Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO122: A preliminary framework to compute reference-decoupled floating lidar uncertainty
Jose Miguel Garro Fernandez, Data Scientist, EOLOS Floating Lidar Solutions
Abstract
Reliable offshore wind resource assessment requires precise and transparent quantification of measurement uncertainty. Recent reconciliation studies by Environmental Resources Management (ERM), Copenhagen Offshore Partners’ and Fraunhofer IWES show evidence that Floating LiDAR Systems (FLS) can achieve total uncertainties below 1% at hub height. In practice, more conservative values are often applied due to pairwise verification calibration uncertainty, affecting project uncertainty budgets and financial assessments. Building on previous reconciliation analyses, EOLOS proposes a methodology to directly assess FLS measurement uncertainty in a fully traceable, evidence-based framework. Systematic differences between an FLS and a co-located Fixed Reference Lidar (FRL) are quantified using Deming regression, which accounts for measurement errors in both instruments. This approach captures reproducible effects such as optical alignment, mounting geometry, and algorithmic biases. Calibration coefficients, additive bias for constant offsets and multiplicative gain for proportional scaling errors, are obtained from the regression. Associated uncertainties are derived via non-parametric bootstrap resampling of the collocated datasets. After correcting systematic effects, stochastic variability in the FLS measurements is assessed using Triple Collocation Analysis (TCA), comparing the corrected FLS, the FRL, and an external reanalysis product (e.g., ERA5). TCA isolates the error variance unique to each system, capturing contributions from buoy motion, sensor noise, short-term turbulence, and other environmental or operational fluctuations. Iterative refinement ensures residual biases or scaling effects are minimized. Interpolation uncertainty, when wind speeds at hub height are estimated from nearby measurement levels, is quantified using a power-law shear model with bracketing heights, yielding a representative campaign-average value. All uncertainty components are combined in quadrature to obtain total FLS uncertainty at hub height, producing a fully traceable, statistically defensible figure of approximately 1%. This methodology is independent of long-term correlations or spatial adjustments and is presented as a first proposal.
No recording available for this poster.
