Representativeness of the Precipitation Observing Network for Monitoring Precipitation Change and Variability in Canada
Bibliographic record
Abstract
While there are thousands of precipitation stations in the Canadian climate archive, it has been challenging to estimate regional and national averages of precipitation for the purpose of monitoring climate change and variability, because of evolution of the monitoring network and generally sparse network density, in particular in the North. Changes in the observing network have resulted in segmentation and inhomogeneities in precipitation records. We used monthly precipitation from the Canadian Regional Climate Model (CanRCM4) large ensemble simulations as a proxy of observations with complete spatial and temporal coverage. By comparing results from the complete-coverage dataset with versions masked by observational coverage, we examined the representativeness of two long-term precipitation datasets for the estimation of annual mean precipitation at regional and national levels for the purpose of monitoring precipitation change and variability in Canada. We also analysed the implications of changes in the network and the possible added value of data processing, such as in-filling through spatially interpolating station records. We find that at the best coverage of approximately 450 precipitation stations in the Adjusted and/or Homogenized Canadian Climate Data dataset, station coverage is, in general, adequate for the purpose of monitoring long term precipitation trends. However, this capability is severely compromised if station density changes (reduces) with time or if there is a substantial number of missing values over time. The addition of station records in regions already better represented (i.e. regions with more population) does not provide significant improvement. In-filling over space through spatial interpolation does add value, provided that there is sufficient information in the station network. Our analysis demonstrates the importance of maintaining a consistent long-term network with sufficient station density for the purpose of monitoring climate change and variability.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".