Impact of model resolution on the representation of the wind speed field: An example from the United Kingdom
Bibliographic record
Abstract
Abstract The United Kingdom (UK) is experiencing rapid growth in wind farm development, primarily in the south as well as in offshore regions. As such, reliable long‐term wind statistics are integral in planning future development. Given the scarcity of in situ data, atmospheric reanalyses are beginning to be used for this purpose. However, most reanalyses have resolutions of ∼50 km or lower – scales too coarse to capture many topographic and coastal effects. To address this, dynamical downscaling has been used to obtain higher‐resolution data. However, it is unclear how the downscaling process impacts the temporal and spatial representation of the wind field. Here a set of reanalysis and analysis datasets with a common lineage and with horizontal resolutions ranging from ∼75 to ∼9 km are used to investigate the impact of model resolution on the representation of the spatial and temporal variability in the wind field in the UK. To assess this impact, the decorrelation length and temporal scale – DCLS and DCTS, respectively – are used to characterize spatial and temporal variability. The results can be classified into two categories – resolution‐dependent and resolution‐independent features. Resolution‐dependent results suggest that even resolutions of ∼30 km do not capture the wind field's variability, especially in coastal and mountainous regions. However, resolution‐independent results suggest that there are regions, such as central Ireland, over the oceans, and southeastern England, where the 30 km resolution captures the wind fields' variability. Results also support the historic choice of wind farm development in the UK, namely, the southern/southwestern tip of England as well as off the southern shore.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".