Articles | Volume 15, issue 22
Atmos. Chem. Phys., 15, 13217–13239, 2015

Special issue: Monitoring atmospheric composition and climate, research in...

Atmos. Chem. Phys., 15, 13217–13239, 2015

Research article 30 Nov 2015

Research article | 30 Nov 2015

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

M. J. Wooster1,2, G. Roberts3, P. H. Freeborn1,4, W. Xu1, Y. Govaerts5, R. Beeby1, J. He1, A. Lattanzio6, D. Fisher1,2, and R. Mullen1 M. J. Wooster et al.
  • 1King's College London, Environmental Monitoring and Modelling Research Group, Department of Geography , Strand, London, WC2R 2LS, UK
  • 2NERC National Centre for Earth Observation (NCEO), UK
  • 3Geography and Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
  • 4Fire Sciences Laboratory, Rocky Mountain Research Station, US Forest Service, Missoula, Montana, USA
  • 5Rayference, Brussels, Belgium
  • 6MakaluMedia, Darmstadt, Germany

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).

Short summary
Landscape fires strongly influence atmospheric chemistry, composition, and climate. Characterizing such fires at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth Observation satellites. Here we detail the Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat geostationary satellites.
Final-revised paper