Abstract

Abstract. 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).

Highlights

  • 1.1 Meteosat Second Generation and biomass burning observationsSmoke emissions from landscape-scale fires are strong influencers of atmospheric composition, chemistry, and climate (Williams et al, 2010), and Earth Observation (EO) satellites are key to their characterization

  • The Level 3 Fire Radiative Power (FRP)-GRID product is already full disk, albeit at a reduced spatio-temporal resolution, and includes simple adjustments for cloud cover and for Spinning Enhanced Visible and Infrared Imager (SEVIRI)’s inability to detect the lowest FRP fires (Freeborn et al, 2009), Each FRP-PIXEL product consists of two separate product files: (i) an FRP-PIXEL List Product file that stores variables derived at each detected active fire pixel, and (ii) an FRP-PIXEL Quality Product file that contains a 2-D array of flags recording the processing status of each SEVIRI pixel, not just those identified as containing active fires

  • We have provided a detailed description of the algorithms and information content of the operational SEVIRI FRP products available from the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF), both the FRP-PIXEL product (3 km every 15 min), and the spatio-temporal summary (5◦, hourly) FRP-GRID product that includes bias adjustments for cloud cover and SEVIRI’s inability to detect the lowest FRP fire pixels

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Summary

Meteosat Second Generation and biomass burning observations

Smoke emissions from landscape-scale fires are strong influencers of atmospheric composition, chemistry, and climate (Williams et al, 2010), and Earth Observation (EO) satellites are key to their characterization. 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 et al, 2005; Wooster et al, 2005; Roberts and Wooster, 2008; Roberts et al, 2009a, b). 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). These products are available via both near-real time and offline dissemination routes and have already provided information used in a number of biomass burning emissions inventories These products are available via both near-real time and offline dissemination routes and have already provided information used in a number of biomass burning emissions inventories (e.g. Turquety et al, 2014) and to the Global Fire Assimilation System (GFAS) that provides fire emissions data to the CAMS (e.g. Hollingsworth et al, 2008; Kaiser et al, 2012; Andela et al, 2015)

Landscape-scale fires and smoke emissions
LSA SAF Meteosat SEVIRI FRP products
Active fire data from the MSG satellite series
SEVIRI data capture and pre-processing
Introduction to the LSA SAF Meteosat SEVIRI FRP product suite
The FRP-PIXEL product processing chain
Sunglint detection
Contextual active fire detection
Derivation of per-pixel FRP values
Method for FRP atmospheric correction
FRP uncertainty formulation
Meteosat-8 special operations mode: data collection and analysis
LSA SAF SEVIRI FRP-GRID product
Comparison to other SEVIRI active fire products
Comparison to MODIS and analysis of active fire trends
Findings
Summary and conclusion
Full Text
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