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Scanning Ka-band Doppler radar observations reveal the development and intensification of a counter-rotating vortex pair (CVP) embedded in an advancing fire-front during California’s Dixie Fire in August 2021. The observations show that an initially isolated plume associated with a new spot fire develops flow splitting and a fire-generated inflow wind on the plume’s lee side. This inflow retards the fire progression and enhances the lateral wind shear along the plume flanks. The lateral shear evolves into quasi-symmetric cyclonic and anticyclonic vortices with winds >40 m s⁻¹. This counter rotating vortex pair (CVP) spreads perpendicular to the wind direction, yielding a “y-shaped” fire perimeter, with fire intensity and direction of spread strongly linked to the vortices. Detailed snapshots of the vortices reveal associated radar hook echoes and orbiting sub-vortices of tornado-like intensity. Some vortices remain attached to the fire, while others shed downstream. Additional lidar observations show the structure and development of the fire’s inflow. We discuss the observed vortex evolution in the context of existing conceptualizations for CVPs in wildland fire, including their preferential occurrence on lee slopes and their role in generating lateral fire spread. 

Abstract. Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The results are in good agreement with observations recorded during the event. Extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making and emergency response management during wildfire events. 

Abstract This study investigated the sensitivity of pyrocumulonimbus (PyroCb) induced by the California Creek fire of 2020 to the amount and type of surface fuels, within the WRF‐SFIRE modeling system. Satellite data were used to derive fire arrival times to constrain fire progression, and to augment the fuel characterization with better estimates of combustible vegetation accounting for tree mortality. Machine learning was employed to classify standing dead vegetation from aerial imagery, which was then added as a custom fuel class along with the standard Anderson fuel categories. Simulations using this new fuel class produced a larger and more vigorous PyroCb than the control run, however, still under‐predicted the cloud top. Additional augmentation of fuel mass to represent the accumulation of dead vegetation on the forest floor further improved the simulations, demonstrating the efficacy of representing both dead standing and fallen vegetation to produce more realistic PyroCb and smoke simulations. 

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved. 

Background Accurate simulation of wildfires can benefit pre-ignition mitigation and preparedness, and post-ignition emergency response management. Aims We evaluated the performance of Weather Research and Forecast-Fire (WRF-Fire), a coupled fire-atmosphere wildland fire simulation platform, in simulating a large historic fire (2018 Camp Fire). Methods A baseline model based on a setup typically used for WRF-Fire operational applications is utilised to simulate Camp Fire. Simulation results are compared to high-temporal-resolution fire perimeters derived from NEXRAD observations. The sensitivity of the model to a series of modelling parameters and assumptions governing the simulated wind field are then investigated. Results of WRF-Fire for Camp Fire are compared to FARSITE. Key results Baseline case shows non-negligible discrepancies between the simulated fire and the observations on rate of spread (ROS) and spread direction. Sensitivity analysis results show that refining the atmospheric grid of Camp Fire’s complex terrain improves fire prediction capabilities. Conclusions Sensitivity studies show the importance of refined atmosphere modelling for wildland fire simulation using WRF-Fire in complex terrains. Compared to FARSITE, WRF-Fire agrees better with the observations in terms of fire propagation rate and direction. Implications The findings suggest the need for further investigation of other possible sources of wildfire modelling uncertainties and errors. 

Abstract The social, economic, and ecological impacts of wildfires are increasing over much of the United States and globally, partially due to changing climate and build-up of fuels from past forest management practices. This creates a need to improve coupled fire–atmosphere forecast models. However, model performance is difficult to evaluate due to scarcity of observations for many key fire–atmosphere interactions, including updrafts and plume injection height, plume entrainment processes, fire intensity and rate-of-spread, and plume chemistry. Intensive observations of such fire–atmosphere interactions during active wildfires are rare due to the logistical challenges and scales involved. The California Fire Dynamics Experiment (CalFiDE) was designed to address these observational needs, using Doppler lidar, high-resolution multispectral imaging, and in situ air quality instruments on a NOAA Twin Otter research aircraft, and Doppler lidars, radar, and other instrumentation on multiple ground-based mobile platforms. Five wildfires were studied across northern California and southern Oregon over 16 flight days from 28 August to 25 September 2022, including a breadth of fire stages from large blow-up days to smoldering air quality observations. Missions were designed to optimize the observation of the spatial structure and temporal evolution of each fire from early afternoon until sunset during multiple consecutive days. The coordination of the mobile platforms enabled four-dimensional sampling strategies during CalFiDE that will improve understanding of fire–atmosphere dynamics, aiding in model development and prediction capability. Satellite observations contributed aerosol measurements and regional context. This article summarizes the scientific objectives, platforms and instruments deployed, coordinated sampling strategies, and presents first results. 

Abstract Coupled fire‐atmosphere models often struggle to simulate important fire processes like fire generated flows, deep flaming fronts, extreme updrafts, and stratospheric smoke injection during large wildfires. This study uses the coupled fire‐atmosphere model, WRF‐Fire, to examine the sensitivities of some of these phenomena to the modeled total fuel load and its consumption. Specifically, the 2020 Bear Fire and 2021 Caldor Fire in California's Sierra Nevada are simulated using three fuel loading scenarios (1X, 4X, and 8X LANDFIRE derived surface fuel), while controlling the fire rate of spread using observations. This approach helps isolate the fuel loading and consumption needed to produce fire‐generated winds and plume rise comparable to radar observations of these events. Increasing fuel loads and corresponding fire residence time in WRF‐Fire leads to deep plumes in excess of 10 km, strong vertical velocities of 40–45 m s⁻¹, and combustion fronts several kilometers in width (in the along wind direction). These results indicate that LANDFIRE‐based surface fuel loads in WRF‐Fire likely under‐represent fuel loading, having significant implications for simulating landscape‐scale wildfire processes, associated impacts on spread, and fire‐atmosphere feedbacks. 

Abstract Fire-generated tornadic vortices (FGTVs) linked to deep pyroconvection, including pyrocumulonimbi (pyroCbs), are a potentially deadly, yet poorly understood, wildfire hazard. In this study we use radar and satellite observations to examine three FGTV cases during high-impact wildfires during the 2020 fire season in California. We establish that these FGTVs each exhibit tornado-strength anticyclonic rotation, with rotational velocity as strong as 30 m s −1 (60 kt), vortex depths of up to 4.9 km AGL, and pyroCb plume tops as high as 16 km MSL. These data suggest similarities to EF2+ strength tornadoes. Volumetric renderings of vortex and plume morphology reveal two types of vortices: embedded vortices anchored to the fire and residing within high-reflectivity convective columns and shedding vortices that detach from the fire and move downstream. Time-averaged radar data further show that each case exhibits fire-generated mesoscale flow perturbations characterized by flow splitting around the fire’s updraft and pronounced flow reversal in the updraft’s lee. All the FGTVs occur during deep pyroconvection, including pyroCb, suggesting an important role of both fire and cloud processes. The commonalities in plume and vortex morphology provide the basis for a conceptual model describing when, where, and why these FGTVs form. 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed.