Stochastic modelling of daily precipitation for Canada
Bibliographic record
Abstract
This study assesses the performance of various stochastic models in generating daily precipitation amounts and the durations of wet and dry spells across Canada for the period 1971–2000. The models are tested at 657 stations representing the wide range of climate variability across the country. It is found that the simple firstorder Markov chain (SMC) model is capable of reproducing the statistics of dry and wet spell durations reasonably well. However, the SMC model also yields a substantial over‐dispersion problem, resulting in a considerable reduction of interannual variability in monthly total precipitation. This is mainly attributable to smaller variability in the frequency of wet days. This inadequacy is improved by adding a separate model to simulate the number of wet days in a year. The modification to the SMC model has an advantage over alternative approaches using higher order Markov chains since it requires the estimation of fewer parameters. The generation of daily precipitation amounts is tested using the exponential, gamma, skewed normal, and mixed exponential probability distributions, with one to three parameters. Results indicate that the mixed exponential distribution is superior in general, especially during warmer months, while the gamma distribution is adequate for winter months.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".