Homogeneity analysis of wind data from 213 m high Cabauw tower
Bibliographic record
Abstract
ABSTRACT Homogeneous meteorological data are a prerequisite for reliable climatological studies. This paper investigates the homogeneity of wind data from 213 m high Cabauw tower located in The Netherlands. The wind measurements are conducted at 10, 20, 40, 80, 140 and 200 m above ground. The analysed data cover the period from February 1986 to January 1997 and from April 2000 to December 2015. This study presents the first homogeneity analysis of wind data from a tall meteorological mast. Homogeneities of wind speed and wind direction series were investigated independently using the ReDistribution Method. Overall, the wind measurements at Cabauw tower are very homogeneous. The only wind speed inhomogeneity was detected at 200 m above ground and it seems to be, at least to a certain extent, caused by the rapid expansion of the town of Lopik in the 1990s. Lopik's growth to the west, however, only influenced the east winds on the Cabauw tower. Small inhomogeneities in wind direction data were detected at 20, 40 and 80 m levels, whereas a fairly large inhomogeneity was observed at 10 m above ground. Several potential causes of inhomogeneities in wind direction data are discussed, but the major contributor could not be determined with certainty. In addition, the homogeneity of real measurements from Cabauw tower is compared against the synthetically created wind data for Cabauw tower using the Monte‐Carlo method of random sampling. The results show that the detected anomalies are not due to the random noise in the time series.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".