ALBERT

Description: Wildfires in the United Kingdom (UK) can pose a threat to people, infrastructure and the natural environment (e.g. to the carbon in peat soils), and their simultaneous occurrence within and across UK regions can periodically place considerable stress upon the resources of Fire and Rescue Services. "Fire danger" rating systems (FDRS) attempt to anticipate periods of heightened fire risk, primarily for early-warning purposes. The UK FDRS, termed the Met Office Fire Severity Index (MOFSI) is based on the Fire Weather Index (FWI) component of the Canadian Forest FWI System. MOFSI currently provides operational mapping of landscape fire danger across England and Wales using a simple thresholding of the final FWI component of the Canadian System. Here we explore a climatology of the full set of FWI System components across the entire UK (i.e. extending to Scotland and Northern Ireland), calculated from daily 2 km gridded numerical weather prediction data, supplemented by meteorological station observations. We used this to develop a percentile-based calibration of the FWI System optimised for UK conditions. We find the calibration to be well justified, since for example the values of the "raw" uncalibrated FWI components corresponding to a very "extreme" (99th percentile) fire danger situation can vary by up to an order of magnitude across UK regions. Therefore, simple thresholding of the uncalibrated component values (as is currently applied) may be prone to large errors of omission and commission with respect to identifying periods of significantly elevated fire danger compared to "routine" variability. We evaluate our calibrated approach to UK fire danger rating against records of wildfire occurrence, and find that the Fine Fuel Moisture Code (FFMC), Initial Spread Index (ISI) and final FWI component of the FWI system generally have the greatest predictive skill for landscape fires in Great Britain, with performance varying seasonally and by land cover type. At the height of the most recent severe wildfire period in the UK (2 May 2011), 50 % of all wildfires occurred in areas where the FWI component exceeded the 99th percentile, and for each of the ten most serious wildfire events that occurred in the 2010–2012 period, at least one FWI component per event was found to surpass the 95th percentile. Overall, we demonstrate the significant advantages of using a calibrated, percentile-based approach for classifying UK fire danger, and believe our findings provide useful insights for any future redevelopment of the current operational UK FDRS.

Description: Fires associated with land use and land cover changes release into the atmosphere large amounts of aerosols and trace gases. Although there are several inventories of biomass burning emissions covering Brazil, there are still considerable uncertainties and differences among these. While most fire emissions inventories still utilize the parameters of vegetation fuel load, emission factors and other parameters to estimate the biomass burned and its associated emissions, certain more recent inventories tend to apply an alternative method based on fire radiative power (FRP) observations to estimate the amount of biomass burned and the corresponding emissions of trace gases and aerosols. The Brazilian Biomass Burning Emission Model (3BEM) and Fire Inventory from NCAR (FINN) are examples of the first, while Brazilian Biomass Burning Emission Model with FRP assimilation (3BEM_FRP) and Global Fire Assimilation System (GFAS) are examples of the latter method mentioned. In this paper, the output of four biomass burning emission inventories used during the South American Biomass Burning Analysis (SAMBBA) field campaign are analyzed and intercompared, focusing on eight predefined grids. Aerosol optical thickness derived from measurements made by the MODIS sensor operating onboard the Aqua satellite is applied to assess the inventories consistency. Significant correlation coefficients (r, p〉0.05 level, Student t-test) were found between 3BEM and FINN, and between 3BEM_FRP and GFAS, with approximately 0.86 and 0.85, respectively. These results indicate that emissions estimates in this region derived via similar methods tend to agree with one other, but differ more from the estimates derived via the alternative approach. However, correlations in specific grids indicate that 3BEM and FINN typically underestimated the smoke emission loading in the eastern region of Amazon Forest, whilst 3BEM_FRP presents a tendency to overestimate fire emissions in the same area. The relationship between the 3BEM and FINN fire inventories present a correlation coefficient of 0.75-0.92, with a tendency of FINN to overestimate the emission of carbon monoxide by 20-30%. Moreover, 3BEM and GFAS shows a correlation coefficient of between 0.75-0.85, with higher values near the arc of deforestation in Amazon Rainforest. However, GFAS has a tendency to present higher carbon monoxide emissions in this region, and 3BEM_FRP tends to overestimate the emissions in soybean expansion (east of Amazon forest).

Description: Characterising the dynamics of landscape scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and northern and southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP dataset, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 and 65–77% respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35% over the Northern Africa region to 89% over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America, and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 Peloponnese wildfires within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS), as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring System (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions datasets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET AOD indicates that the former is overestimated by ∼ 20–30%, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those currently implemented as part of the Monitoring Atmospheric Composition and Climate (MACC) programme within the CAMS.

