Recent record-breaking fire activity in the western US poses clear threats to humans, ecosystems, and climate. Larger and faster fires increase the challenges for fire managers and further motivate the need for improved tracking of extreme fire behavior. There are also known limitations to our current ability to monitor fires from space. These include infrequent coverage from moderate resolution (<= 1 km) sensors, smoke and cloud obscuration, omission of small or low-intensity fires, and atmospheric attenuation of fire radiative power (FRP). These effects diminish our ability to quantify fire behavior and emissions, including persistent burning behind the flaming fire front, particularly in ecosystems with high fuel loads. In this study, we examined the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery and data products to assess the utility of candidate fire pixels in addition to the low/nominal/high confidence 375-m fire detections already included in the active fire product. We found that these candidate pixels added 45% more daytime detections and 12% more nighttime detections for large fires in the western US 2020 fire season. Candidate fires were highly consistent with areas of flaming and smoldering fire activity identified by near-coincident airborne data as well as patterns of known active or candidate fires in sequential VIIRS overpasses, without significantly increasing false detections (commission errors). The candidate fire detections helped fill data gaps due to cloud obscuration during large fires that generated pyrocumulonimbus (pyroCb) clouds. Including this additional information also impacted estimates of fire activity, increasing fire persistence by 20% and FRP by 7% across our sample. Although the contribution from candidate fire detections to total FRP was relatively small, including these additional pixels could provide a more consistent estimate of fire emissions for smoke models and air quality forecasts by filling gaps in active fire information and improving the representation of smoldering fire activity. These results demonstrate the potential to augment the standard VIIRS product with candidate fire information for known large fire events to improve fire tracking and downstream products. Such approaches to leverage additional VIIRS information may be suitable for other biomass burning regions where global fire detection algorithms provide incomplete information for specific fire types and observing conditions.

ABSTRACT The current generation of geostationary Earth-observing satellites provide spectral bandpass, spatial resolution and imaging frequency characteristics well suited to near-continuous active fire detection and monitoring. The earliest of these systems-SEVIRI on-board EUMETSAT’s MSG series-has operated since 2004, and more recently the capability has been expanded globally with the ABI on-board NOAA’s GOES-16 and GOES-17 satellites, and the AHI on-board JMA’s Himawari-8 and Himawari-9. At present, the NOAA and EUMETSAT operational geostationary active fire products are available based on two different algorithms: the Fire Detection and Characterization (FDC) product operating with data from GOES-16 and −17, and FRP-PIXEL active fire products from GOES, Himawari and MSG. We have conducted a comprehensive accuracy assessment of these geostationary fire products across two seasons (1 January–31 March 2020 and 1 July–30 September 2020), based on comparison to Landsat active fire detections made simultaneously (±5 minutes of geostationary overpass time) with the geostationary data. Compared to Landsat we find (i) low false alarm rates, ranging between 4%–7% (FDC) and 2%-6% (FRP-PIXEL)- depending on the season and hemispheric-disk for high confidence pixels, (ii) a reduction in this false alarm rate for FDC due to algorithm changes made since our prior (2018) validation effort (48% false alarms in summer 2018 compared to 4% in summer 2020 for high confidence pixels), and (iii) comparable active fire pixel detection rates for the FDC product (high confidence fire pixel classes only) and the matching FRP-PIXEL product (all fire pixel confidence classes). Overall, the performance of these geostationary products is shown to be strong and complementary in that the FRP-PIXEL product has fewer false alarms but a lower detection rate, whereas the FDC product detects more fire pixels but with a much higher false alarm rate.

Vegetation fires are common in South/Southeast Asian (SA/SEA) countries. However, very few studies focused on vegetation fires and the changes during the COVID as compared to pre-pandemic. This study fills an information gap and reports total fire incidences, total burnt area, type of vegetation burnt, and total particulate matter emission variations in SA/SEA during COVID-2020 and pre-pandemic (2012–2019). Results from the short-term 2020-COVID versus 2019-non-COVID year showed a decline in fire counts varying from − 2.88 to 79.43% in S/SEA. The exceptions in South Asia include Afghanistan and Sri Lanka, with a 152% and 4.9% increase, and Cambodia and Myanmar in Southeast Asia, with an 11.1% and 8.5% increase in fire counts in the 2020-COVID year. The burnt area decline for 2020 compared to 2019 varied from − 0.8% to 92% for South/Southeast Asian countries, with most burning in agricultural landscapes than forests. Several patches in S/SEA showed a decrease in fires for the 2020 pandemic year compared to long term 2012–2020 pre-pandemic record, with Z scores greater or less than two denoting statistical significance. However, on a country scale, the results were not statistically significant in both S/SEA, with Z scores ranging from − 0.24 to − 1, although most countries experienced a decrease in fire counts. The associated mean TPM emissions declined from ~ 2.31 Tg (0.73stdev) during 2012–2019 to 2.0 (0.65stdev)Tg in 2020 in South Asia and 6.83 (0.70stdev)Tg during 2012–2019 to 5.71 (0.69 stdev)Tg in 2020 for South East Asian countries. The study highlights variations in fires and emissions useful for fire management and mitigation.

