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Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper's results will be used to prevent future active fire occurrences in the CC. [ABSTRACT FROM AUTHOR]

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy. [ABSTRACT FROM AUTHOR]

Background: Fire danger rating systems are used daily across Australia to support fire management operations and communications to the general public regarding potential fire danger. Aims: In this paper, we introduce the Australian Fire Danger Rating System (AFDRS), providing a short historical account of fire danger rating in Australia as well as the requirements for an improved forecast system. Methods: The AFDRS combines nationally consistent, spatially explicit fuel information with forecast weather and advanced fire behaviour models and knowledge to produce locally relevant ratings of fire behaviour potential. Key results: A well-defined framework is essential for categorising and defining fire danger based on operational response, the potential for impact and observable characteristics of fire incidents. The AFDRS is modular, supporting continuous and incremental improvements and allowing upgrades to components in response to new science. Conclusions: The AFDRS provides a new method to estimate fire danger based on the best available fire behaviour models, leading to potentially significant improvements in the way fire danger is calculated, forecast and interpreted. Implications: The Australian Fire Danger Rating System was implemented in 2022, the most significant change to fire danger forecasting in Australia in more than 50 years. We introduce the Australian Fire Danger Rating System (AFDRS), providing a history of fire danger rating in Australia and requirement for advancements. We describe development, design principles and the supporting framework of the AFDRS as an introduction to subsequent papers in this series. This article belongs to the Collection Australian Fire Danger Rating System. [ABSTRACT FROM AUTHOR]

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

Temperate peatland wildfires are of significant environmental concern but information on their environmental effects is lacking. We assessed variation in burn severity and fuel consumption within and between wildfires that burnt British moorlands in 2011 and 2012. We adapted the Composite Burn Index (pCBI) to provide semi-quantitative estimates of burn severity. Pre- and post-fire surface (shrubs and graminoids) and ground (litter, moss, duff) fuel loads associated with large wildfires were assessed using destructive sampling and analysed using a Generalised Linear Mixed Model (GLMM). Consumption during wildfires was compared with published estimates of consumption during prescribed burns. Burn severity and fuel consumption were related to fire weather, assessed using the Canadian Fire Weather Index System (FWI System), and pre-fire fuel structure. pCBI varied 1.6 fold between, and up to 1.7 fold within, wildfires. pCBI was higher where moisture codes of the FWI System indicated drier fuels. Spatial variation in pre- and post-fire fuel load accounted for a substantial proportion of the variance in fuel loads. Average surface fuel consumption was a linear function of pre-fire fuel load. Average ground fuel combustion completeness could be predicted by the Buildup Index. Carbon release ranged between 0.36 and 1.00 kg C m-2. The flammability of ground fuel layers may explain the higher C release-rates seen for wildfires in comparison to prescribed burns. Drier moorland community types appear to be at greater risk of severe burns than blanket-bog communities. [ABSTRACT FROM AUTHOR]

Bushfires are classified as catastrophic disasters capable of inflicting significant destruction. The key detrimental consequences of bushfires include the loss of human lives, trauma within communities, economic losses and environmental damage. For example, the estimated economic loss from the September 2019 to March 2020 bushfires in New South Wales (Australia) was about AUD 110 billion, including more than 3000 burned houses. There has been a notable increase in both the frequency and intensity of bushfires, as clearly demonstrated by recent bushfire events. Bushfires are an intricate phenomenon that transpires across various spatial and temporal scales. Further, the changing circumstances of landscapes, vegetation patterns, weather conditions and ecosystems account for the complexity. Therefore, continual attention is essential for the development of bushfire management strategies. In this context, this paper undertakes a comprehensive literature review of bushfire management strategies, encompassing aspects such as bushfire prediction, detection, suppression and prevention. Based on the review, a bushfire management framework is proposed that can eliminate or successfully mitigate the consequences of bushfires. Further, the paper delves into the domains of fire weather conditions, the initiation of bushfires and the adverse consequences stemming from these fires. Both terrestrial and aerial remote sensing methods have proven to be effective in predicting and detecting bushfires. Nevertheless, a simple unique solution cannot be proposed for bushfire management. Changing weather conditions, topography and the geographic mix of asset types need to be considered when deciding on bushfire management strategies and their breadth and depth of application. [ABSTRACT FROM AUTHOR]

Background: In 1986, CSIRO conducted a large program of experimental fires in grassland at Annaburroo Station, Northern Territory, Australia, with the objective of quantifying the effect of fuel condition (load and height) on fire behaviour. Aims: This paper provides the data collected during this program, representing a unique set of observations and measurements of large, free-burning experimental fires conducted in a multi-factor experimental design. Methods: Data are collated by experimental burn plot, providing detailed measurements of weather (wind speed, air temperature, relative humidity), fuel state (load, height, moisture content, curing) and fire behaviour (rate of spread, flame depth, flame height, head fire width), as well as processed information (e.g. steady-state rate of spread). Data availability: The data are made available for free download on the CSIRO Data Access Portal () and include detailed metadata descriptions of the data and their structure, also provided in this article. Conclusions: We have made the data available for fire behaviour researchers around the world to use in their research under the Creative Commons Attributions licence. It is hoped they will analyse these data and extract new and innovative insights to help improve our understanding of wildland fires burning in grass fuels. The 1986 Annaburroo grassland fire experiments conducted in the Northern Territory, Australia, created a unique dataset of fire behaviour across a range of fuel and burning conditions. This dataset, comprising detailed observations of fuels, weather and fire behaviour in 121 plots, is now publicly available via the CSIRO Data Access Portal. [ABSTRACT FROM AUTHOR]

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests' Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures. [ABSTRACT FROM AUTHOR]

Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction. [ABSTRACT FROM AUTHOR]

