Comparative Analysis of Ensemble and Deterministic Models for Fire Weather Index (FWI) System Forecasting
Bibliographic record
Abstract
Abstract Accurate fire weather forecasting is essential for effective wildfire management, particularly in regions increasingly affected by extreme fire activity such as British Columbia and Alberta, Canada. This study evaluates the predictive performance of three ensemble forecasting systems—the Ensemble Prediction System (ENS), the Global Ensemble Forecast System (GEFS), and the Canadian Global Ensemble Prediction System (GEPS)—and one deterministic model (High Resolution Forecast, HRES)—in forecasting components of the Canadian fire weather index (FWI) system with 1–15 days lead time during the 2021–23 wildfire seasons. Using ERA5 reanalysis as reference datasets, forecast skill was assessed using mean absolute error (MAE), continuous ranked probability score (CRPS), and precision-recall area under the curve (PR-AUC) metrics. Results show that ENS consistently demonstrates superior performance across all FWI components and weather inputs, with lower MAE and CRPS values across all the forecast lead times. A super ensemble combining all ensemble members from ENS, GEFS, and GEPS further improves long-range forecast reliability. Although deterministic forecasts outperform individual ensemble members, they are generally surpassed by ensemble-mean and ensemble-median forecasts at lead times greater than 5 days. The skill of deterministic forecasts also declines more rapidly with lead time and fails to quantify forecast uncertainty, despite their higher spatial resolution. These findings highlight the operational benefits of incorporating ensemble forecasts into fire management decision-making. This study also emphasizes the importance of overwintering adjustments and ensemble size in forecast skill and provides insights for improving fire weather prediction systems. Significance Statement Accurate fire danger forecasts support timely wildfire response and planning. This study evaluates the performance of three leading ensemble weather forecasting systems in predicting fire weather conditions across western Canada. It also compares the ensemble forecasts with the deterministic forecasts, the latter being more commonly used in operational fire management. The results show that ensemble-based fire weather forecasts can provide more reliable predictions, especially under high-risk conditions. By highlighting the strengths of ensemble systems, this work supports improvements in fire weather forecasting practices and helps inform operational decision-making in wildfire management.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".