Exceptional fire activity in 2019 sparked concern about Amazon forest conservation. However, the inability to rapidly separate satellite fire detections by fire type hampered fire suppression and assessment of ecosystem and air quality impacts. Here, we describe the development of a near–real-time approach for tracking contributions from deforestation, forest, agricultural, and savanna fires to burned area and emissions and apply the approach to the 2019 fire season in South America. Across the southern Amazon, 19,700 deforestation fire events accounted for 39% of all satellite active fire detections and the majority of fire carbon emissions (63%; 69 Tg C). Multiday fires accounted for 81% of burned area and 92% of carbon emissions from the Amazon, with many forest fires burning uncontrolled for weeks. Most fire detections from deforestation fires were correctly identified within 2 days (67%), highlighting the potential to improve situational awareness and management outcomes during fire emergencies.

To pursue the development and validation of coupled fire-atmosphere models, the wildland fire modeling community needs validation data sets with scenarios where fire-induced winds influence fire front behavior, and with high temporal and spatial resolution. Helicopter-borne infrared thermal cameras have the potential to monitor landscape-scale wildland fires at a high resolution during experimental burns. To extract valuable information from those observations, three-step image processing is required: (a) Orthorectification to warp raw images on a fixed coordinate system grid, (b) segmentation to delineate the fire front location out of the orthorectified images, and (c) computation of fire behavior metrics such as the rate of spread from the time-evolving fire front location. This work is dedicated to the first orthorectification step, and presents a series of algorithms that are designed to process handheld helicopter-borne thermal images collected during savannah experimental burns. The novelty in the approach lies on its recursive design, which does not require the presence of fixed ground control points, hence relaxing the constraint on field of view coverage and helping the acquisition of high-frequency observations. For four burns ranging from four to eight hectares, long-wave and mid infra red images were collected at 1 and 3 Hz, respectively, and orthorectified at a high spatial resolution (<1 m) with an absolute accuracy estimated to be lower than 4 m. Subsequent computation of fire radiative power is discussed with comparison to concurrent space-borne measurements.

Landscape fire is a widespread, somewhat unpredictable phenomena that plays an important part in Earth's biogeochemical cycling. In many biomes worldwide fire also provides multiple ecological benefits, but in certain circumstances can also pose a risk to life and infrastructure, lead to net increases in atmospheric greenhouse gas concentrations, and to degradation in air quality and consequently human health. Accurate, timely and frequently updated information on landscape fire activity is essential to improve our understanding of the drivers and impacts of this form of biomass burning, as well as to aid fire management. This information can only be provided using satellite Earth Observation (EO) approaches, and remote sensing of active fire is one of the key techniques used. This form of EO is based on detecting the signature of the (mostly infrared) electromagnetic radiation emitted as biomass burns. Since the early 1980's, active fire (AF) remote sensing conducted using low Earth orbit (LEO) satellites has been deployed in certain regions of the world to map the location and timing of landscape fire occurrence, and from the early 2000's global-scale information updated multiple times per day has been easily available to all. Geostationary (GEO) satellites provide even higher frequency AF information, more than 100 times per day in some cases, and both LEO- and GEO-derived AF products now often include estimates of a fires characteristics, such as its fire radiative power (FRP) output, in addition to the fires detection. AF data provide information relevant to fire activity ongoing when the EO data were collected, and this can be delivered with very low latency times to support applications such as air quality forecasting. Here we summarize the history of achievements in the field of active fire remote sensing, review the physical basis of the approaches used, the nature of the AF detection and characterization techniques deployed, and highlight some of the key current capabilities and applications. Finally, we list some important developments we believe deserve focus in future years.

