Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , 2 , W. P. Menzel 1 were comparable with 5,6,7 2.5 , T. Kindap 1 , A. Unal 2 , and J. W. Kaiser 2 1 4 erent operational Fire Radiative Power (FRP) prod- ff , C. C. Schmidt , A. Kavgaci 1 3 observations show that including the diurnal variability of fire 3 , L. Pozzoli 1 , P.-F. Coheur 3 This discussion paper is/has beenand under Physics review (ACP). for Please the refer journal to Atmospheric the Chemistry corresponding final paper in ACP if available. Eurasia Institute of Earth Sciences,Cooperative Istanbul Institute Technical University, for Istanbul, Meteorological Turkey Satellite Studies, University of Wisconsin, Madison, Spectroscopie de l’Atmosphère, Service de Chimie Quantique et de Photophysique, Southwest Anatolia Forest Research Institute,King’s Antalya, College, Turkey London, UK European Center for Medium-Range WeatherMax Forecasts, Planck Reading, Institute UK for Chemistry, Mainz, Germany ucts derived from SEVIRI, byin studying July–August the 2008. case The of EUMETSAT LSA athe SAF large fire product forest episode has fire than higher in FRP thelocated, Antalya, values WF_ABBA Turkey, during gridded product. It MODIS is FRP.the Both also region in products and better miss are agreement small detected withThey by the fires are MODIS. co- used that Emissions along-side are frequently GFAS derivedthe occur emissions from in Community in the smoke Multiscale FRP plume Air products. simulationsand Quality with model IASI WRF (CMAQ). and CO Comparisons andemissions with NH MODIS improves AOT the spatialsmoke distribution plumes and associated peak to concentrationstween the of the the large currently available simulated fire.the operational They most FRP also appropriate. products, show with a the large LSA SAF discrepancy one be- being 1 Introduction Fire is the main(JRC, 2008) cause where of the fire forest seasonIn starts destruction terms in in of April emissions, and countries can the last biomassthe of until burning anthropogenic the the contribution contribution end to Mediterranean of during PM November. recent basin, years (e.g., Sofiev et al., 2009). 1 2 WI, USA 3 Université Libre de Bruxelles4 (U.L.B), Brussels, Belgium 5 6 7 Received: 2 October 2014 – Accepted: 21Correspondence November to: 2014 G. – Baldassarre Published: ([email protected]) 6 January 2015 Published by Copernicus Publications on behalf of the European Geosciences Union. Abstract Among the atmosphericized emission by sources, large wildfires spatialgaseous are and and aerosol temporal episodic emissions variability. for eventsity Therefore, specific forecasts. character- regions accurate The and information Spinning seasons Enhanced on istionary Visible critical orbit fire provides and for fire air Infrared observations qual- Imager overtemporal Africa (SEVIRI) resolution and in of the geosta- 15 Mediterranean min. withthe It a fires’ unique thus peak resolves intensities, the which completeries is fire like not life GFAS. possible We cycle evaluate in and two MODIS-based captures di fire emission invento- S. Whitburn Using SEVIRI fire observations tosmoke drive plumes in the CMAQmodel: air the quality case of Antalya inG. 2008 Baldassarre Atmos. Chem. Phys. Discuss., 15, 1–46,www.atmos-chem-phys-discuss.net/15/1/2015/ 2015 doi:10.5194/acpd-15-1-2015 © Author(s) 2015. CC Attribution 3.0 License. 5 10 15 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ective way and with broader coverage, ff 4 3 erent algorithms. ff erent satellite techniques have been developed using high temporal resolution ff Observed FRP has been successfully used to calculate biomass combusted from Di It has been demonstrated in small-scale experiments that the amount of radiant en- In 2008 most of the large forest damages in Europe occurred in the Southeastern Although designed for operational weather forecasting and not specifically for fire Satellite remote sensing provides automated powerful means of locating and char- Emissions from open vegetation burnings are increasingly recognized as an impor- The recent improvements of air quality models, such as the Community Multiscale Air wildfires using the SEVIRIin radiometer Africa onboard (Roberts the et geostationary al., globally Meteosat-82005) (Kaiser platform and et MODIS al., data2009; bothDarmenov in Africa and ( Ellicott da et, Silva al., 2013). 2009) and is coming from the Earthby can open measure fires the (Wooster radiative et componentFRP of, al. avoids the2003 ). using energy The the released estimate complexa parameters of robust of biomass approach fuel burning for the loading emissionsobservations global and from (e.g. estimates burnedIchoku of area. and biomass Thus,, Kaufman burning it2005; emissions is Heil from et satellite al., 2010; Yang et al., ). 2011 multi-spectral data intomated order Biomass to Burning detect AlgorithmRadiative and (WF_ABBA) Power characterize and provide the fire operational EUMETSATobservations activity. LSA fire using The SAF di radiative Wildfire Fire power Au- products based on SEVIRI ergy liberated per unit time(FRP), during is a related vegetation to fire, the thesensors rate able so-called at to Fire which observe Radiative the the Power fuel Middle biomass Infrared (MIR) is spectral being radiance consumed. around Spaceborne 3.9 µm that series of the Meteosat Secondpotential Generation for (MSG) geostationary fire satellites detection showsa and great geostationary characterization. Since platform, this it(one instrument can observation is sample per employed a 15 on flection, min). large etc.) Under SEVIRI region delivers certain important withfires. information conditions high on (no the