LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)

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 nondetection 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 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 o...

LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis

“…Since the first MSG launch in 2002, SEVIRI has observed Europe, Africa, and parts of South America every 15 min, and provided the first geostationary EO data to be used to estimate FRP from landscape fires Roberts and Wooster, 2008;Roberts et al, 2009a, b). SEVIRI-derived FRP data have been used to paremeterize high temporal resolution smoke emissions fields for atmospheric modelling (Baldassarre et al, 2015), including within the Copernicus Atmosphere Monitoring Service (CAMS; Roberts et al, 2015). Here we describe the algorithms and characteristics of the SEVIRI FRP products available operationally from the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF; http://landsaf.ipma.pt).…”

“…Africa is considered the most "fire-affected" continent, responsible for ∼ 30-50 % of the global burned area and a very significant proportion of annual global fire emissions (Andreae, 1991;van der Werf et al, 2003van der Werf et al, , 2006. Landscape burning is also relatively common across parts of Europe and, occasionally, extreme "wildfire" outbreaks can threaten large population centres and/or deliver acute air quality impacts, particularly in southern Europe (Liu et al, 2009;Baldassarre et al, 2015;Roberts et al, 2015). The region of South America viewed by SEVIRI is primarily dry and moist forest, cerrado and croplands, which is also greatly affected by fires; however, because of the extreme SEVIRI view angles, the FTA algorithm applied to the GOES (Geostationary Operational Environmental Satellites) imager provides better geostationary FRP data here (Xu et al, 2010).…”

“…The companion paper (Roberts et al, 2015) provides detailed product performance evaluation, a much more extensive SEVIRI fire product intercomparison, and a demonstration of use of the FRP-PIXEL product in the characterization of fire emissions within CAMS. Finally, we provide recommendations for pre-processing considerations related to Meteosat Third Generation observations of active fires.…”

“…The dynamic nature of this threshold is now being returned to the operational chain (Fig. 3), since new testing has shown that use of the dynamic threshold reduces active fire detection errors of commission with respect to MODIS by a further 2 % compared to the static case (see Roberts et al, 2015).…”

“…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 and1 Introduction…”