Using SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model: a case study over Antalya in 2008

Abstract. Among the atmospheric emission sources, wildfires are episodic events characterized by large spatial and temporal variability. Therefore, accurate information on gaseous and aerosol emissions from fires for specific regions and seasons is critical for air quality forecasts. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) in geostationary orbit provides fire observations over Africa and the Mediterranean with a temporal resolution of 15 min. It thus resolves the complete fire life cycle and captures the fires' peak intensities, which is not possible in Moderate Resolution Imaging Spectroradiometer (MODIS) fire emission inventories like the Global Fire Assimilation System (GFAS). We evaluate two different operational fire radiative power (FRP) products derived from SEVIRI, by studying a large forest fire in Antalya, Turkey, in July–August 2008. The EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) has higher FRP values during the fire episode than the Wildfire Automated Biomass Burning Algorithm (WF_ABBA). It is also in better agreement with the co-located, gridded MODIS FRP. Both products miss small fires that frequently occur in the region and are detected by MODIS. Emissions are derived from the FRP products. They are used along-side GFAS emissions in smoke plume simulations with the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. In comparisons with MODIS aerosol optical thickness (AOT) and Infrared Atmospheric Sounding Interferometer (IASI), CO and NH3 observations show that including the diurnal variability of fire emissions improves the spatial distribution and peak concentrations of the simulated smoke plumes associated with this large fire. They also show a large discrepancy between the currently available operational FRP products, with the LSA SAF being the most appropriate.

[1]  J. Morcrette,et al.  LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS) , 2015 .

[2]  R. Koster,et al.  The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4. Volume 38; Technical Report Series on Global Modeling and Data Assimilation , 2015 .

[3]  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 .

[4]  P. Coheur,et al.  Fire plume simulation with geostationary observations , 2015 .

[5]  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 .

[6]  Johannes W. Kaiser,et al.  Constraining CO 2 emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia , 2014 .

[7]  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.

[8]  Lieven Clarisse,et al.  Global distributions, time series and error characterization of atmospheric ammonia (NH 3 ) from IASI satellite observations , 2014 .

[9]  N. Khabarov,et al.  Modeling biomass burning and related carbon emissions during the 21st century in Europe , 2013 .

[10]  Lieven Clarisse,et al.  FORLI radiative transfer and retrieval code for IASI , 2012 .

[11]  Jean-Noël Thépaut,et al.  The MACC reanalysis: an 8 yr data set of atmospheric composition , 2012 .

[12]  P. Zanis,et al.  REGIONAL CLIMATE CHANGE SCENARIOS FOR GREECE: FUTURE TEMPERATURE AND PRECIPITATION PRO JECTIONS FROM ENSEMBLES OF RCMs , 2012 .

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

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

[15]  Lieven Clarisse,et al.  Thermal infrared nadir observations of 24 atmospheric gases , 2011 .

[16]  D. Melas,et al.  The impact of temperature changes on summer time ozone and its precursors in the Eastern Mediterranean , 2011 .

[17]  Charalambos Kontoes,et al.  Wildfire Detection and Tracking over Greece Using MSG-SEVIRI Satellite Data , 2011, Remote. Sens..

[18]  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 .

[19]  Anu Dudhia,et al.  An effective method for the detection of trace species demonstrated using the MetOp Infrared Atmospheric Sounding Interferometer , 2010 .

[20]  Aleksander Marinšek,et al.  Long-term post-fire succession of Pinus brutia forest in the east Mediterranean , 2010 .

[21]  Alper Unal,et al.  Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system , 2010 .

[22]  Tanya L. Otte,et al.  The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1 , 2010 .

[23]  T. L. Otte,et al.  The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system , 2009 .

[24]  U. Shankar,et al.  Simulating emission and chemical evolution of coarse sea-salt particles in the Community Multiscale Air Quality (CMAQ) model , 2009 .

[25]  Jeffrey Young,et al.  Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 , 2009 .

[26]  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 .

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

[28]  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.

[29]  Lieven Clarisse,et al.  IASI measurements of reactive trace species in biomass burning plumes , 2009 .

[30]  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 .

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

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

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

[34]  William C. Skamarock,et al.  A time-split nonhydrostatic atmospheric model for weather research and forecasting applications , 2008, J. Comput. Phys..

[35]  J. Stoyanova,et al.  ACTIVE FIRE MONITORING OVER BULGARIA: VALIDATION OF SEVIRI FIR PRODUCT , 2008 .

[36]  M. G. Schultz,et al.  Freeval: Evaluation of a Fire Radiative Power Product derived from Meteosat 8/9 and Identification of Operational User Needs - Final Report , 2008 .

[37]  Lieven Clarisse,et al.  Monitoring of atmospheric composition using the thermal infrared IASI/METOP sounder , 2009 .

[38]  Alper Unal,et al.  Determining the Sources of Regional Haze in the Southeastern United States Using the CMAQ Model , 2007 .

[39]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[40]  Giovanni Laneve,et al.  Continuous Monitoring of Forest Fires in the Mediterranean Area Using MSG , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  P. Palmer,et al.  Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature) , 2006 .

[42]  Shu‐Hua Chen,et al.  Long-range aerosol transport from Europe to Istanbul, Turkey , 2006 .

[43]  Song‐You Hong,et al.  The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) , 2006 .

[44]  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 .

[45]  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.

[46]  Alper Unal,et al.  Airport related emissions and impacts on air quality: Application to the Atlanta International Airport , 2005 .

[47]  E. Vermote,et al.  The MODIS Aerosol Algorithm, Products, and Validation , 2005 .

[48]  P. Zandveld,et al.  Study to the effectiveness of the UNECE Heavy Metals Protocol and costs of possible additional measures. Phase I: Estimation of emission reduction resulting from the implementation of the HM Protocol , 2005 .

[49]  J. Dudhia,et al.  A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation , 2004 .

[50]  John S. Kain,et al.  The Kain–Fritsch Convective Parameterization: An Update , 2004 .

[51]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[52]  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 .

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

[54]  John N. McHenry,et al.  Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions , 2001 .

[55]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[56]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[57]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[58]  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 .

[59]  W. Malm,et al.  Spatial and seasonal trends in particle concentration and optical extinction in the United States , 1994 .

[60]  E. Prins,et al.  Geostationary satellite detection of bio mass burning in South America , 1992 .

[61]  J. Dudhia Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

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

[63]  Smoke in the air. , 1971, JAMA.