temporal temporal opaque frequency variability clouds, of active solar re- Mediterranean countries, which weretions under that the facilitated fire influenceaged ignition of was and Turkey, where extreme spread. the weather forest Theber, condi- fire country and danger especially that was during high was the during most period the heavily July period to dam- May September, to which Octo- had very high tempera- detection, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the especially in remote areas.scenes. In However, the satellite end, satellite observationsissues. observations are can still limited provide a to better cloud-free understanding of fire atmospheric window spectral channels,characteristics such as can the be 3.9 determined.fires and Furthermore by 11 µm, ground the fire and cost locations aircraftlite of observations and observations continuous is can monitoring prohibitive, be whereas of monitoring achieved in with satel- a cost e acterizing open vegetation burnings.vantage of Infrared the fire fact detectionthe that from shortwave as end satellites target temperature of takes increases the ad- radiance spectrum increases as faster opposed at to the longwave end. By using two at fine spatial andprovided temporal with resolutions. higher Therefore also levelwhich emission are of inventories characterized detail, by must and high be Menendez spatial et this al., and),2014 is temporal in variations. addition particularlymodeling to According adequate important to the estimates (Garcia- of for impact emitted mass, forestallocation successfully of fires, of fires emissions. on air quality depends on an accurate spatiotemporal tant parameter in atmospheric modeling,specific and their regions accurate and description seasons isstrated important and that for for open specific biomass episodes.fects burning Recent on events, studies the although have photochemistry episodic, demon- inthermore may the the have Eastern impact important Mediterranean of ef- (Poupkou biomassSoutheastern et burning Mediterranean al., according is2014). to expected Fur- future to(Tolika et scenarios become, al. on more2012; climate importantMigliavacca change in et indicate the al. , ). 2013 Quality (CMAQ) model, permit to simulate the chemical composition of the atmosphere 5 5 15 10 20 25 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cult ffi ect is that radiance (GFASv1.1). All four ff ◦ 0.1 × ◦ ected 15 795 ha of forestland ff Ten.), typical fire adapted ecosys- et al., 2010). In this fire, tens of homes ı retrievals from the Infrared Atmospheric erence of the Middle Infrared (MIR) spec- 3 ff 5 6 Pinus brutia (GFASv1.0), and 0.1 ◦ 0.5 × ective wind. In Turkey, the coastline, which starts from ◦ ff ected pixels (Calle et al., 2009). Also, the background temperature that ff erent spatial resolutions, 0.5 At the sub satellite point, the actual field of view of the SEVIRI thermal channels is Following the Wooster approach the LSA SAF algorithm uses the SEVIRI band cen- We calculate gridded FRP and emission inventories for a large region of the Eastern In this paper, we investigate the applicability of the available SEVIRI-based FRP The 4 µm channel of the SEVIRI sensor has a saturation temperature of 335 K. Sat- The fire products described in this paper are derived using SEVIRI level-1.5 radio- A large forest fire occurred in Turkey’s most touristic province of Antalya on 31 July ff Sounding Interferometer (IASI), which weretransport previously of used pollution and to to track measureduring the reactive the emission trace intense species and 2007 in Greek biomass burning forest plumes fires (Turquety et al., 2009; Coheur et al., 2009). emission inventories are subsequentlyQuality used (CMAQ) as model. input The inson simulated the with Community smoke the Multiscale plumes Aerosol Air Optical areSpectroradiometer Thickness then (MODIS) product and validated from CO by the and compari- Moderate NH Resolution Imaging about 4.8 km with an overlap of 1.6 km. The consequence of this e 2 Methods 2.1 SEVIRI Fire Radiative Power Fire Radiative Power (FRP) isfrom a burning measure vegetation of via the theet rapid radiant al., 2003) oxidation energy approximated of liberated the fuel per FRP carbon as unit and the time hydrogen. di (Wooster tered at 4 µmpixels. infrared While window the to WF_ABBA evaluatethe one the availability uses of FRP instantaneous a area associated combination andpixel. with of temperature estimation the both over detected methods, the detected depending fire fire on di tral radiances between a firealternative pixel method and uses ambient the backgroundtemperature Dozier pixels (Dozier, in approximation1981) a of to linear theThermal form. evaluate instantaneous and An the Middle fire FRP infrared area satellite over and observations. the detected fire pixel by using coming from a single location isthis at fact times may present in not several significantly neighboringof impact pixels. fire Although other detection, applications, since it fires is present very complex important geometric in structures the that case make it di Mediterranean during the life timeFRP products. of They the are Antalya compared fire to from the daily the emission two inventory operational based SEVIRI on MODIS at model, and compareMODIS. to simulations based on the daily fire emissions derived from products for air quality simulations with the Community Multiscale Air Quality (CMAQ) uration of the SEVIRI pixelsfires, will but not it impact will on lead thewhich to ability can an of contribute underestimation the significantly of fire to the algorithms an true to underestimation fire detect of radiative power FRP formetrically in large calibrated a fires, and given region. geometrically corrected imagery product provided by the EU- and many farmingtechnical building personals, were 1680 destroyed. fire fighters, Duringlagers 75 and the forest 80 workers, fire gendarmes 20 were suppressionwith local employed. sprinklers, managers, works, In 45 1450 those bulldozers, 227 vil- 38 works, trailers,7 197 5 planes fire road were suppression graders, occupied trucks 63 (Internal pickups, fire 9 hazard helicopters and report). to identify the a is used in the FRP calculation is derived from neighboring pixels. 