Description: Characterising changes in landscape scale fire activity at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from these types of geostationary observations, often with the aim of supporting the generation of data related to biomass burning fuel consumption and trace gas and aerosol emission fields. The Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) are one such set of products, and are freely available in both near real-time and archived form. Every 15 min, the algorithms used to generate these products identify and map the location of new SEVIRI observations containing actively burning fires, and characterise their individual rates of radiative energy release (fire radiative power; FRP) that is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the highest spatial resolution FRP dataset, delivered for all of Europe, northern and southern Africa, and part of South America at a spatial resolution of 3 km (decreasing away from the west African sub-satellite point) at the full 15 min temporal resolution. The FRP-GRID product is an hourly summary of the FRP-PIXEL data, produced at a 5° grid cell size and including simple bias adjustments for meteorological cloud cover and for the regional underestimation of FRP caused, primarily, by the non-detection of low FRP fire pixels at SEVIRI's relatively coarse pixel size. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) algorithm used to detect the SEVIRI active fire pixels, and detail methods used to deliver atmospherically corrected FRP information together with the per-pixel uncertainty metrics. Using scene simulations and analysis of real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe some of the sensor and data pre-processing characteristics influencing fire detection and FRP uncertainty. We show that the FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel, and is more sensitive to fire than algorithms used within many other widely exploited active fire products. We also find that artefacts arising from the digital filtering and geometric resampling strategies used to generate level 1.5 SEVIRI data can significantly increase FRP uncertainties in the SEVIRI active fire products, and recommend that the processing chains used for the forthcoming Meteosat Third Generation attempt to minimise the impact of these types of operations. Finally, we illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity. A companion paper (Roberts et al., 2015) provides a full product performance evaluation for both products, along with examples of their use for prescribing fire smoke emissions within atmospheric modelling components of the Copernicus Atmosphere Monitoring Service (CAMS).

Description: 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 estimations are often substantial. This is for a large part because the most often used low-Earth orbit satellite-based instruments like 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. Specifically, we assess how representing the fire diurnal cycle affects FRP and FRE estimations when using data collected at MODIS overpasses. Using data assimilation we explored three different methods to estimate hourly FRE, based on an incremental sophistication of parameterizing the fire diurnal cycle. We sampled data from the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) at MODIS detection opportunities to drive the three approaches. The full SEVIRI time-series, providing full coverage of the diurnal cycle, were used to evaluate the results. Our study period comprised three years (2010–2012), and we focussed 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 as done currently in some approaches caused structural errors, while generally overestimating FRE. Including information on the climatology of the fire diurnal cycle provided the most promising avenue to improve FRE estimations. This approach also improved the performance on relatively high spatiotemporal resolutions, although only when aggregating model results to coarser spatial and/or temporal scale good correlation was found with the full SEVIRI hourly reference dataset. In general model performance was best in areas of frequent fire and low errors of omission. 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.

Description: Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, SubSaharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar orbiting MODerate-resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned area estimates. The fuel consumption estimates based on the geostationary and polar orbiting instruments showed good agreement in terms of spatial patterns, but absolute fuel consumption estimates remained more uncertain. Fuel consumption varies considerably in space and time, complicating the comparison of various approaches and using field measurements to constrain our results. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub-Saharan Africa. Strikingly, we found the infrequently burning interior of Australia having higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, both by affecting fuel build up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emissions estimates.

Description: Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of actively burning fires made from geostationary Earth Observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from geostationary EO data, often with the aim of supporting biomass burning fuel consumption and trace gas and aerosol emission calculations. Such Fire Radiative Power (FRP) products are generated operationally from Meteosat by the Land Surface Analysis Satellite Applications Facility (LSA SAF) and are available freely every 15 min in both near-real-time and archived form. These products map the location of actively burning fires and characterize their rates of thermal radiative energy release (FRP), which is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the full spatio-temporal resolution FRP data set derivable from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) imager onboard Meteosat at a 3 km spatial sampling distance (decreasing away from the west African sub-satellite point), whilst the FRP-GRID product is an hourly summary at 5° grid resolution that includes simple bias adjustments for meteorological cloud cover and regional underestimation of FRP caused primarily by underdetection of low FRP fires. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) detection algorithm used to deliver these products and detail the methods used to generate the atmospherically corrected FRP and per-pixel uncertainty metrics. Using SEVIRI scene simulations and real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe certain sensor and data pre-processing characteristics that influence SEVIRI's active fire detection and FRP measurement capability, and use these to specify parameters in the FTA algorithm and to make recommendations for the forthcoming Meteosat Third Generation operations in relation to active fire measures. We show that the current SEVIRI FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel and that it appears more sensitive to fire than other algorithms used to generate many widely exploited active fire products. Finally, we briefly illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity; the companion paper (Roberts et al., 2015) provides a full product performance evaluation and a demonstration of product use within components of the Copernicus Atmosphere Monitoring Service (CAMS).

Description: Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.