Grassland fires and dust weather in Mongolia can trigger major cascading disasters. Grassland fires from autumn to the following spring can indirectly affect dust weather occurrence in the spring by affecting land surface vegetation cover. In this paper, we selected the aimag (province) of Dornod, Mongolia, a typical temperate grassland area, as the study area. The study aims to (1) analyze the spatiotemporal patterns of grassland fire and dust weather in the past 22 years, as well as the effect of grassland fire on dust weather and to (2) explore in depth the mechanisms of the effects of grassland fire on dust weather. To achieve these goals, we utilize high-resolution satellite burned-area data and Synop dust data. In general, grassland fire and dust weather occurrence clearly varied spatiotemporally across the study area. Grassland fires are typically more frequent in spring and autumn, and dust weather is mainly concentrated in spring. Cumulative grassland fires (both days and burned area) from autumn to the following spring affected the spring cumulative dust weather days significantly, especially the spring cumulative dust storm days. Analysis of the mechanism of the effect of grassland fire on dust storms showed that abundant summer precipitation resulted in higher vegetation cover and more accumulated fuel from autumn to April of the following spring. Consequently, the cumulative grassland fire days were higher, and the cumulative burned area was larger during the period, leading to a significant increase in cumulative dust storm days in May of the spring. In Mongolia, grassland fires are often caused by human factors. The findings of the present study could facilitate the crafting of measures to prevent and reduce grassland fires and indirectly minimize dust weather frequency to protect the ecological environment and promote sustainable development. [ABSTRACT FROM AUTHOR]

Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new "MESMER-X" version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts. [ABSTRACT FROM AUTHOR]

Background: An increase in fire weather is expected in a warming climate, but its translation to fire activity (fire numbers and sizes) remains largely unknown. Additionally, disentangling the extent to which geographic and seasonal extensions as well as intensification contribute to future fire activity remain largely unknown. Aims: We aimed to assess the impact of future climate change on fire activity in southeastern France and estimate changes in spatial and seasonal distributions. Methods: We projected future fire activities using a Bayesian modelling framework combined with ensemble climate simulations. Changes in numbers of escaped fires (>1 ha), large fires (>100 ha) and burned area were studied for different emission scenarios or degrees of global warming. Key results: Fire activity could increase by up to +180% for +4°C of global warming, with large expansions of fire-prone regions and long seasonal lengthenings. Overall, changes will be dominated by intensification within the historical fire niche, representing two-thirds of additional future fire activity, half of this occurring during the high fire season. Conclusions: This study confirms that major changes in fire niches would be expected in Euro-Mediterranean regions. Implications: Long-term strategic policies for adapting prevention and suppression resources and ecosystems are needed to account for such changes. Projections of fire activity in southeastern France show that very large increases in fire metrics arise mostly from an intensification in the already fire-prone region during the core of the current fire season and only to a lower degree from an expansion of the fire-prone region and lengthening of the season. (This paper is part of a Special Issue that includes papers on research presented at the IX International Conference on Forest Fire Research, Coimbra, Portugal, November 2022.) [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is expected to cause an increase in fire danger over south-eastern Australia during the 21st century, primarily driven by increased surface temperature. Studies of future fire weather in Victoria, Australia, have so far mostly utilised direct output from general circulation models, which have inadequate resolution for resolving the dynamics of local fire danger and are prone to substantial biases that may affect the seasonality of dry fuels. In this paper, we assess the ability of the Multivariate Adaptive Constructed Analogs (MACA) method to downscale output from general circulation models over Victoria, and replicate statistical attributes of fire danger indices. We find that climatological descriptors of meteorological variables of wind, temperature and humidity are captured extremely well, and fields on extreme fire days are well captured. We find that the method works very well for statistically downscaling fire weather elements over Victoria and provides a vehicle to assess the regional variation of fire weather projections over Victoria. This paper evaluates the computationally efficient Multivariate Adaptive Constructed Analogs (MACA) method for downscaling a single general circulation model to a fine scale over the state of Victoria, Australia. Comparison with a 4-km fire weather climatology dataset shows the method well reproduces observed statistics of mean and extreme fire weather. Photo showing planned burn in Victoria, Australia, by Tim Brown. [ABSTRACT FROM AUTHOR]

Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change. [ABSTRACT FROM AUTHOR]

The destructive Sir Ivan Dougherty fire burned 55,000 hectares around 250 km northwest of Sydney in New South Wales on 12 February 2017. Record hot temperatures were recorded in the area during the lead-in days and the fire conditions at the time were described as the 'worst ever seen in NSW'. The observed weather conditions were hot, dry and very windy ahead of a synoptic frontal wind change during the afternoon. 'Extreme' to 'catastrophic' fire weather was predicted, and the potential for extreme fire behaviour was identified several days in advance. The Australian coupled fire–atmosphere model ACCESS-Fire has been run to explore the characteristics of the Sir Ivan fire. Several features resulting from fire–atmosphere interaction are produced in the simulations. Simulated heat flux along the fire perimeter shows increased intensity on the northern fire flank in response to gradual backing winds ahead of the main frontal wind change. Temporal and spatial variability in fire activity, seen as pulses in fire intensity and fireline wind speed, develop in response to boundary layer rolls in the wind fields. Deep moist convection consistent with the observed pyrocumulonimbus (pyroCb) cloud is simulated over the fire at around the time of the frontal wind change, and matches guidance from the 'PyroCb Firepower Threshold' tool, which showed transient favourable conditions. After the wind change, short-lived near-surface and elevated vortices suggest organised rotating features on the northern flank of the fire. The coupled model captures processes that cannot be produced in uncoupled fire predictions and that are not captured in current operational meteorological forecast products provided to Australian fire agencies. This paper links the features from coupled simulations to available observations and suggests pathways to embed the learnings in operational practice. [ABSTRACT FROM AUTHOR]