2008 (Fig.1). It burned for 5 consecutive days and a tures, very low humidityHatay and and e extends over the Mediterraneanfire and risk. Aegean In up to otherlocated Istanbul, words, in has approximately fire the highest sensitive 60 % areas, (JRC (122009). million ha) of Turkey’s forest area is mainly dominated by Turkish Red Pinetems ( of Eastern Mediterranean basin (Kavgac 5 5 25 15 10 20 15 25 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | : opaque cloud coverage; 8 7 : latitude; longitude; satellite view angle; pixel : this file provides the actual status of each pixel in the selected grid. ◦ 0.1 : this has the format of a list. One entry for every fire pixel having an es- × ◦ FRP List timated FRP value. For eachinformation fire such pixel, the as FRP the andprovided. fire an pixel exhaustive list background of window relevant mean temperatureFRP is Quality also Flag region, whether it contains a detectedsuch fire as or not, whether and the a2009). number pixel of is other conditions cloudy or clear sky, coastline etc. (Lattanzio et al. , Fire mask indicating where fireblock-out detection zones due is to not solar possible reflectance, clouds,bad extreme data, view angles, etc. biome type, Revised ASCII fire productsize; output observed 4 and 11fire µm size, brightness temperature, temperatures; and instantaneous FRP; estimates biome of type; fire confidence flag. – – – – The Global Fire Assimilation System version 1.1 (GFASv1.1) calculates biomass- Previous studies have demonstrated that SEVIRI data can be used operationally to The comparison of SEVIRI FRP to GFAS emissions should be regarded as a com- The Meteosat Fire Radiative Power-Pixel (FRP-Pixel) product is derived at the Eu- easier to read; ininformation delivered fact, by just this product. likesion The of the product the homologous is prototype created WF_ABBA, geostationarypresented using active the in an fire FRP operational Roberts detection ver- and is andso characterization Wooster one far algorithm been (2008). type focused Most of on usesinformation Africa on of (e.g. fires Roberts the burning et across FRP-Pixel al.,Pixel Europe product 2009), and Product have also but is parts the freely of product availableeach South also in processed America. provides near-real region The time FRP- the or FRP-Pixel in algorithm archived generates from two the output LSA files SAF. For burning emissions by assimilating Fireinstruments Radiative Power onboard observations the froma the Terra global MODIS 0.1 and Aqua satellites. It provides daily emissions on Due to theResolution higher Imaging spatial Spectroradiometer resolution (MODIS)to active observations be fire the and products, reference wide standard these against are usage which taken the of SEVIRI the FRP product Moderate is assessed. parison between two independent datadata sets set rather (Schultz and than, Wooster a2008). validation using a reference Studies (CIMSS) atdata the to University monitor biomass of burning1994; Wisconsin-Madison inPrins, the using).2001 Western Recently multispectral Hemisphere a (Prinsapplied GOES global and to version, Menzel MSG-SEVIRI of1992, data. the The WF_ABBAcludes global (Version the 6.5) geostationary following: has WF_ABBA been Version 6.5.006 in- metsat Land Surface Analysis)meteo.pt/ Satellite from Applications 15 Facility minute (LSApaper temporal SAF: we resolution oftenhttp://landsaf. refer Meteosat to SEVIRI thisSAF product observations. FRP-Pixel) only In in as LSA order this SAF to (instead make of its using its comparative full analysis name with LSA other satellite products assist the detection ofet fires al., by2006; improvingStoyanova et the, al. reliability2008)ern and Europe. in allowing The fire real role time announcements of fire (Laneve size SEVIRI front monitoringSifakis ( is in et especially South- useful al., as2011). the fires increase in2.1.1 number and Fire characterization data The Wildfire Automated Biomasstextual Burning algorithm Algorithm developed (WF_ABBA) at is the a Cooperative dynamic Institute con- for Meteorological Satellite METSAT LSA SAF. Resampling and regriddingmask actions the included fire in signal level-1.5 and can impact act both to fire detection and characterization. 5 5 20 10 15 25 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | grid ◦ G × ◦ ) the total FRP G erence between Y ff , for agriculture, and G means that the cen- 1 X ◦ ] proposed by (Ichoku − 1 G − × ◦ erences between the three G ff ∈ ) . Then we averaged the gridded f ◦ over land within the G j , G 1 f i × cients [kgMJ ). (1) ◦ f ffi j , f i , t 9 ( 10 FRP ◦ G grid box. For each grid point ( × ◦ ◦ G G ∈ X ) t × j , ◦ t i ( ) G j ) is inside the new grid G , f j G 1 , i , f i t ( for savannah and tropical forest, 0.084 kgMJ s resolution containing area integrated FRP totals corrected for partial f ) is the fraction of clear sky pixels 1 ◦ 0.2 the equation is not estimated. ( G − = j , < for extra tropical forest.2 Table shows a significant di ) 0.1 G s G i 1 f j , × − , t ◦ ( G i s f , t ( We used the WF_ABBA fire mask and LSA SAF Quality Flag products to