VIIRS 375 m active fire data (VNP14IMG), the highest spatial resolution available cost-free fire product, were assessed for representing fire in typical degraded tropical peatlands in Indonesia. The results of applying the Tropical Peatland Combustion Algorithm to Landsat-8 (ToPeCAl-L8) daytime imagery were utilised as the fire references. To permit the comparison of non-simultaneous VNP14IMG and ToPeCAl-L8, peatland fire propagation speeds resulting from previous study using TET-1 data in Central Kalimantan's peatlands were utilised. Most peatland fires were still within 750 m from their source over 15 h under uniform conditions, except for very large fires. The detection rates of nighttime VNP14IMG compared with ToPeCAl-L8 showed about 80% agreement for small fire areas (< 14 ha). For fires larger than 14 ha, a dissolved 375 m buffer (cluster buffer) of VNP14IMG active fires with an integration of nighttime and daytime acquisitions, produced a probability of detection up to 90%. These results generated a recommendation for implementing cluster buffer analysis and integration of nighttime and daytime analysis of VNP14IMG data for better accuracy in fire detection for ground fire management. They also demonstrate the utility of the ToPeCAl-L8 algorithm with VIIRS 375 m active fire data.

The understanding and prediction of large wildland fire events around the world is a growing interdisciplinary research area advanced rapidly by development and use of computational models. Recent models bidirectionally couple computational fluid dynamics models including weather prediction models with modules containing algorithms representing fire spread and heat release, simulating fire-atmosphere interactions across scales spanning three orders of magnitude. Integrated with weather data and airborne and satellite remote sensing data on wildland fuels and active fire detection, modern coupled weather-fire modeling systems are being used to solve current science problems. Compared to legacy tools, these dynamic computational modeling systems increase cost and complexity but have produced breakthrough insights notably into the mechanisms underlying extreme wildfire events such as fine-scale extreme winds associated with interruptions of the electricity grid and have been configured to forecast a fire's growth, expanding our ability to anticipate how they will unfold. We synthesize case studies of recent extreme events, expanding applications, and the challenges and limitations in our remote sensing systems, fire prediction tools, and meteorological models that add to wildfires' mystery and apparent unpredictability.

Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities.

Forest fires annually destroy extensive areas of vegetation, causing great environmental and economic damages. Many products derived from satellite observations have been used to monitor fire events. In Brazil, the Queimadas Program from INPE develops applications for the daily operational monitoring of hot spots detected by satellites. These detections are obtained with measurements from different sensors, which require specific algorithms and calibration parameters. The use of thermal sensor coupled to drone allows obtaining parameters with adequate spatial resolution. However, there are still no defined routines for obtaining these measures. The aim of this work is to propose a procedures protocol for the use of thermal sensor coupled to drone in experiments for validate the detection of hot spots by satellites. Based on our field experiences it is first necessary to have support from brigadiers, to instruct the teams with safety procedures to carry out the experiments. Secondly, i) to request authorization for the drone flights, in the SARPAS / DECEA system; ii) to check the weather conditions (cloud cover, wind speed and direction, etc.); iii) to determine the size of the burned area, which is inversely proportional to the spatial resolution of the satellite sensor studied; iv) to define the height of the drone's positioning, depending on the dimension of the chosen area and the sensor's field of view, and; v) to determine the sequence of the drone activation time, the start of the burning and the satellite imagery over the location. Finally, it is necessary to obtain quality thermal measures: i) to start the fire in advance for arrive to high temperatures during satellite imaging in the place of interest; ii) to consider the time to stabilize the drone and start acquiring the thermal data, before and after the peak of the satellite passage, and; iii) after the satellite has passed, to use the drone for measure the surrounding temperature (radius = ~ 200m), obtaining a temperature reference in the surrounding areas. This protocol aims to standardize the experiments, improving the detection algorithms and providing improvements in the products presented in the Queimadas Program database.

Biomass burning on natural and agricultural lands has profound effects on atmospheric chemistry, climate, and air quality. Over the past decade, a number of global fire emissions inventories have been developed based on near-real time detection of actively burning fires by the MODIS instruments. However, the MODIS instruments provide variable and incomplete global sampling of fire activity, resulting in large uncertainty in the spatiotemporal accuracy of daily emissions inventories. Here, we compared active fire products from MODIS and VIIRS to characterize product-specific shortcomings of each system with the goal to develop a new, more accurate, global emissions inventory. The VIIRS 375m product was most sensitive to global fire activity and detected up to 55% more energy release from fires than the comparable 1km MODIS product in the tropics. Differences originated from improved coverage, sensitivity to low energy fires, and a more consistent cross-track spatial resolution. Nevertheless, both MODIS and VIIRS instruments showed reduced sensitivity to low energy fires at larger off-nadir angles, resulting in a cyclical pattern of daily fire detections and an underestimate of low energy fires, the dominant firetype in shoulder seasons and more densely populated regions. Starting in 2018, the constellation of VIIRS instruments aboard NOAA-20 and SNPP provide improved near-nadir coverage, largely eliminating issues originating from incomplete sampling of low-energy fires at the edge of the VIIRS image swath. Based these findings, we developed a new near-real time emissions inventory that is spatially consistent with the GFED4s data record (1997-2016). Spatial allocation of emissions in the new GFED near-real time product differ considerably from existing daily emissions inventories, highlighting how different methodologies redistribute emissions across natural and human dominated landscapes based on daily active fire detections. Using column observations of NO 2 and daily fire expansion rates from USGS, we demonstrate that the new, VIIRS-based daily fire emissions product provides more consistent spatial and temporal distribution of fire emissions compared to systems based on MODIS active fire detections. Improved accuracy is critical for air quality forecasts, source attribution, and the development of management strategies to minimize impacts on society.