Peatland fires in Indonesia tend to be more active during El Niño-related droughts, with the exception of fires in North Sumatra. As North Sumatra is located north of the equator and is affected by the winter and summer monsoons, fires tend to be more active not only during the dry main season from January to March, but also in June and August due to short-term droughts. Due to these complex fire trends, no appropriate fire-related indices have been found in North Sumatra. In this paper, 20 years of fire (hotspot (HS) data from 2003 to 2022, weather data (hourly and daily), and various satellite data were used to analyze fire weather conditions in Dumai plantation areas. Analysis results of 20 fire incidents (largest fires (HSs) of each year) showed the following fire weather conditions: high wind speeds (>19 km h−1), high temperatures (>33 °C), and low relative humidity (<50%). Based on the results of fire and weather analyses, several fire-related indices selected from various satellite-measured data were examined. Precipitable water vapor has the highest negative correlation with fires. It is hoped that this new fire index will be used for fire prevention not only Sumatra but also in other areas in Indonesia. [ABSTRACT FROM AUTHOR]

Anyone who has tried lighting a campfire on a windy day can appreciate how difficult it could be. However, despite real-life experience and despite laboratory experiments which have demonstrated that fire ignition risk dramatically decreases beyond a certain wind threshold, current fire weather indices (FWIs) do not take this effect into account and assume a monotonic relation between wind velocity and ignition risk. In this paper, we perform a global analysis which empirically quantifies the probability of ignition as a function of wind velocity. Using both traditional methods (a logistic regression and a generalized additive model) and machine learning techniques, we find that beyond a threshold of approximately 3–4 m/s, the ignition risk substantially decreases. The effect holds when accounting for additional factors such as temperature and relative humidity. We recommend updating FWIs to account for this issue. [ABSTRACT FROM AUTHOR]

Confronted with an imminent bushfire threat, Australian householders can respond by evacuating, waiting to see what the bushfire does before deciding, or remaining to defend property from the fire. This decision-making is central to householders' survival because late evacuation has been associated with fatalities. North Americans who seek alternatives to mandatory evacuation from wildfires are confronted with similar choices. Lindell and Perry's Protective Action Decision Model (PADM) has been extensively used to analyse the behaviour and decision-making of people subject to threat from a wide range of hazards and has acquired the status of a theory of hazard decision-making. It has not been widely used for the study of hazards in Australia. This paper addresses the question of how the PADM can be applied to the analysis of behaviour in Australian bushfires and North American wildfire, and whether the model requires adjusting for effective use in a context where householders choose their protective response rather than having decisions made for them. The paper addresses Australian bushfire policy, which emphasises the dangers of staying to defend during extreme fire weather conditions and promotes leaving early as the safer option but allows householders to decide whether to 'stay or go' during a bushfire. This Australian policy approach impacts key elements of the PADM, most importantly how long-run hazard adjustments, preparatory, mitigating and equipping actions, effect the perception of evacuating or remaining. The paper concludes that the PADM should be modified to reflect the complexity of choice confronting Australian bushfire prone householders and North Americans considering alternatives to mandatory evacuation. [ABSTRACT FROM AUTHOR]

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day‐to‐day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near‐surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1‐day R2 = 0.39–0.48, 2‐day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models. Plain Language Summary: Increasing wildfire smoke is undoing decades of air quality progress, yet air quality forecasts often miss the most intense smoke events. This is because forecasted smoke is released at constant rates whereas the rate of smoke release from real wildfires varies in time. In this work we teach a machine learning algorithm to predict the daily change in fire heat output, a quantity that is used to calculate wildfire emissions. The machine learning algorithm uses information regarding weather, fuel moisture and amount, and firefighting efforts to make its predictions. We also tried to predict the daily change in fire heat output using only weather information but found the machine learning method to be more successful. Many federal agencies have their own ways of tracking fire weather and fuel moisture, and in this paper, we show that we can apply machine learning to the data from any of several agencies and get the same level of forecasting skill. Key Points: Random forest models trained on weather, fuel, and firefighting data surpass persistence and weather‐based methods to predict fire energyRandom forest models beat persistence across states, for most days in the 2020 fire season, and across levels of fire severityFire weather and latest fire energy predictors add most skill to the random forest; models using agency weather data perform similarly [ABSTRACT FROM AUTHOR]

Wildfire is one of the primary natural disturbance agents in the forests of China. The forecast of fire danger is critically important to assist stakeholders to avoid and mitigate wildfire-induced hazards and losses to both human society and natural ecosystems. Currently, fire danger rating methods often focus on fire weather classification based on fixed thresholds, which has shortcomings in generalizability and robustness. Based on historical fire occurrence data and meteorological data of Northeastern China from 2004 to 2015, we proposed a forest fire danger rating classification and forecasting model by combining the advantages of the Canadian Fire Weather Index (FWI) system and two machine learning models such as the Long Short-Term Memory (LSTM) network and Random Forest (RF) model. The method is divided into two stages. The first stage is the LSTM-based FWI system indexes prediction. In the first stage, the future FWI system indexes are obtained through the LSTM-based prediction model, and the RMSE and MAE of the prediction results are calculated to verify the prediction performance of the model. The second stage is random forest-based fire danger rating prediction method. In the second stage, we use the random forest method to get the fire danger occurrence probability and present the fire danger rating classification scheme. Then we verify the reliability of the fire danger rating classification scheme by using the forest fire danger data in Qipan Mountain. Our method predicts two randomly selected future intervals, and the prediction accuracy is 87.5%. The experimental results show that our machine learning-based forest fire danger rating classification method can provide a new idea for forest fire danger warnings. [ABSTRACT FROM AUTHOR]

