scholarly journals Identifying required model structures to predict global fire activity from satellite and climate data

2016 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Satellite Observations ◽  
Burned Area ◽  
Human Society ◽  
Fire Emissions ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation ◽  
Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

scholarly journals A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

2017 ◽  
Vol 10 (12) ◽  
pp. 4443-4476 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Observations ◽  
Data Driven ◽  
Predictor Variables ◽  
Burned Area ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.

scholarly journals Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

2020 ◽  
Vol 20 (2) ◽  
pp. 969-994 ◽  
Author(s):  
Xiaohua Pan ◽  
Charles Ichoku ◽  
Mian Chin ◽  
Huisheng Bian ◽  
Anton Darmenov ◽  
...  
Keyword(s):  
Southern Hemisphere ◽  
Biomass Burning ◽  
Satellite Observations ◽  
Burned Area ◽  
Aerosol Model ◽  
Fire Emissions ◽  
Active Fire ◽  
Aerosol Emission ◽  
High Level ◽  
Aerosol Emissions

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 regions. The six BB emission datasets are (1) GFED3.1 (Global Fire Emissions Database version 3.1), (2) GFED4s (GFED version 4 with small fires), (3) FINN1.5 (FIre INventory from NCAR version 1.5), (4) GFAS1.2 (Global Fire Assimilation System version 1.2), (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). The global total emission amounts from these six BB emission datasets differed by a factor of 3.8, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most of the regions, QFED2.4 and FEER1.0, which are based on satellite observations of fire radiative power (FRP) and constrained by aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB aerosol emissions than the rest by a factor of 2–4. By comparison, the BB aerosol emissions estimated from GFED4s and GFED3.1, which are based on satellite burned-area data, without AOD constraints, were at the low end of the range. In order to examine the sensitivity of model-simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and the Multiangle Imaging SpectroRadiometer (MISR) in the 14 regions during 2008. In Southern Hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD values were underestimated in almost all experiments compared to MISR, except for the QFED2.4 run in SHSA. The model-simulated AOD values based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being, respectively, about 73 % and 100 % of the AERONET observed AOD at Alta Floresta in SHSA and about 49 % and 46 % at Mongu in SHAF. The simulated AOD based on the other four BB emission datasets accounted for only ∼50 % of the AERONET AOD at Alta Floresta and ∼20 % at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD values simulated with QFED2.4 were the highest and closest to AERONET and MISR observations, followed closely by FEER1.0. However, the QFED2.4 run tends to overestimate AOD in the region of SHSA, and the QFED2.4 BB emission dataset is tuned with the GEOS model. In contrast, the FEER1.0 BB emission dataset is derived in a more model-independent fashion and is more physically based since its emission coefficients are independently derived at each grid box. Therefore, we recommend the FEER1.0 BB emission dataset for aerosol-focused hindcast experiments in the two biomass-burning-dominated regions in the Southern Hemisphere, SHAF, and SHSA (as well as in other regions but with lower confidence). The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.

Fire - climate interactions in a warming world

2020 ◽  
Author(s):  
Guido van der Werf ◽  
James Randerson ◽  
Louis Giglio ◽  
Dave van Wees ◽  
Niels Andela ◽  
...  
Keyword(s):  
Climate Change ◽  
The Arctic ◽  
Burned Area ◽  
Global Pattern ◽  
Fire Emissions ◽  
Fuel Loads ◽  
Fire Activity ◽  
Rate Of Decline ◽  
Fire Climate Interactions ◽  
Warming World

<p>Elevated fire activity in 2019 across the arctic, Amazon, Australia, and other regions sparked a discussion about the role of climate change for the recent rise in biomass burning.  Given that drivers of fire vary widely between different fire types and regions, interpreting trends requires a regional breakdown of the global pattern. Our Global Fire Emissions Database (GFED) now provides nearly 25 years of consistent data and offers important insights into changing fire activity. The GFED record captures a global decline in burned area, driven mostly by reductions in savanna fires from fragmentation and land use change. The global declining trend is therefore driven by areas with relatively low fuel loads where fire often decreases during drought.  Here, we report on increasing fire trends in several other regions, which become even more apparent when proxy data from before the satellite era are included. Increasing trends are concentrated in areas with higher fuel loads that burn more easily under drought conditions, and where warming leads to increasing vapor pressure deficits that contribute to more extreme fire weather and higher combustion completeness values. Therefore, the rate of decline in fire emissions is less pronounced than that in burned area, and emissions of several reduced gases have actually increased over time. The historic time series provides important context for trends and drivers of regions that burned extensively in 2019, and moving beyond burned area to estimate fire emissions of greenhouse gases and aerosols is critical to assess how these events may feed back on climate change if trends continue.     </p>

scholarly journals Wetter environment and increased grazing reduced the area burned in northern Eurasia: 2002–2016

