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

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

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

Snapshot of rainfall and O isotope values during landfall of Superstorm Sandy.

<p>Storm track has landfall6hrs as solid line.</p

Sandy precipitation isotopic composition relative to storm center.

<p>Precipitation (<b>A</b>) O and (<b>C</b>) deuterium-excess with data points positioned relative to the storm center at time of sample collection. (<b>B</b>) Samples are grouped by region as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091117#pone-0091117-g001" target="_blank">Figure 1</a>.</p

Spatial and temporal distribution of Sandy precipitation samples.

<p>(<b>A</b>) Histogram of the temporal distribution of collected precipitation isotope samples with NOAA classification of Sandy development: TS, Tropical Storm; HU, Hurricane; EX, Extra Tropical. (<b>B</b>) Spatial distribution of collected precipitation isotope samples by region (New England, Mid-Atlantic, Midwest, and South) with the Sandy storm track. Average regional precipitation (<b>C</b>) O and (<b>D</b>) -excess throughout the course of the storm color-coded by region.</p

Implications of a shrinking Great Salt Lake for dust on snow deposition in the Wasatch Mountains, UT, as informed by a source to sink case study from the 13–14 April 2017 dust event

The deposition of dust on snow accelerates melt by perturbing snow albedo, directly by darkening the snow surface and indirectly by enhancing snow grain growth. The snow darkening process impacts hydrology by shifting runoff timing and magnitude. Dust on snow deposition has been documented in the Wasatch Mountains, snowmelt from which accounts for up to 80% of surface water supply for Salt Lake City, UT, but the impact on snow melt has not yet been investigated. Here, we present a case study of a dust event observed in the Wasatch (13–14th April, 2017), sampled coincidentally in the air and at the snow surface at an instrumented high elevation site (Atwater Study Plot, Alta, UT). Atmospheric backtrajectory modeling, the results of which were supported by measurements, showed that dust originated predominantly from the west: the Great Salt Lake Desert and the Great Salt Lake (GSL) dry lake bed. The deposited dust mass accounted for ∼50% of the season total dust loading in snow, and daily mean radiative forcing of 20–50 W m ^−2 accelerated snow melt by approximately 25%. This has important implications for The Greatest Snow on Earth ^® , and snow water resources; the water level of the GSL has been declining, exposing dry lake beds, and there are no legal water rights or protections to maintain lake levels or mitigate dust emission