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

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.

[1]  Y. Govaerts,et al.  LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis , 2015 .

[2]  N. Andela,et al.  New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations , 2015 .

[3]  P. Coheur,et al.  Using SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model: a case study over Antalya in 2008 , 2015 .

[4]  Y. Govaerts,et al.  Meteosat SEVIRI Fire Radiative Power (FRP) products from the Land Surface Analysis Satellite Applications Facility (LSA SAF) - Part 1: Algorithms, product contents and analysis , 2015 .

[5]  S. Freitas,et al.  Development and optimization of a wildfire plume rise model based on remote sensing data inputs – Part 2 , 2015 .

[6]  N. Andela,et al.  New fire diurnal cycle characterizations to improve fire radiative energy assessments made from low-Earth orbit satellites sampling , 2015 .

[7]  S. Freitas,et al.  A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models , 2015 .

[8]  C. Zerefos,et al.  A modeling study of the impact of the 2007 Greek forest fires on the gaseous pollutant levels in the Eastern Mediterranean , 2014 .

[9]  M. Odman,et al.  Simulating smoke transport from wildland fires with a regional-scale air quality model: sensitivity to spatiotemporal allocation of fire emissions. , 2014, The Science of the total environment.

[10]  Gareth Roberts,et al.  Development of a multi-temporal Kalman filter approach to geostationary active fire detection & fire radiative power (FRP) estimation , 2014 .

[11]  Mark Z. Jacobson,et al.  Effects of biomass burning on climate, accounting for heat and moisture fluxes, black and brown carbon, and cloud absorption effects , 2014 .

[12]  Martin J. Wooster,et al.  A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic , 2014, Remote. Sens..

[13]  D. Roy,et al.  Quantification of MODIS fire radiative power (FRP) measurement uncertainty for use in satellite‐based active fire characterization and biomass burning estimation , 2014 .

[14]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[15]  Gareth Roberts,et al.  Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product , 2014, Remote. Sens..

[16]  J. Randerson,et al.  The role of temporal evolution in modeling atmospheric emissions from tropical fires , 2014 .

[17]  C. Justice,et al.  Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results , 2014 .

[18]  Giorgos Mallinis,et al.  Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation , 2013, Remote. Sens..

[19]  Fabienne Maignan,et al.  APIFLAME v1.0: high-resolution fire emission model and application to the Euro-Mediterranean region , 2013 .

[20]  E. Mitsakis,et al.  Assessment of extreme weather events on transport networks: case study of the 2007 wildfires in Peloponnesus , 2013, Natural Hazards.

[21]  I. Mitsopoulos,et al.  Estimation of canopy fuel characteristics of Aleppo pine (Pinus halepensis Mill.) forests in Greece based on common stand parameters , 2013, European Journal of Forest Research.

[22]  Charles Ichoku,et al.  Space‐based observational constraints for 1‐D fire smoke plume‐rise models , 2012 .

[23]  Harshvardhan,et al.  The use of satellite‐measured aerosol optical depth to constrain biomass burning emissions source strength in the global model GOCART , 2012 .

[24]  Christopher C. Schmidt,et al.  Near-Real-Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation , 2012 .

[25]  T. Nightingale,et al.  Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets , 2012 .

[26]  Nikos Koutsias,et al.  Where did the fires burn in Peloponnisos, Greece the summer of 2007? Evidence for a synergy of fuel and weather , 2012 .

[27]  J. Randerson,et al.  Daily and 3‐hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide , 2011 .

[28]  Mikhail Sofiev,et al.  Evaluation of the smoke-injection height from wild-land fires using remote-sensing data , 2011 .

[29]  G. Roberts,et al.  Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory , 2011 .

[30]  M. Razinger,et al.  Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power , 2011 .

[31]  S. Freitas,et al.  Monitoring the transport of biomass burning emission in South America , 2011 .

[32]  T. Georgiadis,et al.  Aleppo pine forests of northern and western Peloponnisos (southern Greece): Plant communities and diversity , 2011 .

[33]  Sundar A. Christopher,et al.  Use of hourly Geostationary Operational Environmental Satellite (GOES) fire emissions in a Community Multiscale Air Quality (CMAQ) model for improving surface particulate matter predictions , 2011 .

[34]  G. Roberts,et al.  Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa , 2011 .

[35]  J. Randerson,et al.  Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009) , 2010 .

[36]  Sander Veraverbeke,et al.  The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. , 2010 .

[37]  G. Roberts,et al.  New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America , 2010 .

[38]  J. Pereira,et al.  Detection and monitoring of African vegetation fires using MSG-SEVIRI imagery , 2010 .

[39]  Anastasia Spanou,et al.  Heat waves observed in 2007 in Athens, Greece: synoptic conditions, bioclimatological assessment, air quality levels and health effects. , 2010, Environmental research.

[40]  J. D. Laat,et al.  An aerosol boomerang: Rapid around-the-world transport of smoke from the December 2006 Australian forest fires observed from space , 2009 .

[41]  D. L. Nelson,et al.  Smoke injection heights from fires in North America: analysis of 5 years of satellite observations , 2009 .

[42]  Gareth Roberts,et al.  An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power , 2009 .

[43]  Jaakko Kukkonen,et al.  An operational system for the assimilation of the satellite information on wild-land fires for the needs of air quality modelling and forecasting , 2009 .

[44]  D. L. Nelson,et al.  Interactive comment on “ The sensitivity of CO and aerosol transport to the temporal and vertical distribution of North American boreal fire emissions ” by Y . , 2009 .

[45]  F. Gonzalez-Alonso,et al.  Impact of point spread function of MSG-SEVIRI on active fire detection , 2009 .

