lidar calibration. The new approach has two steps. First, the uncertainty of the lidar line of sight velocities (LOS) are measured. Second, those intermediate uncertainties are used to derive the uncertainty of the horizontal wind speed using a simulated wind field. LOS uncertainties are assessed using a fiber optic bench as a synthetic wind tunnel called Simulation of the Atmosphere with Fiber Optics Multi-Velocity (SAFO-MV). Using SAFO-MV, the lidar LOS is shifted using an acousto-optic modulator (AOM) and backscattered from a fiber spool, mimicking backscatter from a uniform, low turbulence wind field. The reference signals are SI-traceable via calibration of the AOM's RF-driver. Sources of uncertainty in the bench and the laboratory process are examined and found to be small. Wind speed uncertainties are derived via Monte Carlo uncertainty propagation. Wind fields are simulated using PyConTurb, an implementation of the KSEC turbulence model. A virtual lidar in the simulation replicates lidar sensitivities derived by IEC 61400-50-2 classification campaigns. Replication of device sensitivities and the traceability of the approach comprise a validation of the measurement model, enabling its use for lidar calibration. The proposed method yields uncertainties comparable to IEC lidar calibration, while greatly reducing calibration time. This new technology can streamline lidar deployment and reduce uncertainty in wind resource assessment.
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BibTeX
@misc{black2026anemometerfree,
title = {{Anemometer-free indoor calibration of Doppler lidar using fiber optics and Monte Carlo simulation}},
author = {Andrew Black and Klaus Franke and Davide Trabucci and Pierre E. Allain and Lisa Lopez and Axel Albers},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20851},
eprint = {2609.20851},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20851},
}
RIS
TY - UNPB
TI - Anemometer-free indoor calibration of Doppler lidar using fiber optics and Monte Carlo simulation
AU - Andrew Black
AU - Klaus Franke
AU - Davide Trabucci
AU - Pierre E. Allain
AU - Lisa Lopez
AU - Axel Albers
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.20851
UR - https://arxiv.org/abs/2609.20851
AB - Existing methods for calibrating and classifying Doppler lidars for wind energy applications are time-consuming and overestimate lidar measurement uncertainty. These shortcomings are due to the reference instruments: anemometers on meteorological masts. The input signals in field calibrations are real wind conditions, and include uncertainties associated with the met mast and the terrain. This research presents validation of a new method for lidar calibration. The new approach has two steps. First, the uncertainty of the lidar line of sight velocities (LOS) are measured. Second, those intermediate uncertainties are used to derive the uncertainty of the horizontal wind speed using a simulated wind field. LOS uncertainties are assessed using a fiber optic bench as a synthetic wind tunnel called Simulation of the Atmosphere with Fiber Optics Multi-Velocity (SAFO-MV). Using SAFO-MV, the lidar LOS is shifted using an acousto-optic modulator (AOM) and backscattered from a fiber spool, mimicking backscatter from a uniform, low turbulence wind field. The reference signals are SI-traceable via calibration of the AOM's RF-driver. Sources of uncertainty in the bench and the laboratory process are examined and found to be small. Wind speed uncertainties are derived via Monte Carlo uncertainty propagation. Wind fields are simulated using PyConTurb, an implementation of the KSEC turbulence model. A virtual lidar in the simulation replicates lidar sensitivities derived by IEC 61400-50-2 classification campaigns. Replication of device sensitivities and the traceability of the approach comprise a validation of the measurement model, enabling its use for lidar calibration. The proposed method yields uncertainties comparable to IEC lidar calibration, while greatly reducing calibration time. This new technology can streamline lidar deployment and reduce uncertainty in wind resource assessment.
ER -