have information 1 An alternative approach to estimate biomass burning smoke aerosols is to directly Accordingly to this, the hourly FRP gridded product was converted to major contami- By using satellite retrievals and top-down estimates of particulate matter, (Kaiser The gridding method is the one described in (Govaerts et al., ).2007 The FRP derived FRP Vertical distribution Emission factors for WF_ABBA and LSA SAF cloudiness at the grid-cell scale. from the SEVIRI image (LSAa SAF regular and WF_ABBA) grid acquired of at resolution time G t are summed over is: relate these to FRP, using smoke emission coe based fire emission inventory comparable withdure this as dataset described we followed in a (Kaiserburning similar et proce- pollutants, al. calculated2012 ), from toization WF_ABBA evaluate and data. emission LSA rates This SAF of approach FRP-Pixel main also fire biomass character- helps to discuss the di et al., 2012) concluded thatneed emissions of to particulate be matter calculated boostedcarbon. with to this According method reproduce to the this,factor of global in 3.4. distribution this of work organic we matter used and the black proposed aerosol enhancement maximum of the boundary layerredistributes height. the Below emissions this through height, out fast the turbulent boundary mixing layer. rapidly nant emission rate using conversion factorsfactors to dry based matter on combustion an rate updated and version emission of the compilation by Andreae and Merlet (2001). Vertical distribution of fireenergetic emission wild is fire critical episodes for when air the quality plume simulation top height in can presence strongly of exceed the daily Where box. When biomass burning emission inventoriesfire. over the case study area and over the Antalya and Kaufman, 2005). Specifically, the valuesare: we 0.06 assigned kgMJ to the main0.02 kgMJ land cover types smoke aerosol emissions evaluated with(Kaiser this et approach al., andatmospheric2012), with composition the that of one Antalya are described fire the in with the ones CMAQ we air quality finally model. used in this work to simulate the The GFAS emission inventory representsing our activity reference over the information Eastern on Mediterranean biomass domain. burn- In order to make ourabout SEVIRI the FRP status ofreflectance, each clouds, processed extreme view pixel angles, in biome the type, selected bad region data, etc.). (block-out zones due to solar FRP over 1 h timetories period. will have As hourly a timediurnal consequence, resolution pattern. the in contrast SEVIRI with based MODIS fire based emission one inven- with constant ter of SEVIRI pixel ( 2.1.2 Emission inventory WF_ABBA/LSA SAF gridding at 0.1 regular grid In order to createfrom a WF_ABBA fire and emission LSA SAF inventory FRP-Pixeluct at products at the and 0.1 generated same a grid gridded of FRP prod- GFASv1.1, we started 5 5 15 10 25 20 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 156 cells), 100 m. The × ≈ 10km (186 160 cells) and it is cover- × × lat/lon horizontal resolution and 6- ◦ and PMcoarse). The emissions from 30km (192 0.25 2.5 × × ◦ 12 11 , PM 3 , NMVOC, NH 2 , SO x The TNO/MACC_2005 emission inventory (Denier van der Gon et al., 2005) was The WRF-CMAQ model simulations were performed for two nested domains. CMAQ is a regional air quality model widely used to simulate the atmospheric com- The emissions calculated for each hour were distributed uniformly from the ground vegetation, biogenic volatile organicwere compounds (BVOC, estimated e.g. using isoprene(MEGAN, the and terpenes) Guenther Model et ofation al., Emissions fields)2006 of from according Gaseslated to and the on-line the Aerosols meteorological by simulated from CMAQ model.eral temperature model Nature dust Sea as and emissions, salt a radi- ied were aerosol function episode not of emissions dust included wind are outbreakscasted speed in from calcu- over (Kelly this North the et study, Africav1.0, Mediterranean al., nevertheless andhttp://www.bsc.es/earth-sciences/mineral-dust/catalogo-datos-dust Sea).2009 ), during Arabian Min- and the Peninsula the South impact were stud- Turkey not (not fore- shown, BSC-DREAM8b used for anthropogenic sourceslutants of (CO, the NO main gaseous and aerosol atmospheric pol- ing all Europe. Acentered fine in the domain Marmara hasSupplement), where Sea the the region resolution studied and of firevertical layers, including episode 10km from occurred, surface south to close Turkey ca. toincreases (see 26 km, from the Fig. are surface city used to 13 for of the bothin Antalya.initial top, domains, 24 the and the chemical layer the thickness concentrations first 8 andprovided levels from boundary have the a conditions Monitoring spacing Atmospheric of forvice, Composition the and which Climate coarse provide (MACC) domain data aperiod ser- were 2003–2010 comprehensive (http://gmes-atmosphere.eu/, reanalysisInness ofof et the atmospheric al., coarse domain2013), composition was while used forfor the to the the create output nested initial domain. concentrations and boundary conditions A coarse domain has a resolution of 30km cessor (MCIPv3.6, Otte and Pleim, cal output2010) for was the used CMAQ simulations. to(Yarwood The et process Carbon the al., Bond-V (CB05) WRF2005) chemical meteorologi- and mechanism gas-phase chemistry the and AERO5 aerosol module and (Foley aqueous et chemistry, respectively. al., 2010) were used for the position of the atmosphere (HogrefeOdman et et al., ; 2001 al., Unal2007; etIm al., et2005; al., Kindap2010, et2011). al., The2006; Meteorology-Chemistry Interface Pro- of the atmosphere during thecasting selected model episode (WRF-ARW