Machine learning estimation of fire arrival time from level-2 active fires satellite data

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

Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

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 \u3c3 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 PM2.5 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%

Incorporating a canopy parameterization within a coupled fire-atmosphere model to improve a smoke simulation for a prescribed burn

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

QES-Fire: A dynamically coupled fast-response wildfire model

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model\u27s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire\u27s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data

Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts

Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements to improve fire spread forecasts from numerical models through data assimilation. This work develops a method for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere-wildfire models from a measured wildfire state in a physics-informed approach. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein Generative Adversarial Network (cWGAN), trained with WRF-SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sorensen's coefficient of 0.81 for the fire perimeters and an average ignition time error of 32 minutes suggest that the method is highly accurate

Doctor of Philosophy

dissertationWildfires and wind-blown dust events are expected to increase through the end of the 21st century as a result of warmer temperatures and increasing aridity, which is being driven by climate change. There is a significant need to develop air quality modeling frameworks that can simulate the impacts of fires and dust in the future. Modeling wildfire and dust events is often difficult as both vary significantly in time and space, often requiring sophisticated high-resolution atmospheric transport models that can resolve fine-scale processes. A Lagrangian-based modeling framework was initially developed for Salt Lake City during the summers of 2007 and 2012 to evaluate model performance during two wildfire seasons. The model was able to replicate the timing of enhanced PM2.5 concentrations from wildfires; however, an underestimation was observed, which was attributed to the failure to include a wildfire plume rise parameterization. Additional work was carried out to determine the optimal configuration needed to accurately resolve the wildfire plume rise and the downwind smoke transport for a prescribed burn at Eglin Air Force Base, FL. From this analysis, recommendations were provided for the model configuration needed to accurately simulate the downwind transport of smoke. A separate modeling framework was developed for wind-blown dust, which was applied to the Wasatch Front during the spring of 2010. Final results from this study found that dust model simulations were able to successfully replicate two wind- blown dust episodes on 30 March and 27-28 April 2010. However, significant updates were needed for soil classifications within the model in order to account for dust production across dry lake beds. Simulations were then carried out to estimate the impacts of a desiccating Great Salt Lake on air quality along the Wasatch Front. From these simulations, it was determined that concentrations of harmful particulates increased by a factor of 2 across the Wasatch Front as a result of decreased Great Salt Lake water levels

The shrinking Great Salt Lake contributes to record high dust-on-snow deposition in the Wasatch Mountains during the 2022 snowmelt season

Seasonal snowmelt from the Wasatch Mountains of northern Utah, USA, is a primary control on water availability for the metropolitan Wasatch Front, surrounding agricultural valleys, and the Great Salt Lake (GSL). Prolonged drought, increased evaporation due to warming temperatures, and sustained agricultural and domestic water consumption have caused GSL water levels to reach record low stands in 2021 and 2022, resulting in increased exposure of dry lakebed sediment. When dust emitted from the GSL dry lakebed is deposited on the adjacent Wasatch snowpack, the snow is darkened, and snowmelt is accelerated. Regular observations of dust-on-snow (DOS) began in the Wasatch Mountains in 2009, and the 2022 season was notable for both having the most dust deposition events and the highest snowpack dust concentrations. To understand if record high DOS concentrations were linked to record low GSL levels, dust source regions for each dust event were identified through a backward trajectory model analysis combined with aerosol measurements and field observations. Backward trajectories indicated that the exposed lakebed of the GSL contributed 23% of total dust deposition and had the highest dust emissions per surface area. The other potential primary contributors were the GSL Desert (45%) and the Sevier +Tule dry lakebeds (17%), both with lower per-area emissions. The impact on snowmelt, quantified by mass and energy balance modeling in the presence and absence of snow darkening by dust, was over 2 weeks (17 d) earlier. The impact of dust on snowmelt could have been more dramatic if the spring had been drier, but frequent snowfall buried dust layers, delaying dust-accelerated snowmelt later into the melt season