[46]  Sundar A. Christopher,et al.  Global Monitoring and Forecasting of Biomass-Burning Smoke: Description of and Lessons From the Fire Locating and Modeling of Burning Emissions (FLAMBE) Program , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[47]  B. Malamud,et al.  Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data , 2009 .

[48]  Cathy Clerbaux,et al.  Tracking the emission and transport of pollution from wildfires using the IASI CO retrievals: analysis of the summer 2007 Greek fires , 2009 .

[49]  Yang Liu,et al.  Analysis of the impact of the forest fires in August 2007 on air quality of Athens using multi-sensor aerosol remote sensing data, meteorology and surface observations , 2009 .

[50]  E. Vermote,et al.  Estimating biomass consumed from fire using MODIS FRE , 2009 .

[51]  Johannes W. Kaiser,et al.  Global Real‐time Fire Emission Estimates Based on Space‐borne Fire Radiative Power Observations , 2009 .

[52]  David P. Roy,et al.  Southern Africa Validation of the MODIS, L3JRC, and GlobCarbon Burned-Area Products , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[53]  S. Freitas,et al.  Modeling the effect of plume-rise on the transport of carbon monoxide over Africa with NCAR CAM , 2008 .

[54]  G. Roberts,et al.  Annual and diurnal african biomass burning temporal dynamics , 2008 .

[55]  A. Polychronaki,et al.  Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece , 2008 .

[56]  A. Hollingsworth,et al.  Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project , 2008 .

[57]  D. Morton,et al.  Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data , 2008 .

[58]  Gareth Roberts,et al.  Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[59]  Ioannis Z. Gitas,et al.  Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece , 2008 .

[60]  Charles Ichoku,et al.  Relationships between energy release, fuel mass loss, and trace gas and aerosol emissions during laboratory biomass fires , 2008 .

[61]  O. Samain,et al.  TOWARDS AN IMPROVED ACTIVE FIRE MONITORING PRODUCT FOR MSG SATELLITES , 2008 .

[62]  Z. Klimont,et al.  Modeling of elemental carbon over Europe , 2007 .

[63]  G. Roberts,et al.  New perspectives on African biomass burning dynamics , 2007 .

[64]  David G. Streets,et al.  Impacts of enhanced biomass burning in the boreal forests in 1998 on tropospheric chemistry and the sensitivity of model results to the injection height of emissions , 2007 .

[65]  J. Randerson,et al.  Interannual variability in global biomass burning emissions from 1997 to 2004 , 2006 .

[66]  G. Roberts,et al.  Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001–2004) , 2006 .

[67]  Y. Kaufman,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release , 2005 .

[68]  G. Roberts,et al.  Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI imagery , 2005 .

[69]  M. Derrien,et al.  MSG/SEVIRI cloud mask and type from SAFNWC , 2005 .

[70]  Yoram J. Kaufman,et al.  A method to derive smoke emission rates from MODIS fire radiative energy measurements , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[71]  D. Roy,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[72]  James A. Gardner,et al.  MODTRAN5: a reformulated atmospheric band model with auxiliary species and practical multiple scattering options , 2004, SPIE Asia-Pacific Remote Sensing.

[73]  D. Roya,et al.  Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data , 2005 .

[74]  Sundar A. Christopher,et al.  Real‐time monitoring of South American smoke particle emissions and transport using a coupled remote sensing/box‐model approach , 2004 .

[75]  Yoram J. Kaufman,et al.  An Enhanced Contextual Fire Detection Algorithm for MODIS , 2003 .

[76]  M. Wooster,et al.  Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products. , 2003 .

[77]  François Petitcolin,et al.  Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data , 2002 .

[78]  E. P. McClam,et al.  A Method for Satellite Identification of Surface Temperature Fields of Subpixel Resolution , 2002 .

[79]  M. Andreae,et al.  Emission of trace gases and aerosols from biomass burning , 2001 .

[80]  T. Eck,et al.  An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET , 2001 .

[81]  Eric P. Shettle,et al.  Observations of boreal forest fire smoke in the stratosphere by POAM III, SAGE II, and lidar in 1998 , 2000 .

[82]  Yoram J. Kaufman,et al.  A Review of AVHRR-based Active Fire Detection Algorithms: Principles, Limitations, and Recommendations , 2000 .

[83]  E. Prins,et al.  An overview of GOES‐8 diurnal fire and smoke results for SCAR‐B and 1995 fire season in South America , 1998 .

[84]  C. Justice,et al.  Potential global fire monitoring from EOS‐MODIS , 1998 .

[85]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[86]  B. Jenkins,et al.  Combustion properties of biomass , 1998 .

[87]  Donny M. A. Aminou,et al.  Characteristics of the Meteosat Second Generation (MSG) radiometer/imager: SEVIRI , 1997, Remote Sensing.

[88]  Johannes Schmetz,et al.  Synthetic satellite radiances using the radiance sampling method , 1997 .

[89]  E. Prins,et al.  Trends in South American biomass burning detected with the GOES visible infrared spin scan radiometer atmospheric sounder from 1983 to 1991 , 1994 .

[90]  Lorraine Remer,et al.  Detection of forests using mid-IR reflectance: an application for aerosol studies , 1994, IEEE Trans. Geosci. Remote. Sens..

[91]  J. Dozier A method for satellite identification of surface temperature fields of subpixel resolution , 1981 .

[92]  J. Dozier,et al.  Identification of Subresolution High Temperature Sources Using a Thermal IR Sensor , 1981 .

[93]  P. Crutzen,et al.  Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning , 1980 .