2020 ◽  
Author(s):  
Wei Min Hao ◽  
Matthew C. Reeves ◽  
L. Scott Baggett ◽  
Yves Balkanski ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Driving Forces ◽  
The Arctic ◽  
Burned Area ◽  
Northern Eurasia ◽  
Fire Dynamics ◽  
Data Set ◽  
Fire Emissions ◽  
Area Burned ◽  
Fire Activity

Abstract. Northern Eurasia is highly sensitive to climate change. Fires in this region can have significant impacts on regional air quality, radiative forcing and black carbon deposition in the Arctic to accelerate ice melting. Using a MODIS-derived burned area data set, we report that the total annual area burned in this region declined by 53 % during the 15-year period of 2002–2016. Grassland fires dominated the trend, accounting for 93 % of the decline of the total area burned. Grassland fires in Kazakhstan contributed 47 % of the total area burned and 84 % of the decline. Wetter climate and increased grazing are the principle driving forces for the decline. Our findings: 1) highlight the importance of the complex interactions of climate-vegetation-land use in affecting fire activity, and 2) reveal how the resulting impacts on fire activity in a relatively small region such as Kazakhstan can dominate the trends of burned areas across a much larger landscape of northern Eurasia. Our findings may be used to improve the prediction of future fire dynamics and associated fire emissions in northern Eurasia.

scholarly journals The Fire Modeling Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols

2016 ◽  
Author(s):  
Sam S. Rabin ◽  
Joe R. Melton ◽  
Gitta Lasslop ◽  
Dominique Bachelet ◽  
Matthew Forrest ◽  
...  
Keyword(s):  
Phase 1 ◽  
Model Performance ◽  
Fire Models ◽  
Intercomparison Project ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
First Time ◽  
Model Structures

Abstract. The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models.

Comparison of burned area mapping products and combustion efficiency approaches for estimating GHG and particulate emissions from Italian fires

2020 ◽  
Author(s):  
Valentina Bacciu ◽  
Carla Scarpa ◽  
Costantino Sirca ◽  
Spano Donatella
Keyword(s):  
Fuel Consumption ◽  
Combustion Efficiency ◽  
Emission Factors ◽  
Weather Data ◽  
Burned Area ◽  
Particulate Emissions ◽  
Management Service ◽  
Specific Chemical ◽  
Fire Emissions ◽  
Fire Activity

<p>Vegetation fires contribute to 38% to the emission of CO<sub>2</sub> into the atmosphere, against 62% caused by the combustion of fossil fuels. Further, it could approach levels of anthropogenic carbon emissions, especially in years of extreme fire activity (e.g. 2003, 2017). According to the equation first proposed by Seiler and Crutzen (1980), fire emission estimation use information on the amount of burned biomass, the emission factors associated with each specific chemical species, the burned area, and the combustion efficiency. Still, simulating emission from forest fires is affected by several errors and uncertainties, due to the different assessment approach to characterize the various parameters involved in the equation. For example, regional assessment relied on fire-activity reports from forest services, with assumptions regarding the type of vegetation burned, the characteristics of burning, and the burned area. Improvements and new advances in remote sensing, experimental measurements of emission factors, fuel consumption models, fuel load evaluation, and spatial and temporal distribution of burning are a valuable help for predicting and quantifying accurately the source and the composition of fire emissions.</p><p>With the aim to contribute to a better estimation of biomass burning emission, in this work we compared fire emission estimations using two different types of burned area products and combustion efficiency approaches in the framework of the recently developed system for modeling fire emission in Italy (Bacciu et al., 2012). This methodology combines a fire emission model (FOFEM - First Order Fire Effect Model, Reinhardt et al., 1997) with spatial and non-spatial inputs related to fire, vegetation, and weather conditions. The perimeters and burned area of selected large fires that occurred in 2017 in Italy were obtained by the former Corpo Forestale dello Stato (actually Carabinieri C.U.F.A.A.) and by the Copernicus Emergency Management Service (EMS). The vegetation types were derived from CORINE LAND COVER (2012). For each vegetation type, fuel loading was assigned using a combination of field observations and literature data (e.g., Mitsopoulos and Dimitrakopoulos 2007; Ascoli et al., 2019). Fuel moisture conditions, influencing the combustion efficiency, were derived from the daily Canadian Fine Fuels Moisture Code (FFMC), calculated from MARS interpolated weather data (25km x 25km). The daily FFMC was then associated with the two types of fire data with the aim of group fires in function of their relative ease of ignition and flammability of fine fuel (burning conditions, from low to extreme). For the EMS fire, it was also possible to further define fire severity and thus the percentage of combusted crown through the assessed fire damage grade.</p><p>The results showed differences in the total emissions according to the fire product and the approach to estimate the combustion efficiency. Furthermore, it seems that the difference in the evaluation of severity - and therefore in the degree of combustion of the canopy- affects more than the differences in terms of area burned. Overall, the results pointed out the crucial role of appropriate fuel, fire, and weather data and maps to attain reasonable simulations of fuel consumption and smoke emissions.</p>