Background. Increasing extreme weather events due to climate change require updated environmental monitoring and prediction systems in Germany. Aim. The Grassland Fire Index (GLFI), developed by the German Meteorological Service ~15 years ago for temperate climates, was revised to improve fire-danger predictions during the fire season. Our paper gives insight into the new model version. Methods. The former fire-behaviour core, i.e. Fosberg's Fire Weather Index (FWI), is replaced by the standardised fire-reaction intensity, a different fuelmoisture of extinction term, and a replica of the fire-spread rate of the Canadian FFBP-System. A standardised ease-of-ignition index is added as a measure of ignition success. The fire module is supplied with diurnal dead-grass fuel-moisture calculations based on the water-budget and energy-balance concept. Key results. The GLFI output is compared with diurnal fuelmoisture measurements and results of Wotton's Grass-Fuel-Moisture model, Fosberg's FWI, and Cheney's rate of spread equation. The GLFI computes periods with a high fuel moisture more realistically, whereas it exceeds Cheney's rate-of-fire spread systematically at lower wind speeds, which leads to higher danger ratings during calm-air conditions (as requested by users). Conclusions and Implications. The GLFI estimates dead-fuel moisture and fire danger on open, horizontal topography according to the current scientific level. Model extensions are necessary to run the model on complex topography under varying greenness and occasional frost conditions. [ABSTRACT FROM AUTHOR]

Accurate predictions of daily wildfire growth rates are crucial, as extreme wildfires have become increasingly frequent in recent years. The factors which determine wildfire growth rates are complex and depend on numerous meteorological factors, topography, and fuel loads. In this paper, we have built upon previous studies that have mapped daily burned areas at the individual fire level around the globe. We applied several Machine Learning (ML) algorithms including XGBoost, Random Forest, and Multilayer Perceptron to predict daily fire growth rate based on meteorological factors, topography, and fuel loads. Our best model on the entire dataset obtained a 1.15 km2 MAE. The ML model obtained a 90% accuracy when predicting whether a fire's growth rate will increase or decrease the following day, compared to 61% using a logistic regression. We discuss the central factors that determine wildfire growth rate. To the best of our knowledge, this study is the first to perform such analyses on a global dataset. [ABSTRACT FROM AUTHOR]

Two multispectral satellite imagery products are presented that were developed for use within the fire management community. These products, which take the form of false color red–green–blue composites, were designed to aid fire detection and characterization, and for assessment of the environment surrounding a fire. The first, named the Fire Temperature RGB, uses spectral channels near 1.6, 2.2, and 3.9 μm for fire detection and rapid assessment of the range of fire intensity through intuitive coloration. The second, named the Day Fire RGB, uses spectral channels near 0.64, 0.86, and 3.9 μm for rapid scene assessment. The 0.64 μm channel provides information on smoke, the 0.86 μm channel provides information on vegetation health and burn scars, and the 3.9 μm channel provides active fire detections. Examples of these red–green–blue composite images developed from observations collected by three operational satellite imagers (VIIRS on the polar-orbiting platform and the Advanced Baseline Imager and Advanced Himawari Imager on the geostationary platform) demonstrate that both red–green–blue composites are useful for fire detection and contain valuable information that is not present within operational fire detection algorithms. In particular, it is shown that Fire Temperature RGB and Day Fire RGB images from VIIRS have similar utility for fire detection as the operational VIIRS Active Fire products, with the added benefit that the imagery provides context for more than just the fires themselves. Significance Statement: The current generation of operational polar-orbiting weather satellites that began with the launch of Suomi NPP offers new capabilities with regard to fire detection and monitoring. In particular, false color red–green–blue composite imagery is now being used by fire managers, incident meteorologists, and others in the fire management community to visualize a fire's behavior and the context in which it occurs. This paper outlines two of these red–green–blue composites that have gained widespread use throughout the U.S. National Weather Service and the Alaska Fire Service. These red–green–blue composites have been applied to the current generation of geostationary and polar-orbiting satellites to great effect and have changed how incident management teams respond to wildland fires. [ABSTRACT FROM AUTHOR]

This paper introduces and analyzes fire climate regimes, steady-state conditions that govern the behavior of fire weather. A simple model representing fire climate was constructed by regressing high-quality regional climate averages against the station-averaged annual Forest Fire Danger Index (FFDI) for Victoria, Australia. Four FFD indices for fire years 1957–2021 were produced for 10 regions. Regions with even coverage of station-averaged total annual FFDI (ΣFFDI) from 1971–2016 exceeded Nash–Sutcliffe efficiencies of 0.84, validating its widespread application. Data were analyzed for shifts in mean, revealing regime shifts that occurred between 1996 and 2003 in the southern states and 2012–2013 in Queensland. ΣFFDI shifted up by ~25% in SE Australia to 8% in the west; by approximately one-third in the SE to 7% in the west for days above high fire danger; by approximately half in the SE to 11% in the west for days above very high, with a greater increase in Tasmania; and by approximately three-quarters in the SE to 9% in the west for days above severe FFDI. Attribution of the causes identified regime shifts in the fire season maximum temperature and a 3 p.m. relative humidity, with changing drought factor and rainfall patterns shaping the results. The 1:10 fire season between Regimes 1 and 2 saw a three to seven times increase with an average of five. For the 1:20 fire season, there was an increase of 2 to 14 times with an average of 8. Similar timing between shifts in the Australian FFDI and the global fire season length suggests that these changes may be global in extent. A trend analysis will substantially underestimate these changes in risk. [ABSTRACT FROM AUTHOR]

The intelligent information system constructed by the sensor web can monitor all kinds of sudden abnormal events, improve the ability of event discovery and rapid disposal, and promote the development of smart city and the construction of the Internet of Things (IoT). In this paper, we consider the problem of complex integration and poor expansibility in the large-scale full-network operation and maintenance system construction and propose an active and passive event service (APES) method based on the sensor web. In the APES, a system framework with the perception layer, data service layer, event service layer, and user layer is firstly defined and constructed. Secondly, a middleware with the ability to active and passive event service (APES) is designed and implemented based on the system framework. Finally, taking abnormal weather and fire warning as examples, the performance of the proposed event service middleware is tested, respectively. Experimental results show that the proposed APES model in this paper has the advantages of high precision, stable operation, and strong practicability and solves "Information Island" and low reusability in the whole network operation and maintenance system. This is an attempt at the structural design of a similar intelligent information system. [ABSTRACT FROM AUTHOR]

