Evaluation of geo‐referenced grids of 1961–1990 Canadian temperature and precipitation normals
Bibliographic record
Abstract
Four sets of geo‐referenced grids of 1961–90 normals, or thirty‐year averages, of monthly maximum and minimum temperatures, and total precipitation are compared for the following areas of western Canada: Alberta, Saskatchewan, Manitoba and south‐eastern British Columbia. The landscape varies from lowlands around Hudson Bay in the east and flat plains in the south, to moderate elevation hills and numerous lakes in the north, to a complex terrain of high mountains and deep valleys in the west. Interpolation methods used to create the grids range from a very simple technique ‐ Inverse Distance Weighting (IDW) of closest neighbours, to more sophisticated statistical models ‐ ANUSPLIN (thin plate smoothing splines on geographic location and elevation) and SQUARE‐GRID (multivariate regression on elevation, distance from large water bodies and barriers, etc.), to even more complex hybrid systems ‐ PRISM (statistical regression, combined with physical models and expert knowledge). The main characteristics of each technique are discussed in detail, as they are often very good predictors of the accuracy of the ensuing gridpoint values. Based on the intercomparison of point and average values, as well as the verification of temperature with upper air soundings and precipitation with streamflow measurements, all grids, except SQUARE‐GRID for precipitation, produce very good results in the Prairies’ ecozone. Temperatures in most cases agree within 1°C and precipitation within several percent. PRISM, which was verified to model the Arctic inversion correctly, performs the best in winter in northern Saskatchewan, Manitoba, and Alberta, in particular, over the hills of northern Alberta (PRISM warmer), and most likely over low lying areas of the Nelson River Delta (PRISM colder). PRISM and ANUSPLIN can be recommended for the mountains of south‐eastern British Columbia and southwestern Alberta. Both grids verify well, in both winter and summer, with upper air soundings for maximum temperature and station vertical profiles for minimum temperature. They are remarkably close to water balance estimates of precipitation computed from streamflow gauge measurements ‐ PRISM is slightly high and ANSUPLIN slightly low. Precipitation from IDW and SQUARE‐GRID are not satisfactory in the mountains; both severely underestimate precipitation by as much as 40%. IDW, which does not incorporate any orographic effects, is also too warm in the mountains. As expected, topography, physiography, and monitoring network issues are sources of major discrepancies among the grids. SQUARE‐GRID, besides using far fewer stations and a preprocessed dataset, also produced anomalous values, e.g., values of zero precipitation along the eastern slopes of the Rocky Mountains.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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".