Accounting for spatiotemporally correlated errors in wind speed for remote surveys of methane emissions
Bibliographic record
Abstract
Abstract. Spatiotemporally correlated errors in wind speeds estimated via numerical weather prediction (NWP) models – where estimated wind speeds and associated uncertainties at similar times or at nearby sites are likely to be correlated – are an important, but to date neglected, determinant of overall uncertainties in remote surveys of emissions in the oil and gas and other sectors. In this work, we develop a methodology to model such errors using publicly available, anemometer-measured wind speeds at weather stations within a region of interest. The method is parsed into two: (1) creation of a region-averaged wind speed error model to provide the probability of the true wind speed given an NWP model-estimated wind speed while ignoring autocorrelation, and (2) development of a Gaussian copula model for the region of interest (ROI) to capture spatiotemporal autocorrelation. The combined model for total wind uncertainty, including spatiotemporally autocorrelated errors, is demonstrated using the oil and gas-producing region of northeastern British Columbia, Canada as a case study. We also provide additional combined models for the Canadian provinces of Alberta and Saskatchewan, the U.S. state of North Dakota, and Colombia. Finally, we share a simple python code to interface with these models and to simplify application by others. The combined models show varying correlations in wind speed errors, which are attributable to the variability of terrain in the ROIs and the relative accuracy of different NWP models. Results further reveal how temporal correlations and hence uncertainties in aggregated emissions can be minimized through remote survey design, where waiting at least two days before revisiting a site and phase shifting re-surveys by approximately six hours can avoid both near-field and diurnal patterns in temporal autocorrelation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".