using v3.3, the Weather (Skamarockand Research and the and Community Klemp, Fore- Multiscale),2008 Air (http://wrfmodel.org/) QualityWRF-ARW model model (CMAQv4.7.1, isFoley widely et al., usedtions,2010). and also The its in ability the to regionin of reproduce interest the previous (the meteorological studies Eastern condi- (e.g., Mediterraneanhumidity basin)Im and has et been pressure proven al., fieldsWeather2010 , retrieved Forecasting from2011). (ECMWF) the The withhourly European 0.25 operational temporal Center temperature, resolution for wind, were Medium-Range usedthrough nudging. to The constrain following physical the options WRFwere in meteorological the used: WRF simulation WSM3 meteorological simulations microphysicstransfer scheme model) long-wave (Hong radiation et schemeradiation al. , (Mlawer scheme (Dudhia, et);2004 );1989 , al. RRTM NOAH1997); landYonsei (rapid University surface Dudhia model Planetary radiative short-wave (Chen Boundary and Layer, Dudhia schemeFritsch2001); cumulus (Hong parameterization and scheme Lim, 2006); (Kain, and2004). Kain- up to the heightet determined al., by2012). using Thisheight, a methodology Brunt–Väisälä semi-empirical frequency is formula in based suggestedfirst the on by free two (Sofiev three troposphere parameters input and arefireplace fire parameters: using derived radiative boundary output by power. from layer The the thethe meteorological WRF FRP meteorological conditions in simulation. evaluated the Whilethe at Sofiev the fire presence formula each is of means crucial that for a a correct correct vertical estimation allocation2.1.3 of of the the fire diurnal Meteorological emissions. and cycle Air of Quality modeling A series of model simulations were performed to reproduce the chemical composition 5 5 25 20 10 15 15 20 25 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 10km × apodized) 1 − erent algorithms, ff and 280 K) (Clerbaux et al., 2009), 1 − ) and its variability (covariance matrix a x erent wildfire emission inventories. ff 14 13 30km are used only to provide boundary and × 0.2 K at 950 cm ≈ are retrieved from IASI using the algorithm of (Van Damme 3 ), combined with a medium spectral resolution (0.5 cm 1 − from IASI 3 profile (averaged value expected, total column using look-up-tables of HRI built from simulated spectra under 3 . 2 erent emission inventories were used and created to describe the Antalya wildifre ff a priori ), which represent our best knowledge of the system (Turquety et, al. 2009). The MODIS data often contain large areas of missing values, especially during the Total columns of CO are from the FORLI (Fast-Optimal Estimation Retrievals on Di The air quality simulations are first performed without fire emission information on The MODIS instrument has near dailyWe global collect coverage with MODIS a AQUA swath width Level of 2 2330 Aerosol km. Products, collection 5.1 (MOD04, LAADS Total columns of NH a and a low instrumental noise ( S et al., ),2014 which isThe built retrieval on scheme the includes detection two method(HRI) steps. described is First by calculated a ( Walker from et so-called eachinto al., Hyperspectral spectrum a2011). Range NH measured Index by IASI. The HRI is then converted an fires, due to theclassification presence of of fire clouds, plumes as copresence clouds of (Yang et fire, al. plumesCO2011). and and NH clouds, and mis- Layers for IASI) near-real timeis retrieval software based (Hurtmans on et theminimizes, al., by2012). optimal iteratively The estimation updating retrieval method aference state between (OEM) vector an described (set observed by of and (Rodgers, unknown a parameters), simulated2000). the spectrum, It dif- using constrains defined by The Infrared Atmospheric sounding Interferometeris (IASI), a passive the remote-sensing first instrument ofsynchronous operating a in orbit series nadir on of mode board three, circlingvides MetOp-A in a a (Meteorological polar twice-daily Operational) sun- satellite. globala IASI coverage relatively pro- of small the footprint(Coheur Earth on et surface the al., (9.302009). ground a.m. Its (circular(645–2760 and large cm pixel and p.m. LT) with continuous with spectral 12 km coverage of diameter the at infrared nadir) region allow the atmospheric concentrations ofbe a measured variety (Coheur of et constituentsand al., in ammonia, the2009 ; both atmosphere emittedClarisse to in et large al., amounts2011), by vegetation including fires. carbon monoxide initial conditions to the finehorizontal resolution resolution, simulations. the For more the refined simulationserence GFAS1.1 at emission 10km information inventory represents on the biomassmain, ref- burning which activity is oversion compared the inventories with based Eastern two on MediterraneanWF_ABBA FRP newly do- and derived developed by LSA high SEVIRIformed temporal SAF. data to An resolution quantify with the additional two emis- total di simulation impact of without the di fire emissions was per- both reference domains. The GFAS1.0 inventorybiomass (Kaiser et burning al., emission2012) is information usedThe over to WRF/CMAQ provide the simulations coarse at 30km domain (covering all Europe). of mineral dust on PMfor concentrations the can simulated be episode. neglected over the region of interestepisode, and and used in the WRF-CMAQ simulation. Web-NASA). This product provides10 km AOT data at 0.55 µm with a spatial resolution of 2.1.4 Satellite observations MODIS AOT As a source of informationfected by on the the Antalya aerosol fire content atProduct in the that the beginning of atmosphere monitors August overglobally the 2008 the and we area ambient used over af- a the aerosol MODIS portion optical Aerosol of thickness the continents (AOT)Remer ( over et the al., 2005; oceans Levy et al., ). 