Comparison of Gridding Methods for Precipitation Over Canada and Assessment of Station and Data Density Effects on Gridding Results
Bibliographic record
Abstract
This study compares the results from applying different gridding methods to different precipitation variables, including total and normal monthly precipitation amounts as well as anomalies and relative anomalies of monthly precipitation for representation of precipitation climate and regional mean precipitation trends. We applied three gridding models, including Optimal Interpolation (OI), currently used to produce the Canadian Gridded (CanGRD) data, Thin-Plate Smoothing Splines, and ordinary kriging. Two observation-based precipitation source datasets were used to derive gridded benchmark and pseudo-observational datasets, with the latter being sampled at full and subsets of a typical long-term precipitation station network in Canada to be then gridded and evaluated against the corresponding benchmark. The results show that the best regional mean precipitation trend estimates are obtained through gridded total precipitation data generated from combined grids of separately kriged relative precipitation anomalies and normal precipitation amounts. This scheme was then used to assess the impact of different station and data densities on the gridded data. The results also indicate that CanGRD-OI is the least accurate model in representing the precipitation climate and trends, and the CanGRD-based trend estimations notably overestimate the trend of regional mean precipitation amounts in the North while underestimating it in the South.
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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 | 0.001 | 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".