WRF-Based High-Resolution Fire Weather Forecasting: Accuracy, Uncertainty, and Applications

Wildfire risk management increasingly relies on high-resolution, operational fire weather forecasts; however, converting numerical model outputs into actionable decision support remains a persistent challenge. This study critically evaluates the Weather Research and Forecasting (WRF) model’s capacity for hourly fire weather prediction, with particular emphasis on forecast accuracy, uncertainty quantification, and software engineering practices pertinent to scientific computing. WRF was configured at convection-permitting resolution to generate short-term forecasts of key fire weather variables like temperature, relative humidity, wind speed, and associated derived fire weather indices. Forecast performance was evaluated against ground-based observations using standardized verification metrics, including bias, root mean square error (RMSE), and correlation. Uncertainty was assessed through error propagation analysis and sensitivity testing. Results indicate strong model skill in reproducing the diurnal cycles and spatial variability of temperature and humidity, whereas wind fields exhibit greater uncertainty during rapidly evolving mesoscale events. Notably, derived fire weather indices demonstrate nonlinear error amplification, underscoring the necessity of robust uncertainty representation within decision-support frameworks. We document reproducible workflows, containerized deployment, and automated verification pipelines that enhance model reliability and operational readiness. The study further demonstrates visualization-driven dissemination of fire risk forecasts, with an emphasis on usability for fire management agencies. This work advances WRF-based fire weather forecasting by integrating rigorous evaluation with contemporary scientific software engineering practices, thereby promoting reproducible, scalable, and trustworthy computational science.