Comparative Analysis of Ensemble and Deterministic Models for Fire Weather Index (FWI) System Forecasting

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 2021–2023 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 five 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.