Limitations and breakdown of Monin–Obukhov similarity theory for wind profile extrapolation under stable stratification
Bibliographic record
Abstract
Abstract The intent of this study is to investigate the limitations of the Monin–Obukhov similarity theory (MOST) for wind profile extrapolation—particularly its breakdown in stable stratification—and to explore several modifications intended to circumvent aspects of this breakdown. Using 10years of 10min averaged data from the 213m Cabauw meteorological tower in the Netherlands, we first demonstrate the sensitivity of the logarithmic wind speed model to highly uncertain estimates of the roughness length, z0, and the associated limitations of applying the model in horizontally inhomogeneous conditions. We then demonstrate that these limitations can be mitigated by avoiding the use of z0 in the logarithmic wind speed model. Rather, by using a lower boundary above z0 (e.g. 10m) and a ‘bulk’ Obukhov length measured between two near‐surface altitudes, substantial improvements in wind speed extrapolation accuracy are found. Next, we demonstrate the limitations in applying the logarithmic wind speed model above the surface layer (SL), specifically the divergence of different forms of the MOST stability function, the role of the Coriolis force and the decoupling of surface winds from those aloft. Finally, we explore similarity‐based modifications to the logarithmic wind speed model that are intended to improve its accuracy above the SL, but we find that such modifications cannot circumvent the limitations described earlier. Given that modern hub heights and altitudes swept out by a wind turbine blade extend well beyond the range of applicability of MOST under conditions of stable stratification, new extrapolation models are required that are more applicable at these altitudes. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".