This I Atmosphere i Special Issue on "Fire and the Atmosphere" explores the atmosphere's role in a range of wildland fire related topics through 14 papers covering fire spread, fuels, climate change, fire behavior, smoke, fire weather and fire climate relationships. The papers can be loosely grouped into four categories: fire weather and climate, numerical modeling, smoke impacts and designing field measurement campaigns. Three of the four papers in the fire weather and climate category focus on fire weather indices, common tools for combining weather variables to reduce complex processes down to a more easily understood scale. [Extracted from the article]

Wildland fire is expected to increase in response to global warming, yet little is known about future changes to fire regimes in Europe. Here, we developed a pyrogeography based on statistical fire models to better understand how global warming reshapes fire regimes across the continent. We identified five large‐scale pyroregions with different levels of area burned, fire frequency, intensity, length of fire period, size distribution, and seasonality. All other things being equal, global warming was found to alter the distribution of these pyroregions, with an expansion of the most fire prone pyroregions ranging respectively from 50% to 130% under 2° and 4°C global warming scenarios. Our estimates indicate a strong amplification of fire across parts of southern Europe and a subsequent shift toward new fire regimes, implying substantial socio‐ecological impacts in the absence of mitigation or adaptation measures. Plain Language Summary: Pyroregions represent the typical range of area burned, fire frequency, intensity, and seasonality that prevail in a region over long periods of time. Pyroregions are thus of great interest given their potential utility to determine regional fire patterns and foresee future alterations in response to global warming. Previous research has investigated the effects of global warming focusing on burned area and fire frequency, yet changes in fire regimes are often overlooked. In this paper, we examined the effects of global warming on pyroregions in Europe. We identified five large‐scale pyroregions reflecting different fire regimes. Future climate projections indicated an increase in fire activity and subsequent expansion of fire prone pyroregions across parts of southern Europe under warmer and drier conditions. The most fire prone pyroregions presented a spatial expansion ranging respectively from 50% to 130% under 2° and 4°C global warming scenarios with potential impacts on society. Limiting global warming would substantially reduce the expansion of the fire prone pyroregions in Europe. Key Points: This is the first study to project future changes in fire regimes on a pan‐European scaleOur projections point to an intensification and expansion of the most fire prone pyroregions in southern Europe under a warmer climateLimiting global warming would substantially reduce the expansion of the area at risk and the transition toward more intense fire regimes [ABSTRACT FROM AUTHOR]

Wildfire smoke can be transported far from its origin, adversely impacting human health. The height of the atmospheric mixing layer, the near-surface layer of the troposphere in which turbulent convection leads to vertical mixing, is called the mixing height. Mixing height is a critical input in the smoke dispersion and air quality models used by agencies that monitor wildfires and air pollution. These models, coupled with forecaster expertise, are also used to determine if it is safe to execute a prescribed burn. In this paper, we derive mixing heights from two satellite datasets in order to assess mixing height forecasts produced by the NationalWeather Service (NWS) Fire Weather Program. Namely, we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) Vertical Feature Masks (VFM) and vertical water vapor profiles from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our comparison indicates that NWS forecasts tend to underestimate CALIOPmixing heights with a median relative error of --13% and a mean relative error of --3.34%. Although MODIS and NWS mixing heights showed some agreement below 3 km, the lower vertical resolution of the MODIS estimates hindered a full comparison. We examine the discrepancies among mixing heights over wildfire smoke plumes determined by these methods and discuss biases and limitations. This work provides insight into potential bias patterns present in current mixing height forecasts and provides directions for future improvements in both NWS mixing height forecasts and satellite-based measurements of mixing height. [ABSTRACT FROM AUTHOR]

Violent fire-driven convection can manifest as towering pyrocumulus (pyroCu) or pyrocumulonimbus (pyroCb) clouds, which can have devastating impacts on the environment and society. Their associated fire spread is erratic, unpredictable and not generally suppressible. Research into large pyroconvective events has mainly focused on the atmospheric processes involved in normal atmospheric convection, or on surface fire weather and associated fuel conditions. There has been comparatively less attention paid to the role of the fire itself in these coupled fire-atmosphere events. This paper draws on recent insights into dynamic fire propagation and extreme wildfire development to investigate how the fire influences the occurrence of violent pyroconvective events. A static heat source of variable dimension and intensity is used. This is accompanied by a companion paper that extends the analysis by including the effect of fire geometry on the pyroconvective plume. The analyses indicate that the spatial expanse and intensity of large fires are critical factors driving the development of pyroconvective plumes and can override the influence of the stability of the atmosphere. These findings provide motivation for further investigation into the effect of the fire's attributes on the immediate atmosphere and have the potential to improve forecasting of blow-up fire events. [ABSTRACT FROM AUTHOR]

In the long run, ongoing climate change is expected to alter fuel production as well as the frequency and severity of fire weather, which may result in an unprecedented frequency of extreme fire events. In this paper we propose a simplified and spatially explicit method to assess the probability of experiencing large fires, based on topography (slope length) as well as extent and aggregation of the forested area (fuel connectivity). We considered 21 homogeneous pyroregions covering entire Switzerland as a study case and computed the length of the upslope paths within the forested areas, simulating ignition points on a systematic 100 × 100 m square grid. We then compared the obtained path lengths for each pyroregion with selected historical large forest fire statistics (e.g., mean area of the largest 5% of fires, maximum burnt area per fire) collected over the course of the last 30 years. This resulted in rather high R2 values, ranging from 0.558 to 0.651. The proposed approach was shown to allow for an easy identification and geo-localization of potential hotspots in terms of the likelihood for large fires to occur in mountainous regions, which is a prerequisite for a targeted planning of fire management measures aimed at preventing large fires and related post-fire gravitative natural hazards. [ABSTRACT FROM AUTHOR]