scholarly journals Satellite-based assessment of climate controls on US burned area

2012 ◽  
Vol 9 (6) ◽  
pp. 7853-7892 ◽  
Author(s):  
D. C. Morton ◽  
G. J. Collatz ◽  
D. Wang ◽  
J. T. Randerson ◽  
L. Giglio ◽  
...  
Keyword(s):  
Shortwave Radiation ◽  
Fire Season ◽  
Burned Area ◽  
Strongly Correlated ◽  
Climate Data ◽  
Spatial And Temporal Patterns ◽  
Fire Emissions ◽  
Mitigation And Adaptation ◽  
Fire Activity ◽  
Climate Controls

Abstract. Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate-fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997–2010) and Monitoring Trends in Burn Severity (MTBS, 1984–2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5° resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE} and fire activity in the Alaska, while water deficit (precipitation – PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6–12 months). Fire season PE increased from the 1980s–2000s, enhancing climate-driven fire risk in the southern and western US where PE-BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990s–2000s highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climate-fire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity.

scholarly journals Six Global Biomass Burning Emission Datasets: Inter-comparison and Application in one Global Aerosol Model

2019 ◽  
Author(s):  
Xiaohua Pan ◽  
Charles Ichoku ◽  
Mian Chin ◽  
Huisheng Bian ◽  
Anton Darmenov ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Satellite Observations ◽  
Burned Area ◽  
Aerosol Model ◽  
Fire Emissions ◽  
Earth Observing System ◽  
Active Fire ◽  
Aerosol Emission ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Level

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 sub-regions. The six BB emission datasets are: (1) GFED3.1 (Global Fire Emissions Database version 3.1); (2) GFED4s (Global Fire Emissions Database version 4 with small fires); (3) FINN1.5 (Fire INventory from NCAR version 1.5); (4) GFAS1.2 (Global Fire Assimilation System version 1.2); (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). Although biomass burning emissions of aerosols from these six BB emission datasets showed similar spatial distributions, their global total emission amounts differed by a factor of 3–4, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most regions, QFED2.4 and FEER1.0, which are based on the satellite observations of fire radiative power (FRP) and utilize the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB emissions than the rest by a factor of 2–4. In comparison, the BB emission from GFED4s and GFED3.1, which are based on satellite retrieval of burned area and no AOD constraints, were at the low end of the range. In order to examine the sensitivity of model simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and MODIS in 14 sub-regions during 2008. In Southern hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD were underestimated in all experiments. More specifically, the model-simulated AOD based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being about 73 % and 100 % of the AERONET observed AOD at Alta-Floresta in SHSA, 49 % and 46 % at Mongu in SHAF, respectively. The simulated AOD based on the other four BB emission datasets accounted for only ~ 50 % of the AERONET AOD at Alta Floresta and ~ 20 % of at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD simulated with QFED2.4 were the highest and closest to AERONET and MODIS observations, followed closely by FEER1.0. The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.  

scholarly journals Monitoring emissions from the 2015 Indonesian fires using CO satellite data