Global biomass burning impacts millions of hectares annually resulting in high social, environmental and economic costs. Satellite-based active fire detection products provide key information in support of land management and science applications and are available routinely from a variety of sources. Data validation is an important aspect guiding product development and characterization and is addressed in this study with the use of miniaturized sensors paired to unmanned airborne vehicles, or drones. Specifically, we deployed a custommade broadband spectral radiometer along with a commercial off-the-shelf infrared camera (FLIR Zenmuse XT) mounted to small consumer drones (DJI's Phantom3 and Inspire) flown over small prescribed burns implemented so as to coincide with the overpass times of different earth observing satellites (e.g., NASA Terra & Aqua, NOAA/NASA S-NPP, USGS Landsat-8, and ESA Sentinel-2). Near-simultaneous fire radiative power retrievals were obtained using the airborne and spaceborne data acquired during prescribed fires conducted in grasslands and savannas plots in the Brazilian states of Rio de Janeiro, Tocantins and Mato Grosso do Sul between July 2017 and September 2018. A set of standard operating procedures were defined with attention to satellite active fire data validation requirements (e.g., reference data calibration) and subsequently adopted for each of the fires sampled. Airborne and spaceborne observations were co-located and temporally paired to within 2sec, and path transmittances calculated in order to account for atmospheric attenuation of fire retrievals. Our results showed good agreement (differences as low as 5%) between drone and satellite-based fire retrievals while also serving to demonstrate the potential for fully reproducible satellite data validation protocols using small sensor and drone technologies.

Os incêndios florestais destroem anualmente extensas áreas de vegetação, causando grandes prejuízos ambientais e econômicos. Muitos produtos derivados das observações de satélites têm sido utilizados para monitorar eventos de fogo. No Brasil, o Programa Queimadas/INPE desenvolve aplicações para o monitoramento operacional diário dos focos de queimadas detectados por satélites. Estas detecções são obtidas com medidas de diferentes sensores, os quais requerem algoritmos específicos e parâmetros de calibração. O uso de sensor termal acoplado em drone permite obter parâmetros com resolução espacial adequada. Porém, ainda não existem rotinas definidas para a obtenção dessas medidas. O objetivo deste trabalho é propor um protocolo de procedimentos para o uso de drone com sensor termal em experimentos de validação da detecção de focos de calor, obtidos por satélites. Com base na experiência de campo, para realizar os experimentos é necessário ter apoio de Brigadistas, instruir as equipes com procedimentos de segurança e: i) solicitar autorização para os voos, no sistema SARPAS/DECEA; ii) verificar as condições meteorológicas (cobertura de nuvens, velocidade e direção do vento, etc); iii) determinar o tamanho da área de queima, que é inversamente proporcional à resolução espacial do sensor do satélite de interesse; iv) definir a altura do posicionamento do drone, em função da dimensão da área escolhida e do campo de visão do sensor, e; v) determinar a sequência do horário do acionamento do drone, do início da queima e do imageamento do satélite sobre o local. Para obter as medidas termais com qualidade é necessário: i) iniciar o fogo com antecedência, para tentar alcançar temperaturas elevadas durante o imageamento pelo satélite sobre o local de interesse; ii) considerar o tempo para estabilização do drone e início da aquisição dos dados termais, antes e depois do ápice da passagem do satélite, e; iii) após a passagem do satélite, utilizar o drone para realizar as medidas da temperatura do entorno (raio = ~200m), obtendo referência da temperatura nas áreas circunvizinhas. Este protocolo visa a padronização dos experimentos, para aprimorar os algoritmos de detecção e proporcionar melhorias nos produtos apresentados no banco de dados do Programa Queimadas.

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003–2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012–2016) in South/Southeast Asia (S/SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries.

