Evaluation of Gridded Precipitation Data and Interpolation Methods for Forest Fire Danger Rating in Alberta, Canada
Bibliographic record
Abstract
Abstract The Canadian Forest Fire Weather Index System is the primary measurement of wildfire danger in Canada. Interpolating daily precipitation, one of the inputs for the Fire Weather Index System is a key challenge in areas without sufficient weather stations. This work evaluates the performance of gridded precipitation from the Canadian Precipitation Analysis (CaPA) System and six interpolation methods to achieve the best fire danger rating in Alberta, Canada. Results show that the CaPA System has only average performance due to limited radar coverage (10%) in the forested region; however, using the CaPA System as a covariate with regression kriging generates significantly better precipitation estimates. Ordinary kriging, regression kriging with elevation as a covariate, and the thin‐plate smoothed spline are methods with similar performance. Fuel moisture codes of the Fire Weather Index System respond differently to precipitation amounts due to differences in their time constants for drying. Fine fuels with a short drying time (Fine Fuel Moisture Code) are best estimated by the CaPA System because of its enhanced skill in estimating small precipitation events. Compacted organic fuels with longer drying times (Duff Moisture Code and Drought Code) are best estimated by regression kriging with CaPA because it better predicts significant precipitation events. The dense fire weather station network in our study area (~3.0 stations/10,000 km2) allows us to perform a sensitivity analysis, and we find that a threshold of >0.5 stations/10,000 km2is needed for regression kriging with CaPA to become appreciably better than the CaPA System.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".