2007 5 5 25 20 10 15 10 20 15 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erences erences 7–10 MW ff ff ≈ product used 3 20 MW) at the sub-satellite point. For ≈ 15 16 40 MW (and at extreme ≈ are undetected. 2 erently (Fig.2). The WF_ABBA data leads to lower emission estimates ff erences between GFASv1.1 and SEVIRI-based (WF_ABBA and LSA SAF) fire- the Antalya fire ff Minimum FRPs returned by the fire detection algorithm when applied to real SEVIRI Level 2 On the entire region, GFASv1.1 emission estimation is significantly higher than the Di On the other hand the impacts of coarse spatial resolution is balanced by the very The two SEVIRI-based FRP products used in this work describe the same fire The graph reveals the pronounced diurnal fire cycle driven by day/night di In Fig.2b the estimated FRP, based on SEVIRI observations, over the study area The Antalya fire was an extreme event also in terms of energy output. Fig- MODIS observations, when available, confirm the SEVIRI ones but, due to its depen- 1.5 data is of the order of SEVIRI-based one (WF_ABBA and LSAparable SAF on FRP-Pixel products) Antalya while fire theytions are area, are com- where the the highest (Fig.3b LSA and SAF1). Table FRP-Pixel based emission estima- 3.2 Magnitude of fire emissions over the Eastern Mediterranean basin andThe over hourly and daily averageover Total Particulate the Matter study (TPM) area emission andare rates, over integrated the presented Antalya in fire, Fig.3. betweenover 30 They the July same are 2008 areas plotted to and together 6 time August period. with 2008, the GFASv1.1 one (in green) induced emissions estimates, when they aredue integrated to over the the study area, presenceEastern are Europe. of mainly In agricultural fact, wasteintensity SEVIRI fires burning, coarse common spatial this resolution time is such of that the numerous year low in than those generated from LSA SAFwhile during both the products intense fire are period comparable of when 31 July–1 only August, smaller fires are present. Di high temporal resolution of the geostationarythe observations. complete Thus Antalya SEVIRI could fire capture struments life on cycle EOS that Aqua the andoverpasses. much Terra could higher not spatial describe resolution despite MODIS their in- four-times perepisode day di in atmospheric humidity, temperature andAntalya wind, fire, even the if, nocturnalpart during activity the of was second the also day eventFRP of very (from values the strong. of 31 8000 We MW July can (according to to notice 3 LSA how SAF August the FRP-Pixeldence 2008) product). on first was the scheduled particularly day overpass intense,not of EOS observe reaching AQUA the and TERRA, two this most1 instrument intense could and moments 2 August of 2008. the fire activity, both in the afternoon of MODIS, the minimum FRP(Schultz and detection, Wooster threshold2008). for reliably detected fire pixels is MODIS over the Antalya fire. and over Antalya fire betweenscale 30 for July all 2008 and thediurnal 6 results cycle August 2008 of figures is the is depicted. fires.) in (The In time local the time same (LT) graph that we is can more also easily see linked the FRP to observed the by various atmospheric conditions. For a detailed description of the NH VIRI observations. ure2a shows the spatiotemporalaverage FRP evolution as of estimated the by biomass WF_ABBA burning and in LSA terms SAF fire of algorithms daily by using SE- here, see (Van Damme et al., 2014). 3 Results: wildfire emission inventories 3.1 What SEVIRI saw SEVIRI captured biomass burningginning, activities as in confirmed the by31 province ground July of and reported Antalya monitored observations, the from in entire their lifetime the be- of early the afternoon fire till of the the end, the 5 August 2008. 5 5 10 20 20 25 15 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ff erent pixels of the ff ect di ff 17 18 emission rate for the same fire pixel at 15:00 LT is 2.5 ected by the burning. This level of description of the ff emission rate on 1 August 2008, for a strong emitting fire 2.5 erent emission rates in the pixels of the model grid that during ff erence is largest during peak burning. ff The vertical allocation of the PM By looking at this figure, we can appreciate refinement in terms of temporal allocation Previous studies have shown that vertical mixing of fire emission within theFigure5 PBLshows the is horizontal allocation of the TPMIn fire presence emission of at large wildfires the also beginning, the horizontal allocation of the emissions became (Garcia-Menendez et al., 2014 ) demonstrated that model performance could benefit Figure4a shows the hourly variation of FRP, Plume Injection height (Hp) (as defined after 18:00 LT. model domain during theemission same rate in day. Fig.5b In and fact, c,border during as 1 of August, we the the can Antalya ground fire see reportedemitting moved from from aerosol burned North–West with area SEVIRI di to based South–Eastthe TPM direction hours (see of also the Fig.2a), day became a from more accurately positioning emissions. Inpollutant fact the concentrations responsiveness to of small simulated variations fire also in reflects the horizontal a allocation strong of influence fire from emissions meteorological inputs. of the fire emission achieved by usingdescribed SEVIRI a FRP peak data. of Both emission SEVIRIaround at based 07:00 algorithms 04:00 LT LT for and WF_ABBA around and 14:00 09:00 LT and LT for a LSA second SAF, and maximum less intensity fire activity rapid, while correctly determining