This paper is the second of two reviewing scientific literature from 100 years of research addressing interactions between the atmosphere and fire behaviour. These papers consider research on the interactions between the fuels burning at any instant and the atmosphere, and the interactions between the atmosphere and those fuels that will eventually burn in a given fire. The first paper reviews the progression from the surface atmospheric properties of temperature, humidity and wind to horizontal and vertical synoptic structures and ends with vertical atmospheric profiles. This second paper addresses plume dynamics and vortices. The review presents several questions and concludes with suggestions for areas of future research. This paper reviews over 100 years of observations and studies of how the atmosphere interacts with wildland fires. It examines fire vortices and plume dynamics, synthesising the literature and identifying knowledge gaps. [ABSTRACT FROM AUTHOR]

Timber harvesting has been common in the region around the author's study area. Thus: Throughout the paper, the authors make erroneous comments about the Victorian Land Conservation Council (LCC) Act of 1970, claiming that the LCC Act aimed to ban prescribed burning. Furthermore, in a comprehensive study of indigenous burning in Australia, Williams et al. [[17]] concluded that "... human control of fire by prehistoric people in Australia is not evident at broad landscape levels". This cannot be true because the LCC had no impact on landowners around the authors' study area until 1979-1984, when the LCC recommendations were implemented. [Extracted from the article]

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps. [ABSTRACT FROM AUTHOR]

Coupled atmosphere‐fire modeling is recognized as a relevant approach for the representation of the interaction between a wildland fire and local meteorology at landscape scales. The atmospheric model component used in the coupled system is based on several approximations, which are adopted for computational efficiency or physical processes representation, including the widely used anelastic approximation. The validity domain of the anelastic approximation may be questioned in the context of high‐resolution wildland fire modeling due to the large fire‐induced heat releases near the surface. This study aims to study this question with the MesoNH anelastic model coupled with the Blaze fire model. A compressible version of the MesoNH‐Blaze coupled model has been developed for comparison with the anelastic system. The FireFlux I experimental fire is used for this comparative study conducted at a 10‐m and a 25‐m horizontal atmospheric resolution. Results show significant anelastic/compressible differences at a 10‐m resolution on the physical processes occurring near the fire with higher horizontal velocities and the presence of gravity waves downstream of the fire. This is in addition to the fire plume with realistic larger vertical velocities. Differences at a 25‐m resolution are much smaller in all evaluated processes. The compressible system only enriches the physics underlying fire‐atmosphere interactions at a very high resolution, which means that the anelastic approximation remains relevant for large‐scale coupled atmosphere‐fire simulations, considering the significant economy concerning numerical costs. Plain Language Summary: Wildfires burn large amounts of forests each year, destroying the living habitat of many species, endangering human settlements and provoking cross‐continent smoke events leading to air quality issues. Wildfires result from complex physical, chemical and biological processes. Understanding the fundamental processes driving wildfire behavior is a key point for the prediction of fire spread across the landscape and the induced atmospheric dynamics. Numerical models coupling the atmospheric dynamics (wind, temperature, air density and pressure, etc.) and fire front evolution are efficient tools in this scientific process. This paper evaluates how such model simulations are affected by the way in which the effects of high air density gradients induced by the very large amount of heat released into the atmosphere are modeled. When the spatial resolution of the atmospheric model is as high as 10 m, an exact – compressible – formulation leads to the formation of gravity waves downstream of the fire, and to stronger fire‐induced wind, fire propagation and larger vertical velocities than an approximate (and computationally faster) approach. At a resolution of 25 m, which is still very high, the approximate approach leads to results, which are as good as the exact one. This can then be used to simulate wildland fire behavior at landscape‐to‐meteorological scales. It also paves the way for future accurate wildfire forecast systems. Key Points: A compressible version of the MesoNH atmospheric model has been developed to evaluate equation system approximations in wildfire simulationHorizontally buoyancy‐driven effects induce turbulent movements, waves ahead of the fire, and a bending of the plumeThe anelastic approximation for the atmosphere model is suitable to represent wildland fire effects, except for resolutions of 10 m or finer [ABSTRACT FROM AUTHOR]

In recent environmental studies, the examination of extreme events has great impact. The block maxima of environment-related indices can be analyzed by the tools of extreme value theory. For instance, the monthly maxima of the fire weather index at stations in British Columbia might be modeled by GEV distribution, but it is questionable whether the underlying stochastic process is stationary. This property can lead us to different approaches to determine whether there is a significant trend in the past few years' data or not. One such approach is a likelihood ratio based procedure, which has favorable asymptotic properties, but for realistic sample sizes it might have large decision errors. In this paper, we analyze the properties of the likelihood ratio test for extremes by bootstrap simulations and present a simulation-based procedure to overcome the problem of small sample sizes. We also propose a return level calculation method. Using our theoretical results we reassess the trends of fire weather index monthly maxima in selected stations of British Columbia. [ABSTRACT FROM AUTHOR]

Nowadays, wildfires are an increasing challenge for the defence sector. The fire risk of a given area depends only in part on human factors and the number of registered fires. A fire occurs when the moisture content of dead biomass drops to a level, where the fire can already spread between the individual pieces of fuel. Daily fire danger forecast examines the constant and changing components of the fire environment. This determines the flammability of the biomass; the rate of fire spread makes firefighting more difficult. The fire danger forecast identifies the fire hazard periods when fires can occur. Fire Risk Assessment Systems have been developed in many countries around the world. In addition to the daily fire risk, these include parameters describing the vulnerability of the areas affected by the fire. National risk assessments are available in many countries around the world using several methodologies. The Joint Research Centre of the European Commission has developed a community-wide approach to forest fire risk assessment, using scientific results and studying good practices. In this approach, the risk of a forest fire is made up of the effects of daily fire hazards and vulnerabilities. The risk of fire due to weather conditions is associated with ignition and the spread of fire. The authors examine in the paper the basic criteria to assess wildfire risk at the pan-European level. The authors analyse external and internal risk factors in an observation plot and examine how international recommendations can be utilised in Hungary. [ABSTRACT FROM AUTHOR]

