Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching

Abstract

This study presents a probabilistic surrogate model for localized wildfire spread based on a conditional flow matching algorithm. The approach models fire progression as a stochastic process by learning the conditional distribution of fire arrival times given the current fire state along with environmental and atmospheric inputs. Model inputs include current burned area, near-surface wind components, temperature, relative humidity, terrain height, and fuel category information, all defined on a high-resolution spatial grid. The outputs are samples of arrival time within a three-hour time window, conditioned on the input variables. Training data are generated from coupled atmosphere-wildfire spread simulations using WRF-SFIRE, paired with weather fields from the North American Mesoscale model. The proposed framework enables efficient generation of ensembles of arrival times and explicitly represents uncertainty arising from incomplete knowledge of the fire-atmosphere system and unresolved variables. The model supports localized prediction over subdomains, reducing computational cost relative to physics-based simulators while retaining sensitivity to key drivers of fire spread. Model performance is evaluated against WRF-SFIRE simulations for both single-step (3-hour) and recursive multi-step (24-hour) forecasts. Results demonstrate that the method captures variability in fire evolution and produces accurate ensemble predictions. The framework provides a scalable approach for probabilistic wildfire forecasting and offers a pathway for integrating machine learning models with operational fire prediction systems and data assimilation.

Figures (6)

• Figure 1: Two training samples, with input variables and corresponding fire arrival time outputs displayed. The wind field shown is plotted using the magnitude and direction computed from the zonal and meridional wind components and fuel category data is additionally displayed as a single categorical image determined from the one-hot encoded data used for training.
• Figure 2: Predicted fire area versus (a)$u$ wind speed, (b)$v$ wind speed, (c) relative humidity, (d) temperature, and (e) terrain slope. For each plot, the mean predicted area is shown along with variability indicated by the standard deviation.
• Figure 3: Results for three cases from the testing dataset. The first row shows conditioning inputs, the second row shows six sampled predictions, and the third row presents the target, medoid prediction, pixel-wise standard deviation, and absolute error between the medoid and target.
• Figure 4: Performance of the wildfire spread surrogate model on the testing dataset. Panels (a) and (b) show medoid predicted versus target burned area over the full and restricted ranges, respectively. Panels (c) and (d) show the Sørensen--Dice coefficient (SC) as a function of target burned area over the same ranges.
• Figure 5: Results from recursive application of the wildfire spread surrogate model to predict growth from ignition to 24 hours for four cases. The first row shows the initial fire area, target growth, medoid prediction, standard deviation, and absolute error. The second row shows representative samples of predicted fire growth.