The recent deployment of the next generation of geostationary weather satellites provides an opportunity for the establishment of a robust global network of geostationary fire data that can greatly complement existing polar-orbiting satellite fire products. Among other benefits, geostationary satellites provide frequent sampling of diurnal variations in fire activity. Building on established satellite active fire data validation protocols, we used Landsat-8 Operational Land Imager (OLI) as reference fire data to validate the fire products derived from two geostationary satellite sensors: the Advanced Baseline Imager (ABI) on board the National Oceanic and Atmospheric Administration (NOAA) GOES-16 satellite (launched November 2016), and the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI), on board the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Second Generation (MSG) satellite series (multiple launch dates). The two primary algorithms associated with these geostationary active fire data sets are the Fire Detection and Characterization (FDC) product based on the heritage Wildfire Automated Biomass-Burning Algorithm (WF-ABBA) for the GOES-series, and the Fire Radiative Power (FRP-PIXEL) product based on the Fire Thermal Anomaly algorithm (FTA) for the MSG series. Our standardized validation method allowed for a direct inter-comparison between the complementary active fire datasets. Specifically, we present an error assessment of the detection probability (omission error) and false alarm rate (commission error) for two periods in 2017 and 2018 that include extensive fire activity in the respective full-disk sectors covered by each product. The results highlight (i) the restrictiveness of the FRP-PIXEL product (98% omission error) compared to the FDC product (84% omission error), and (ii) the elevated false alarm rate of FDC (88% commission error) compared to FRP-PIXEL (8% commission error). These validation results will be used to help support the development of a harmonized global multi-sensor active fire dataset to be integrated into the Global Wildfire Information System (GWIS).

Knowledge about the current fire dynamics in the Brazilian Savannas (Cerrado) relies heavily on satellite-derived burned area (BA) products applied at the biome level. Nevertheless, there is still a lack of studies analyzing the consistency of available available satellite products concerning BA location and extension for the region. Accordingly, we performed an accuracy assessment of the MODerate resolution Imaging Spectroradiometer (MODIS) collection 6 BA product (MCD64 /C6) over 222,768,000 ha encompassing the Brazilian Cerrado. We used reference data derived from Landsat-8 OLI to perform an intercomparison of MCD64/C6 with 1) the previous collection 5.1 (C5.1); 2) independent active fires from the Visible Infrared Imaging Radiometer Suite (VIIRS); and 3) recent land use patterns. The results of the comparison between C6 and C5.1 indicate that the new collection decreases the omission error in 90% of the analyzed area and increases the burn hits, providing improved BA estimates in 61% of the region. However, the MCD64 product increases the overall commission errors in 74% of the area. The MCD64/C6 product showed a high coefficient of correlation with active fires independently detected by VIIRS (tau = 0.74). For both MCD64 collections 5.1 and 6, the different accuracy assessment measures exhibited a marked performance deterioration from the north towards the south. The largest bum scars and total affected areas occur mainly across the northern Cerrado, explaining the better performance in that area. Conversely, greater inaccuracies were found in the southern Cerrado area, where natural vegetation has been converted into pasture and cropland, leading to fragmented landscapes and small fire patches. Finally, the BAs mapped by both collections were similar in location albeit divergent in the magnitude, with C6 detecting 21% more area than C5.1 during the year 2015.

On 8–9 October 2017, fourteen wildfires developed rapidly during a strong Diablo wind event in northern California including the Tubbs Fire, which travelled over 19 km in 3.25 h. Here, we applied the CAWFE® coupled numerical weather prediction-fire modeling system to investigate the airflow regime and extreme wind peaks underlying the extreme fire behavior using simulations that refine from a 10 km to a 185 m horizontal grid spacing. We found that as Diablo winds travelled south down the Sacramento Valley and fanned out southwestward over the Wine Country, their strength waxed and waned and their direction wavered, creating varying locations near fire origins where wind overrunning topography reached 30–40 m/s, along with streaks and bursts of strong winds in the lee of some topographic features and stagnation downstream of others. Despite a statically stable layer in the lowest 1.5 km, the high Froude number flow sometimes resembled a hydraulic jump. Elsewhere, the flow behaved similarly to neutrally-stratified flow over small hills, creating wind extrema that exceeded 40 m/s at the crest of some lesser hills including near the Tubbs fire ignition, but which shed bursts of high speed winds that travel downstream at approximately 5–7-min intervals. Nonetheless, simulated fire growth lagged satellite detection of fire arrival in Santa Rosa by up to 1 h, although whether the data detect fire line or spotting is ambiguous. A forecast simulation with a 370 m horizontal grid spacing produced an on-time fire line arrival in Santa Rosa, with calculations executed 4 times faster than real time on a single computer processor.