Wildfires are a major natural hazard that lead to deforestation, carbon emissions, and loss of human and animal lives every year. Effective predictions of wildfire occurrence and burned areas are essential to forest management and firefighting. In this paper we apply various machine learning (ML) methods on a 0.25° monthly resolution global dataset of wildfires. We test the prediction accuracies of four different fire occurrence classifiers: random forest (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP) neural network, and a logistic regression. Our best ML model predicts wildfire occurrence with over 90% accuracy, compared to approximately 70% using a logistic regression. We then train ML regression models to predict the size of burned areas and obtain an MAE score of 3.13 km 2 , compared to 7.48 km 2 using a linear regression. To the best of our knowledge, this is the first study to be conducted in such resolution on a global dataset. We use the developed models to shed light on the influence of various factors on wildfire occurrence and burned areas. We suggest building upon these results to create ML-based fire weather indices. [ABSTRACT FROM AUTHOR]

Fire activity in 288 areas (2.5° N × 10° E) in the Arctic region (50°–70° N, 0°–360° E) was analyzed using about 4.4 million satellite hotspot (HS) data from 2002 to 2021. A total of 21 high fire density areas from eastern Europe to western Canada were selected, and their fire–weather conditions during each active fire period were analyzed using about 1820 various weather maps at the upper and the lower air level. Analysis results showed that the active fires in the Arctic region occurred under the fire–weather conditions associated with the northward movement of cut-off high (COH) and warm air masses detached from the south caused by large westerly meandering (LWM). LWM is a sign of the beginning of an active fire period. Very active fires on HS peak days occurred several days after the start of the northward movement of COHs and under mainly high-pressure conditions in the upper air and strong wind conditions in the lower air. The time lag of these several days suggests that we may be prepared for very active fires. The fire–weather analysis approach described in this paper has shown that future large-scale fire outbreaks are predictable. [ABSTRACT FROM AUTHOR]

The paper presents the results on the study of the possible application of the Canadian Forest Fire Weather Index and the Modified Angstrom Index in forest fire risk assessments. The daily values of these indices for the period 2005-2015 were related to the forest fire database. It was found that there is a relatively weak to moderate correlation between forest fires and the values of the Canadian Forest Fire Weather Index. In order to improve the wildfire risk assessments (including forest fires), the index was modified. The modified index has a significantly greater correlation with the actual events of forest fires and consequently a much wider application in southern Serbia. The modified index can be of great importance in the future concepts of forest fire risk management. [ABSTRACT FROM AUTHOR]

Fire‐induced domino accidents are less probable but highly consequent. Although past studies conducted risk assessment of such events, classical models were used for impact estimation. Moreover the influence of varying weather on evolution of fire domino effect was not investigated, where past research necessitates the use of computational fluid dynamics to perform such analysis. This paper adopts a consolidated risk analysis approach to perform consequence modeling using fire dynamics simulator. Various atmospheric conditions were incorporated in the model. The outcomes were used in probabilistic analysis to estimate the escalation probabilities. Risk of domino events was presented as domino levels. It was found that 37% reduction in humidity ratio resulted in 10% decrease in tanks failure time. At 3 and 6 m/s winds, the failure time of tank in flame direction reduced by 56% and 80%, whereas the escalation probability increased by 3 and 4 orders respectively. The tank farm failed in 11.3 and 12.85 min at 3 and 6 m/s respectively, which is less than the suggested mitigation time. [ABSTRACT FROM AUTHOR]

This paper aims to contribute to the field of green environment and meteorological signal processing by exploring methods for analyzing and predicting environmental factors. The focus is on developing an intelligent approach for preventing and predicting wildfires that may arise due to changes in atmospheric temperature or other conditions. The proposed solution involves using machine learning and evolutionary deep learning to create a neural model that can interact with the Internet of Things (IoT) and respond in real-time to minimize potential damage. The experiments were carried out in the forests of Jandouba, Tunisia, using Python software. The results demonstrate that this approach offers significant advantages over the most ranked existing Canadian method (FWI) for fire weather index. [ABSTRACT FROM AUTHOR]

A series of major fires spread across eastern Washington and western Oregon starting on 7 September 2020, driven by strong easterly and northeasterly winds gusting to ~70 kt (1 kt ≈ 0.51 m s−1) at exposed locations. This event was associated with a high-amplitude upper-level ridge over the eastern Pacific and a mobile trough that moved southward on its eastern flank. The synoptic environment during the event was highly unusual, with the easterly 925-hPa wind speeds at Salem, Oregon, being unprecedented for the August–September period. The September 2020 wildfires produced dense smoke that initially moved westward over the Willamette Valley and eventually covered the region. As a result, air quality rapidly degraded to hazardous levels, representing the worst air quality period of recent decades. High-resolution numerical simulations using the WRF Model indicated the importance of a high-amplitude mountain wave in producing strong easterly winds over western Oregon. The dead fuel moisture levels over eastern Washington before the fires were typical for that time of the year. Along the western slopes of the Oregon Cascades, where the fuels largely comprise a dense conifer forest with understory vegetation, fire weather indices were lower (moister) than normal during the early part of the summer, but transitioned to above-normal (drier) values during August, with a spike to record values in early September coincident with the strong easterly winds. Forecast guidance was highly accurate for both the Washington and Oregon wildfire events. Analyses of climatological data and fuel indices did not suggest that unusual preexisting climatic conditions were major drivers of the September 2020 Northwest wildfires. Significance Statement: This paper describes the meteorological conditions associated with major wildfires in eastern Washington and western Oregon during September 2020. It was found that unusual, extreme winds from the north and east played a critical role in initiating and supporting the fires. [ABSTRACT FROM AUTHOR]

