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%

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

An Analysis of Fire-Induced Conditions During Large Wildfires and Within Steep Canyons

For this study, fire-induced winds from a wind-driven fire (Thomas Fire) and a plume dominated fire (Creek Fire) were analyzed. Then, the small-scale fire-induced circulations within steep canyons were examined. This study used two different WRF-SFIRE simulations, one without the fire present, and the other with fire. The fire-induced conditions were calculated by subtracting a given variable from the “No Fire Run” from the “Fire Run” (Fire - No Fire). Wind speed and geopotential height fields were analyzed to assess spatial and temporal variability. Furthermore, cloud water mixing ratio, precipitation, and fuel moisture were analyzed for the Creek Fire to assess fire-induced rainfall. When analyzing steep canyons, wind speed, temperature, and pressure were analyzed under two different wind profiles, one at a constant 5 m/s, and another with increasing speed and wind shear with height. It was found that the Thomas Fire generally produced much stronger winds than the Creek Fire. The Creek Fire wind speeds followed the diurnal cycle while the Thomas Fire did not. Additionally, intense geopotential height perturbations over the Creek Fire were due to thunderstorms which reduced fire rate of spread. In terms of steep canyons, many observations were found to be like the Creek Fire. In addition, the higher canyons produced more intense fire-induced circulations, particularly with pressure and wind speed. Furthermore, fire rate of spread increased rapidly in steeper canyons upon reaching canyon walls and became explosive in nature

Wildland Fire Smoke in the United States

This open access book synthesizes current information on wildland fire smoke in the United States, providing a scientific foundation for addressing the production of smoke from wildland fires. This will be increasingly critical as smoke exposure and degraded air quality are expected to increase in extent and severity in a warmer climate. Accurate smoke information is a foundation for helping individuals and communities to effectively mitigate potential smoke impacts from wildfires and prescribed fires. The book documents our current understanding of smoke science for (1) primary physical, chemical, and biological issues related to wildfire and prescribed fire, (2) key social issues, including human health and economic impacts, and (3) current and anticipated management and regulatory issues. Each chapter provides a summary of priorities for future research that provide a roadmap for developing scientific information that can improve smoke and fire management over the next decade

Numerical Modeling of Firebrand Transport

Firebrand showers are the fastest and most complex form of wildfire spread by generating spot fires in random locations. This randomness is due to many factors, including turbulent wind and particle shape. This work seeks to understand how small-scale turbulence affects firebrand landing distribution and develop a methodology to couple firebrand transport with wildfire simulations. Understanding transport at small scales can provide knowledge on large-scale transport in wildfire simulations. The computational domain and the mesh size in wildfire simulations are very large and do not feature small-scale turbulence. High-resolution small-scale turbulent and uniform boundary layers at various turbulence intensities are used for testing plate and rod firebrand transport. Plates and rods were found to have higher travel distances in uniform flows. Plates were also found to be more sensitive to changes in turbulence intensity. Rods were found to have a high concentration of depositions in a small area, while plates had a wide range of depositions. The shape of the firebrand was found to be an important factor in where it will land due to small-scale turbulence. The firebrand transport solver was coupled with WRF-SFIRE for large-scale transport in a high-wind-speed prescribed fire and a low-wind-speed wildfire. Both plates and rods were found to travel farther distances in the low-wind-speed fire due to higher lofting by the fire plume. This work provides the first steps toward improved wildfire simulations with firebrand transport of plates and rods. Further development can help the wildfire science community better understand wildfire spread through spot fire generation by firebrands

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

Improving the Representation of Fresh Wildfire Smoke Plumes in Air Quality Forecasts

Wildfires are increasing in size and frequency in the Western US due to a complex interplay between climate change and landscape-scale fire exclusion practices. The smoke from these fires is degrading air quality across much of the Continental US. Chemical transport models are vital for warning the public about smoky periods, but uncertainties related to fresh smoke plumes can propagate through these models and cause errors in the resulting air quality forecasts.We address model uncertainty related to smoke plume vertical extent and total emissions. First, we use aircraft observations obtained during the 2019 Western US wildfires (FIREX-AQ) to evaluate and constrain a commonly used smoke plume rise parameterization in two smoke models (WRF-Chem and HRRR-Smoke). Observations show that free tropospheric smoke layers occur in 35% of observed plumes and up to 95% of modeled plumes. False free tropospheric smoke injections were primarily associated with models overestimating fire heat flux by up to a factor of 25. Next, we present data-driven methods for predicting day-to-day changes in smoke emissions. Our top-performing model (random forest) explains 48% of the variance in observed daily emissions and outperforms the current operational assumption that emissions will remain constant over a forecast period (persistence, R2=0.02). This model primarily relies on fire weather data to inform its predictions. Finally, we show preliminary results from WRF-Chem simulations which include random forest-derived emissions and updated heat flux values. We find that in the vicinity of large wildfires in 2020 under less severe fire weather, the random forest-derived emissions can produce better predictions of aerosol optical depth (AOD) and fine particulate matter (PM2.5) than the persistence fire emissions. However, in most cases, persistence and random forest-derived emissions yield very similar AOD and PM2.5 predictions, and that the random forest-derived emissions can both improve and degrade AOD and PM2.5 forecasts. Overall, this work demonstrates the utility of incorporating fire observations to quantify and address uncertainties in our state-of-the-art air quality modeling systems

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018