Abstract
Abstract. 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.
Highlights
1.1 Biomass burning emissions and Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Radiative Power (FRP) products from the Land Surface Analysis Satellite Applications Facility (LSA SAF)Biomass burning emissions databases derived from Earth Observation (EO) satellite data, such as the widely used Global Fire Emissions Database (GFED; van der Werf et al, 2006, 2010), typically follow the approach proposed by Seiler and Crutzen (1980) and estimate fire emissions via the multiplication of burned area (m2), fuel load and combustion completeness estimates
The reverse analysis showed that 9 % of the Meteosat-9 FRP-PIXEL product active fire pixels had no matching Moderate Resolution Imaging Spectroradiometer (MODIS) active fire pixel, a very similar commission error to that found by Roberts and Wooster (2008) for the prototype SEVIRI fire thermal anomaly (FTA) algorithm over the same Northern Africa region
We find that 57 % of the FRP-PIXEL products perfire FRP measures are within 20 % of those of MODIS, and this level of agreement remains consistent even when limiting the comparison to fires with FRP of ∼ 50 MW to ensure we focus on the FRP range where SEVIRI does not clearly underdetect active fires (e.g. Wooster et al, 2015)
Summary
1.1 Biomass burning emissions and Meteosat SEVIRI FRP products from the LSA SAFBiomass burning emissions databases derived from Earth Observation (EO) satellite data, such as the widely used Global Fire Emissions Database (GFED; van der Werf et al, 2006, 2010), typically follow the approach proposed by Seiler and Crutzen (1980) and estimate fire emissions via the multiplication of burned area (m2), fuel load (kg m−2) and combustion completeness (unitless, 0–1) estimates. The companion paper to this work, Wooster et al (2015) describes the geostationary Meteosat SEVIRI (Spinning Enhanced Visible and Infrared Imager) Fire Radiative Power (FRP) products being generated operationally by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF; http://landsaf.meteo.pt/) This type of geostationary active fire product offers an alternative route to biomass burning emissions estimation based on assessments of the thermal energy being radiated away from fires, and it can do so in nearreal time with frequent updates whilst the fires are still burning, though there are some limitations caused mainly by fires having too low a fire radiative power remaining undetectable with the relatively coarse spatial resolution SEVIRI observations (Roberts and Wooster, 2008). The high temporal frequencies offered by geostationary observations can enable the diurnal fire cycle and related short-term changes in fire activity to be far better characterised than with polar-orbiting data, and this ability is starting to be exploited to parameterise wildfire emissions in atmospheric transport models
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