Discontinuities due to Joining Precipitation Station Observations in Canada
Bibliographic record
Abstract
Abstract When a climatological station is relocated or is closing, it is often possible to join the climate observations of a nearby site to create a longer time series. However, joining climate observations can sometimes introduce artificial discontinuity that affects the trend. A procedure to detect discontinuities at the joining dates for precipitation station observations is described. It is based on standardized ratios between a tested station and a neighbor, and the t test is used to determine whether the means before and after the joining dates are statistically significantly different. The procedure is applied to 234 climatological stations across Canada to identify steps in rainfall and snowfall. The results indicate that joining precipitation station observations creates steps of different magnitude for rain and snow. It is concluded that about 35% of the stations need adjustment for rain whereas 58% of the stations need adjustment for snow. The magnitude of the adjustments varies from 0.75 to 1.25 for rain and from 0.65 to 1.60 for snow. The annual and seasonal trends before and after adjustments are also examined for 1930–2007. The results show that the trends computed from the adjusted data present a more consistent regional pattern than do trends computed from the unadjusted observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".