Optimizing Smoke and Plume Rise Modeling Approaches at Local Scales

Heating from wildfires adds buoyancy to the overlying air, often producing plumes that vertically distribute fire emissions throughout the atmospheric column over the fire. The height of the rising wildfire plume is a complex function of the size of the wildfire, fire heat flux, plume geometry, and atmospheric conditions, which can make simulating plume rises difficult with coarser-scale atmospheric models. To determine the altitude of fire emission injection, several plume rise parameterizations have been developed in an effort estimate the height of the wildfire plume rise. Previous work has indicated the performance of these plume rise parameterizations has generally been mixed when validated against satellite observations. However, it is often difficult to evaluate the performance of plume rise parameterizations due to the significant uncertainties associated with fire input parameters such as fire heat fluxes and area. In order to reduce the uncertainties of fire input parameters, we applied an atmospheric modeling framework with different plume rise parameterizations to a well constrained prescribed burn, as part of the RxCADRE field experiment. Initial results found that the model was unable to reasonably replicate downwind smoke for cases when fire emissions were emitted at the surface and released at the top of the plume. However, when fire emissions were distributed below the plume top following a Gaussian distribution, model results were significantly improved

Wildfire activity is driving summertime air quality degradation across the western US: a model-based attribution to smoke source regions

Over recent decades, wildfire activity across western North America has increased in concert with summertime air quality degradation in western US urban centers. Using a Lagrangian atmospheric modeling framework to simulate smoke transport for almost 20 years, we quantitatively link decadal scale air quality trends with regional wildfire activity. Modeled smoke concentrations correlate well with observed fine-mode aerosol (PM _2.5 ) concentrations (R > 0.8) at the urban centers most impacted by smoke, supporting attribution of observed trends to wildfire sources. Many western US urban centers (23 of 33 total) exhibit statistically significant trends toward enhanced, wildfire-driven, extreme (98th quantile) air quality episodes during the months of August and September for the years 2003–2020. In the most extreme cases, trends in 98th quantile PM _2.5 exceed 2 μ g m ^−3 yr ^−1 , with such large trends clustering in the Pacific Northwest and Northern/Central California. We find that the Pacific Northwest is uniquely impacted by smoke from wildfires in the mountainous Pacific Northwest, California, and British Columbia, leading to especially robust degradation of air quality. Summertime PM _2.5 trends in California and the Intermountain West are largely explained by wildfires in mountainous California and the American Rockies, respectively. These results may inform regional scale forest management efforts, and they present significant implications for understanding the wildfire—air quality connection in the context of climate driven trends toward enhanced wildfire activity and subsequent human exposure to degraded air quality

Transporte transfronterizo de aerosoles emitidos por quema de biomasa y contaminación fotoquímica en la Orinoquia

Los Llanos son un ecosistema de sabana que cubre gran parte de la cuenca del Orinoco, desde el piedemonte de Los Andes colombianos, hasta el Delta del río Orinoco en el Océano Atlántico en Venezuela. Los Llanos experimentan quemas periódicas antropogénicas y naturales durante la temporada seca, la cual es más corta en Colombia. Los vientos alisios del noreste que predominan durante la temporada seca pueden transportar los penachos de quemas desde Venezuela hasta Colombia, incluso durante la temporada de lluvias de los Llanos colombianos. Nuestro análisis se basa en mediciones de aire ambiente en Yopal y Arauca, dos ciudades de los Llanos colombianos separadas 266 km entre sí, durante el periodo abril-mayo de 2015, justo después del final de la temporada seca en Colombia, pero cuando todavía persistía alta actividad de quemas en Venezuela. Las concentraciones promedio de 24 horas de material particulado, PM10, en las dos ciudades fueron inesperadamente altas (hasta 112 µg/m³), particularmente si se considera su tamaño relativamente pequeño y su baja actividad industrial y de tráfico. Las tendencias de las concentraciones de PM10 fueron muy similares, lo cual indica una fuente remota común. También se observaron aumentos de ozono de hasta ~94 ppbv (promedio de siete días). Se encontró una correlación razonable entre PM10 y un proxy de emisiones de fuegos en el footprint calculado usando el modelo Lagrangiano estocástico STILT (Stochastic Time-Inverted Lagrangian Transport model). Actualmente no hay un sistema de vigilancia de calidad de aire permanente en estas ciudades. Nuestros hallazgos implican que ese sistema debe ser establecido y debe incorporar información de sensores remotos de quema de biomasa