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170307 ◽  
Author(s):  
Narcisa Nechita-Banda ◽  
Maarten Krol ◽  
Guido R. van der Werf ◽  
Johannes W. Kaiser ◽  
Sudhanshu Pandey ◽  
...  
Keyword(s):  
Satellite Data ◽  
El Niño ◽  
El Nino ◽  
Air Pollutant ◽  
Satellite Observations ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Burned Area ◽  
Fire Emissions ◽  
The Impact

Southeast Asia, in particular Indonesia, has periodically struggled with intense fire events. These events convert substantial amounts of carbon stored as peat to atmospheric carbon dioxide (CO 2 ) and significantly affect atmospheric composition on a regional to global scale. During the recent 2015 El Niño event, peat fires led to strong enhancements of carbon monoxide (CO), an air pollutant and well-known tracer for biomass burning. These enhancements were clearly observed from space by the Infrared Atmospheric Sounding Interferometer (IASI) and the Measurements of Pollution in the Troposphere (MOPITT) instruments. We use these satellite observations to estimate CO fire emissions within an inverse modelling framework. We find that the derived CO emissions for each sub-region of Indonesia and Papua are substantially different from emission inventories, highlighting uncertainties in bottom-up estimates. CO fire emissions based on either MOPITT or IASI have a similar spatial pattern and evolution in time, and a 10% uncertainty based on a set of sensitivity tests we performed. Thus, CO satellite data have a high potential to complement existing operational fire emission estimates based on satellite observations of fire counts, fire radiative power and burned area, in better constraining fire occurrence and the associated conversion of peat carbon to atmospheric CO 2 . A total carbon release to the atmosphere of 0.35–0.60 Pg C can be estimated based on our results. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

scholarly journals New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations

2015 ◽  
Vol 15 (15) ◽  
pp. 8831-8846 ◽  
Author(s):  
N. Andela ◽  
J. W. Kaiser ◽  
G. R. van der Werf ◽  
M. J. Wooster
Keyword(s):  
Diurnal Cycle ◽  
Radiative Energy ◽  
Burned Area ◽  
Sampled Data ◽  
Fire Event ◽  
Data Set ◽  
Study Region ◽  
Fire Emissions ◽  
Active Fire ◽  
Fire Activity

Abstract. Accurate near real time fire emissions estimates are required for air quality forecasts. To date, most approaches are based on satellite-derived estimates of fire radiative power (FRP), which can be converted to fire radiative energy (FRE) which is directly related to fire emissions. Uncertainties in these FRE estimates are often substantial. This is for a large part because the most often used low-Earth orbit satellite-based instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) have a relatively poor sampling of the usually pronounced fire diurnal cycle. In this paper we explore the spatial variation of this fire diurnal cycle and its drivers using data from the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). In addition, we sampled data from the SEVIRI instrument at MODIS detection opportunities to develop two approaches to estimate hourly FRE based on MODIS active fire detections. The first approach ignored the fire diurnal cycle, assuming persistent fire activity between two MODIS observations, while the second approach combined knowledge on the climatology of the fire diurnal cycle with active fire detections to estimate hourly FRE. The full SEVIRI time series, providing full coverage of the fire diurnal cycle, were used to evaluate the results. Our study period comprised of 3 years (2010–2012), and we focused on Africa and the Mediterranean basin to avoid the use of potentially lower quality SEVIRI data obtained at very far off-nadir view angles. We found that the fire diurnal cycle varies substantially over the study region, and depends on both fuel and weather conditions. For example, more "intense" fires characterized by a fire diurnal cycle with high peak fire activity, long duration over the day, and with nighttime fire activity are most common in areas of large fire size (i.e., large burned area per fire event). These areas are most prevalent in relatively arid regions. Ignoring the fire diurnal cycle generally resulted in an overestimation of FRE, while including information on the climatology of the fire diurnal cycle improved FRE estimates. The approach based on knowledge of the climatology of the fire diurnal cycle also improved distribution of FRE over the day, although only when aggregating model results to coarser spatial and/or temporal scale good correlation was found with the full SEVIRI hourly reference data set. We recommend the use of regionally varying fire diurnal cycle information within the Global Fire Assimilation System (GFAS) used in the Copernicus Atmosphere Monitoring Services, which will improve FRE estimates and may allow for further reconciliation of biomass burning emission estimates from different inventories.

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