Measurement campaigns with terrestrial LiDAR inevitably accumulate periods without valid data. Intense rain, dense fog, concentration of aerosols below the detection threshold or energy failures interrupt the collection. The question that defines the quality of a campaign is not whether there will be gaps — there will be — but how much gap is tolerable, how to classify it and what to communicate to the lender.
This note presents the applicable regulatory criteria, the most common causes of gaps in LiDAR and the documented approach that Renewene applies in technical reports for due diligence and bankability.
The availability criterion in IEC 61400-12-1
The IEC 61400-12-1, which deals with the measurement of wind turbine performance by means of power curves, is the most cited reference for data availability in wind campaigns. The standard establishes that a measurement period is valid when data availability exceeds 80% for the period of analysis considered.
In practice, industry and lenders adopt more conservative thresholds. The consensus for resource characterization campaigns — where the objective is the estimate of AEP and not just the power curve — points to:
- ≥90% For the validity of one month of data (desirable limit);
- ≥85% – as an acceptable minimum threshold for individual months included in the analysis;
- Months with availability less than 85% are usually excluded from the database used for statistical characterization.
Attention: The IEC 61400-12-2, which deals specifically with the evaluation of wind resources with remote instrumentation (including LiDAR), refines these criteria and requires that the technical report explicitly document the excluded periods and the method of treatment. The absence of this documentation is one of the main reasons why reports are questioned in due diligence processes.
| Height | Jan | Feb | – Sea | Apr | . May | Jun | Jul | Aug. | Set | Out | Nov | Ten |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| - 40 m | 97% | 94% | 96% | 98% | 95% | 93% | 88% | 91% | 96% | 94% | 97% | 95% |
| - 60 m | 95% | 92% | 94% | 97% | 93% | 91% | 86% | 90% | 95% | 93% | 96% | 94% |
| - 80 m | 93% | 90% | 92% | 95% | 91% | 88% | 72% | 87% | 93% | 91% | 94% | 92% |
| 100 m | 91% | 88% | 90% | 93% | 89% | 85% | 64% | 84% | 91% | 89% | 92% | 90% |
| - 120 m | 88% | 85% | 87% | 91% | 86% | 81% | 53% | 79% | 88% | 87% | 90% | 87% |
July presents progressive degradation with altitude — characteristic pattern of low aerosol gaps, correlated with intense winds in the period.
Causes of gaps in LiDAR: operational classification
Not all gaps are of the same nature — and the distinction matters for the evaluation of the representativeness of the sample.
Meteorological Gaps
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Low-aerosol gaps
Conditions of very clean atmosphere (low concentration of aerosols, common in days with strong wind after cold fronts) reduce backscatter and compromise signal quality at heights above 80–100m. These gaps are Related to windThis is the most problematic gap from the point of view of representativeness.
Operational Gaps
Energy failures, firmware reboots, maintenance and communication problems generate discrete and generally short gaps. They are random and do not introduce biases, but their frequency and duration need to be documented to show that the equipment operated within the manufacturer's specifications.
July concentrates disproportionally wide aerosol gap — period of strong winds associated with frontal passage. The gap × velocity correlation indicates potential bias if the month is included without correction.
Treatment of gaps: when including, when excluding
The decision to include or exclude a gap period depends on three factors:
- Duration and concentration: Short and randomly distributed gaps throughout the month have minimal impact. A gap of 5 consecutive days in January can be more problematic than 15% of unavailability sprayed throughout the month.
- Correspondence with weather conditions: The gaps correlated with specific wind regimes (especially the low aerosol problem) require careful analysis and, in some cases, correction or exclusion of the period.
- Impact on Weibull distribution: The definitive metric is to verify whether the k and c parameters of the Weibull distribution change significantly when the gap periods are excluded versus when they are maintained with filling. A variation greater than 2–3% in the parameters is a sign of attention.
The exclusion of low-solar months shifts the distribution to the right — a difference of 0.7 m/s at the average speed. In the estimate of AEP of a typical turbine, this difference corresponds to 8–12% of energy.
Documentation for bankability
A technical report which will be submitted to an independent lender or certifier shall at least:
- Monthly table of availability with percentage values per measurement height;
- Classification of the cause of each excluded period (meteorological / aerosol / operational);
- sensitivity analysis showing the impact of the exclusion of months with low availability on the parameters Weibull and the estimate of AEP;
- explicit statement on the methodology adopted for the treatment of gaps, with reference to the applicable standard.
Renewene documents availability by measurement height (not only the overall availability of the equipment) in the BORA-generated QA Score. A sensor can report 92% of overall availability while the cube height of the turbine of interest presents only 78% — a relevant distinction for the validity of the analysis.
Final considerations
Data availability is often treated as a binary number: above the threshold, approved; below, failed. The reality is more nuanced. What matters for the quality of a resource analysis is not only the percentage of availability, but the temporal distribution of gaps, its correlation with weather conditions and the measurable impact on the resource's statistical parameters.
Reports that treat availability with documented accuracy — even when below ideal — are more robust in due diligence processes than reports that simply report a number above 90% without evidence of the calculation method.
. Policy references
- IEC 61400-12-1:2017 — Wind energy generation systems — Part 12-1: Power performance measurements of electricity producing wind turbines. IEC, 2017.
- IEC 61400-12-2:2022 — Wind energy generation systems — Part 12-2: Power performance of electricity-producing wind turbines based on nacelle anemometry. IEC, 2022.
- BIPM/JCGM 100:2008 — Evaluation of measurement data — Guide to the expression of uncertainty in measurement (GUM). Joint Committee for Guides in Metrology, 2008.
- Floors, R. et al. — The effect of atmospheric boundary layer structure on wind handle data availability. Atmosphere Measurement Techniques, 2021.