Observed Surface Wind Speed Trends Inferred from Homogenized in Situ Data and Reanalysis Datasets
Bibliographic record
Abstract
This paper describes the development of an updated Canadian homogenized monthly mean wind speed dataset, CanHomW mlyV2, for the period 1953–2023 and characterizes observed changes in surface wind speed across Canada. Hourly data from 154 stations in Canada were first quality controlled and adjusted for any non-standard anemometer heights. Then, monthly mean wind speed series were derived and subject to a semi-automated comprehensive data homogenization procedure to identify and diminish non-climatic changes. The procedure uses a combination of station metadata and multiple statistical tests with and without using reference series. The results of the automated procedure were reviewed manually. All of the 154 data series were identified to have one or more non-climatic changes, which were diminished by quantile matching adjustments. Station relocation and/or joining (i.e. joining of different stations’ data records into one data series), and instrument changes/problems were found to be the main causes of non-climatic changes. The homogenized dataset shows weakening winds in a large part of southern Canada (spanning from the southern Prairies to Labrador) and strengthening winds in most other regions, particularly in the area that spans south-central British Columbia to the Rocky Mountains. The weakening winds in the southern Prairies are also seen consistently in the three modern reanalysis datasets (ERA5, OCADA, 20CRv3), while the four datasets show inconsistent trends in most of the other regions. The Canadian wind trends show notable seasonality, as do the agreement/disagreement among the four datasets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".