plume(Garcia-Menendez penetration et into al., the2014; free atmosphereKonovalov et is al., critical 2014). at the middle and at the end of thecritical, most because intense these day of fires can the travel Antalya over fire. a vast area and a fire emission inventories. depicted in Fig.4b. We can inferby that using the refinement SEVIRI in FRP theemissions, emission data, inventory, can in achieved terms lead of tothe a a parameters more more used detailed accurate temporal to verticalFor allocation evaluate example, allocation. of the according In the to hourly fact, LSA fire verticalthe the SAF fire distribution fire FRP activity characterization of is became data, the one very at fire of strong 15:00 LT on emissions. of the 1 fire August pixel selected in Fig.4, ejecting a large quantity of particulate matters abovefrom the Planetary Fig.4a Boundary that layer. We GFAS caneven hourly also if injection notice the height pixel isparameters based not used GFAS to constant FRP calculate during this doeslogical the information condition not day. in that In change Sofiev are fact, formula in hourly are changing. a driven by daily meteoro- time frame, the other horizontal allocations of the fire(Fig.5a). emission cannot be achieved with the daily GFASv1.1 by Sofiev formula) and PM the timing and progressionmodel of performance. fire related emissions is a viable approach to improve pixel in the model grid, belonging to the Antalya fire, as estimated by the three di 3.3 Temporal and spatial allocation ofA fire recent emissions study byequate (Garcia-Menendez estimate et of al. , emittedgridded2014) mass, domains has horizontal and shown and that, their verticalfires in timing distributions on addition of are air emissions to key quality.allocation According in input ad- fire-related to to emissions the successfully lie same modelThe in study same the better the analysis impacts characterizing demonstrated largest that of their potentiala the temporal fire gains response distribution. emission at related allocated downwind to to receptors each lasting hour produce 2–3 h, concluding that better approximating between the two algorithms,explain particularly why in the their di handling of pixel oversampling, could 5 5 15 20 10 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ffi concentra- 2.5 cient 0.81) and ffi 3 ) above the Planetary Bound- 3 − total columns with IASI measure- 3 erent fire emission inventories reproduce ff 20 19 cients 0.57) simulations (Fig. 10 ). No correlation is ffi erent fire emission inventories, along the maximum simulated AOT concentrations (more than 40 µg m ff 2.5 erent atmospheric dynamic. This can be observed more clearly if we look at ff 0.08) which could be explained by the more conservative approach used in − erent emission magnitude, timing and injection height estimated by the four emis- ected by the fire plume as evident from the concurrent visible wavelength imagery. In presence of a large biomass burning event, the vertical allocation of the fire emis- The coincident simulated changes in AOT due to the smoke plume dispersion based Figure7 shows the vertical cross sections of simulated changes in PM Very high PM ff ff the GFAS1.1 (Pearson’s Robserved coe between WF_ABBA simulation and the observations (Pearson’s R coe ments. We considered fortime this and purpose spatial only locationthe simulated of fire vertical plume IASI originated columns measurements fromThere at inside Antalya (red the is a dashed same selected a box inaveraged area good Fig. over that correlation11). the includes between selected area modeled for and the observed LSA IASI SAF (Pearson’s CO R total coe cient column WF_ABBA for the calculation Antalya fireemission emitted estimations. radiant This energy, which can results beCMAQ in seen (WF_ABBA) lower in simulation Fig. are10 a,simulations close where seem to CO to those total underestimate without columnshigh the from fires. concentrations the (Fig. CO Generally,10). the total CMAQ columns except for the LSA SAF at di the same outputs of the CMAQand9). simulations From 12 this h figures before, at weFRP-Pixel 03:00 can LT based of see 1 emission that August inventory only8 (Figs. 8c (Figs. theabove and9c) simulation 2000 m performed describes of with a LSA altitude clustermine SAF moving of that towards aerosols particular Cyprus. shape Thethis of same island, the that confirmed fire 12 by h the plume, later MODIS expanding will observations. upon deter- southern–west4.2 coast of Top-down information on total columns of CO and NH sion inventories. sions is an importantthe parameter spatiotemporal that evolution can of strongly the determinecan fire strongly the plume. exceed In correct the fact, description daily in of pletely maximum this di of case the the boundary top layer plume height height and catch a com- We compared the CMAQ simulated CO and NH subtracting the AOT simulatedThe without simulations including performed biomass burning with emission the inventory. four di on hourly estimates are shownLSA in SAF Fig.6c and for Fig.6f GFAS1.0, for6d Fig. WF_ABBA.available for in (Animations GFAS1.1, the covering Fig.6e Supplement.) 