Forest fires are important natural disturbances that influence accurate estimations of forest carbon budgets, largely owing to the uncertainty of carbon emissions from forest fires. Fuel burning efficiency is an important factor affecting accurate estimations of carbon emissions and is difficult to quantify. Here, we quantified burning efficiencies of fuel strata by fire severity and forest types and investigated influencing factors. Burning efficiencies of fuel strata increased with increasing fire severity. The tree stratum had low values of burning efficiency of 0.76, 0.83, 6.84% under low-, moderate-, high-severity fires respectively. The burning efficiency of the herb stratum was the highest, over 95%, followed by the litter stratum between 49 and 85%. Although the tree stratum accounted for the largest carbon storage of aboveground fuels, most carbon consumed during fires came from the shrub and herb strata. Among forest types, the burning efficiency of aboveground fuels in Pinus pumila–Larix gmelinii forest was much higher than the other two studied. Fire Weather Index (FWI) and temperature exerted a positive effect on the burning efficiency of understorey fuels. Precipitation mainly had a negative influence on the burning efficiency of shrub and duff. Forest fires are important disturbances that influence accurate estimates of forest carbon budgets. This paper calculates burning efficiencies of fuels across different strata grouped by fire severities and by forest types and estimates carbon emissions produced from eight forest fires. Effects of environmental factors on fuel burning efficiency are also discussed. [ABSTRACT FROM AUTHOR]

This paper applies the Multivariate Adaptive Constructed Analogs (MACA) statistical downscaling method to 12 general circulation models to produce 21st century projections of fire weather variables over Victoria, Australia, under two emissions scenarios. The statistically downscaled model data accurately replicate the observed distributions of meteorological variables over the contemporary period, but underestimate fire danger extremes in some models. Under each climate scenario, both mean and extreme fire danger are expected to increase. Though there is variation across Victoria, the 12-model average by year for RCP8.5 indicates a 10–20% increase in extreme (99th percentile) Forest Fire Danger Index across the state, with the greatest change projected in the north-west region. At five geographically and climatologically different locations across Victoria, there is a 50–200% increase in the number of days per year exceeding the threshold for the Victorian Very High or higher fire danger rating by the end of the century compared with the start. The high-end warming (RCP8.5) scenario shows increased temperature to be the main driver of heightened fire danger. Changes in temperature, humidity and precipitation during spring and early summer both increase the length of the fire season and may reduce springtime opportunities for prescribed burning. Twelve GCMs are downscaled over Victoria, Australia, to estimate trends in fire weather frequency under two emissions scenarios using the Multivariate Adaptive Constructed Analogs (MACA) method. Increases of ~50–200% in the number of Very High or above fire danger days are likely by 2100, mostly due to higher temperatures. Photo showing smoke from a planned burn in Victoria, Australia, by Nick McCarthy. [ABSTRACT FROM AUTHOR]

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An important aspect of predicting future wildland fire risk is estimating fire weather—weather conducive to the ignition and propagation of fire—under realistic climate change scenarios. Because the majority of area burned occurs on a few days of extreme fire weather, this task should be able to resolve fire weather extremes. In this paper, we present a statistical downscaling procedure based on distribution based scaling (DBS) to bias correct the Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System, as calculated from modeled climate data. Our study area is western Canada (British Columbia and Alberta) and we consider both an historical control period (1990–2000) and three future time periods (2020–2030, 2050–2060, and 2080–2090). The historical data used to calibrate the DBS procedure comprises weather station data and weather from the North American Regional Reanalysis (NARR), whereas the future climate projections are the output of three regional climate models, corresponding to different model parameterizations and downscaled from the NCAR Community Earth System Model under the RCP 8.5 scenario. By fitting a truncated Weibull distribution to observed and modeled FWI values, our method is able to reproduce historical extremes in fire weather indices as determined by the distribution of annual potential spread days, which are defined as days with FWI values greater than 19. Moreover, by calibrating the DBS procedure with gridded reanalysis data as well as station observations, we are able to project future spread day distributions over the entire study area. The results of this study show the DBS procedure leads to a greater number of projected annual spread days at most locations compared with estimates using the uncorrected model output, and that all three RCM models show positive increases in potential annual spread days for the 2050–2060 and 2080–2090 time periods. [ABSTRACT FROM AUTHOR]

Improving decision processes and the informational basis upon which decisions are made in pursuit of safer and more effective fire response have become key priorities of the fire research community. One area of emphasis is bridging the gap between fire researchers and managers through development of application-focused, operationally relevant decision support tools. In this paper we focus on a family of such tools designed to characterise the difficulty of suppression operations by weighing suppression challenges against suppression opportunities. These tools integrate potential fire behaviour, vegetation cover types, topography, road and trail networks, existing fuel breaks and fireline production potential to map the operational effort necessary for fire suppression. We include case studies from two large fires in the USA and Spain to demonstrate model updates and improvements intended to better capture extreme fire behaviour and present results demonstrating successful fire containment where suppression difficulty index (SDI) values were low and containment only after a moderation of fire weather where SDI values were high. A basic aim of this work is reducing the uncertainty and increasing the efficiency of suppression operations through assessment of landscape conditions and incorporation of expert knowledge into planning. This research summarises current applications and improvements to methods used to characterise wildfire suppression difficulty in forested landscapes. We demonstrate application of a suppression difficulty index variant developed for real-time decision support on fires in the USA and a series of recent improvements applied to a wildfire in Spain. [ABSTRACT FROM AUTHOR]