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Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters.

Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and L p bounds on the ensemble then give L p convergence.

Wildfire spread prediction models, including even the most sophisticated coupled atmosphere–wildfire models, diverge from observed wildfire progression during multi-day simulations, motivating the need for measurement-based assessments of wildfire state and improved data assimilation techniques. Data assimilation in the context of coupled atmosphere–wildfire models entails estimating wildfire progression history from observations and using this to obtain initial conditions for subsequent simulations through a spin-up process. In this study, an approach is developed for estimating fire progression history from VIIRS active fire measurements, GOES-derived ignition times, and terrain height data. The approach utilizes a conditional Wasserstein Generative Adversarial Network trained on simulations of historic wildfires from the coupled atmosphere–wildfire model WRF-SFIRE, with corresponding measurements for training obtained through the application of an approximate observation operator. Once trained, the cWGAN leverages measurements of real fires and corresponding terrain data to probabilistically generate fire progression estimates that are consistent with the WRF-SFIRE solutions used for training. The approach is validated on five Pacific US wildfires, and results are compared against high-resolution perimeters measured via aircraft, finding an average Sørensen–Dice coefficient of 0.81. The influence of terrain data on fire progression estimates is also assessed, finding an increased contribution when measurements are uninformative.

This paper proposes a recurrent neural network (RNN) model of dead 10 h fuel moisture content (FMC) for real-time forecasting. Weather inputs to the RNN are forecasts from the High-Resolution Rapid Refresh (HRRR), a numerical weather model. Geographic predictors include longitude, latitude, and elevation. Forecast accuracy is estimated in a study that utilizes a spatiotemporal cross-validation scheme. The RNN is trained on HRRR forecasts and observed FMC from weather station sensors within the Rocky Mountain region in 2023, then used to forecast FMC at new locations for all of 2024. The model is evaluated using a 48 h forecast window. The forecasts are compared to observed data from FMC sensors that were not included in training. The accuracy of the RNN is compared to several common baseline methods, including a physics-based ordinary differential equation, an XGBoost machine learning model, and hourly climatology. The RNN shows substantial forecasting accuracy improvements over the baseline methods.

Correctly initializing the fire within coupled fire-atmosphere models is critical for producing accurate forecasts of meteorology near the fire, as well as the fire growth, and plume evolution. Improperly initializing the fire in a coupled fire-atmosphere model can introduce forecast errors that can impact wind circulations surrounding the fire and updrafts along the fire front. A well-constructed fire initialization process must be integrated within coupled fire-atmosphere models to ensure that the atmospheric component of the model does not become numerically unstable due to excessive heat fluxes released during the ignition, and that realistic fire-induced atmospheric circulations are established at the model initialization time. The primary objective of this study is to establish an effective fire initialization method in a coupled fire-atmosphere model, based on the analysis of the impact of the initialization procedure on the model’s ability to resolve fire-atmosphere circulations and fire growth. Here, we test three different fire initialization approaches leveraging the FireFlux II experimental fire, which provides a comprehensive suite of observations of the pyroconvective column, local micrometeorology, and fire characteristics. The two most effective fire initialization methods identified using the FireFlux II case study are then tested on the 380,000-acre Creek Fire, which burned across the central Sierra Nevada mountains during the 2020 Western U.S. wildfire season. For this case study, simulated pyroconvection and fire progression are evaluated using plume top height observations from MISR and airborne fire perimeter data, to assess the effectiveness of different initialization methods in the context of establishing pyroconvection and resolving the fire growth. The analyses of both the experimental fire simulation and the wildfire simulation indicate that the spin-up initialization method based on historical fire progression that masks out inactive fire regions provides the best results in terms of resolving the fire-induced vertical circulation and fire progression.

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM 2 . 5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

Observational data collected during experiments, such as the planned Fire and Smoke Model Evaluation Experiment (FASMEE), are critical for evaluating and transitioning coupled fire-atmosphere models like WRF-SFIRE and WRF-SFIRE-CHEM into operational use. Historical meteorological data, representing typical weather conditions for the anticipated burn locations and times, have been processed to initialize and run a set of simulations representing the planned experimental burns. Based on an analysis of these numerical simulations, this paper provides recommendations on the experimental setup such as size and duration of the burns, and optimal sensor placement. New techniques are developed to initialize coupled fire-atmosphere simulations with weather conditions typical of the planned burn locations and times. The variation and sensitivity analysis of the simulation design to model parameters performed by repeated Latin Hypercube Sampling is used to assess the locations of the sensors. The simulations provide the locations for the measurements that maximize the expected variation of the sensor outputs with varying the model parameters.

Forecasting fire growth, plume rise and smoke impacts on air quality remains a challenging task. Wildland fires dynamically interact with the atmosphere, which can impact fire behavior, plume rises, and smoke dispersion. For understory fires, the fire propagation is driven by winds attenuated by the forest canopy. However, most numerical weather prediction models providing meteorological forcing for fire models are unable to resolve canopy winds. In this study, an improved canopy model parameterization was implemented within a coupled fire-atmosphere model (WRF-SFIRE) to simulate a prescribed burn within a forested plot. Simulations with and without a canopy wind model were generated to determine the sensitivity of fire growth, plume rise, and smoke dispersion to canopy effects on near-surface wind flow. Results presented here found strong linkages between the simulated fire rate of spread, heat release and smoke plume evolution. The standard WRF-SFIRE configuration, which uses a logarithmic interpolation to estimate sub-canopy winds, overestimated wind speeds (by a factor 2), fire growth rates and plume rise heights. WRF-SFIRE simulations that implemented a canopy model based on a non-dimensional wind profile, saw significant improvements in sub-canopy winds, fire growth rates and smoke dispersion when evaluated with observations.

Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) into a time lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of FMC. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time lag fuel moisture model in the coupled weather-fire code WRF–SFIRE on 10-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve on FMC estimates in unobserved fuel classes.

In this study, WRF-Sfire is coupled with WRF-Chem to construct WRFSC, an integrated forecast system for wildfire behaviour and smoke prediction. WRF-Sfire directly predicts wildfire spread, plume and plume-top heights, providing comprehensive meteorology and fire emissions to chemical transport model WRF-Chem, eliminating the need for an external plume-rise model. Evaluation of WRFSC was based on comparisons between available observations of fire perimeter and fire intensity, smoke spread, PM2.5 (particulate matter less than 2.5 μm in diameter), NO and ozone concentrations, and plume-top heights with the results of two WRFSC simulations, a 48-h simulation of the 2007 Witch–Guejito Santa Ana fires and a 96-h WRF-Sfire simulation with passive tracers of the 2012 Barker Canyon fire. The study found overall good agreement between forecast and observed local- and long-range fire spread and smoke transport for the Witch–Guejito fire. However, ozone, PM2.5 and NO concentrations were generally underestimated and peaks mistimed in the simulations. This study found overall good agreement between simulated and observed plume-top heights, with slight underestimation by the simulations. Two promising results were the agreement between plume-top heights for the Barker Canyon fire and faster than real-time execution, making WRFSC a possible operational tool.