31 for The July changes to in 2 the August AOT 2008 due are to the fires are calculated by 4 Results: air quality model simulations 4.1 Smoke plume simulations During the first 3 days, thearea most were intense mostly period of southerly,to the which Antalya the transported fire, Mediterranean the winds Sea. over smoke6 Figure thewas fromshows study measured the the by smoke fire MODIS plume source visibleaerosol from optical wavelength region the imagery thickness Antalya product (Fig.6a) fire (Fig.6b) and ascan on it by notice 1 MODIS August that 2008 level the at 2 Fig. MODIS 14:30a14 LT. AOT First in data the of Supplement), all contain which we clouds. large is We probably areas due can of to also misclassification missing notice ofa values fire a (see plumes wrong as also attribution of low values of AOT to pixels strongly the observed plume features:bay an of intense Antalya plume that rightbased broadens downwind emission in of inventory, southern the the direction. plumedoes fire, But reaches in above only the the the with southern MODIS LSA observations. west SAF coast FRP-Pixel of Cyprus as it (white dashed lines in Fig.6c–f). tions due to the di ary Layer (PBL) (blackwith dashed the line LSA in SAFGFAS1.1 Fig.7) and (Fig.7c), are WF_ABBA while observed7a, (Fig. for lower b the concentration and CMAQ d are simulation respectively). simulated This with is GFAS1.0, a combined result of 5 5 20 25 15 10 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 and 8.4 erent fire ff 2.5 100 %) for most of ≥ erence. ff total columns and low thermal , the simulations performed us- 3 3 total columns averaged over the area 3 22 21 erent conversion factors available in literature illus- ff cients ranging from 0.38 to 0.91), except for 31 July PM ffi by IASI on 2 August a.m. Daily average total column for NH 2 cients ranging from 0.15 to 0.23) (see Fig. ).11 It implies that is the relative error on the retrieved column. − ffi σ and 2 /σ . (2) total column observations have a large relative error ( 1 i concentration along the fire plume7 (Figs. and9) shows that a better esti- i 3 x molecules cm = i ω 2.5 16 ω ω and CO, respectively. While they tend to be quite lower when integrated on the P P 10 × 2.5 = Also the impact of the use of di In comparison with IASI total columns of CO and NH The analysis of the CMAQ-simulated aerosol and trace gas transport from the An- The high correlation found between CMAQ simulation with LSA SAF-based emis- We conclude that SEVIRI observations allow refining biomass burning emissions, The SEVIRI based fire emission estimates are comparable with the GFASv1.1 ones A good positive correlation is found between each individual CO IASI observation IASI NH 2 X mation of the plumeLSA penetration SAF above based the emission PBL inventory)as (in led confirmed the to by simulation MODIS a performed observations more (Fig.6). with accurate the description of its features trates the large uncertainties of2). currently(Table available biomass burning emission estimates talya fire shows theterms of importance their of magnitude, a butThe also PM correct in estimation terms of of emission the timing emissions and vertical not distribution. only in ing GFASv1.1 and LSA SAF FRP-Pixelaccurate based representation fire of emission the inventories provideWF_ABBA temporal a based pattern one more tends of to emissions underestimatethe and the simulated concentration transport, fire of plume. while these species the along sions and IASI measurements (Fig. )10 rate shows description that of this dataset the provides emission theand emitted most by in accu- Antalya terms fire of both their in spatiotemporal terms distribution. of their magnitude which can subsequentlyorder be to used improve the in prediction ofof regional chemical large scale composition biomass of burning air the episodes. atmosphere quality in models presence like CMAQ in have been calculated using theet weighted al., averaging2014): method as following (Van Damme the CMAQ model provides aof relatively the accurate emission representation and of transport the in temporal comparison pattern to IASI. under study with average≈ values of the same order of magnitude (Fig. 12 ), reaching the measurements. This is due to relatively small NH contrasts above sea (Van Damme etplume al., is2014), located. where most Despite of this,poral the we variations currently can of studied observe fire modeled a and good measured correspondence NH between tem- entire Eastern Mediterranean basin. The presence(common of in low energetic Eastern agricultural Europe burning its during coarse summertime), spatial resolution, undetected could by be SEVIRI the main because cause of of this di and 40.1 Gg of COLSA are SAF, estimated while for thePM the GFASv1.1 entire based Antalya on episode from MODIS WF_ABBA estimates and are 7.4 and 27.3 Gg for when they describe the Antalya fire; for example 2.3 and 10.9 Gg of PM Where 5 Summary and conclusions We explored the use ofto WF_ABBA describe and biomass LSA burning emissions SAF ofranean (MSG-SEVIRI principal during based) pollutants a FRP over the products strong Eastern wildWe Mediter- fire analyzed event the occurred estimates in comparingand the1). them Table South with of the Turkey in MODIS August based 2008. GFASv1.1 (Fig.3 and the coincident modeled CO with the CMAQ simulation with the three di scenarios (Pearson’s R coe data (Pearson’s R coe 5 5 15 10 20 25 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , + ort ff man, ff ects of Climate Change on Air Pol- ff 23 24 airs (Prodex Arrangement 4000111403). S. Whit- ff This research was supported by the Turkish Science Foundation via ectiveness of the UNECE Heavy Metals Protocol and costs of possible additional mea- ff ce for Scientific, Technical and Cultural A We would like to thank Professor Martin Wooster at Kings College London for helping us Higher spatial resolution observations from a future imager in geostationary orbit We gratefully acknowledge support from the projects “E The research in Belgium was funded by the F.R.S.-FNRS and the Belgian State Federal doi:10.1029/2011gl047271, 2011. 14 Pommier, M., Razavi, A.,spheric Turquety, S., composition using Wespes, the C., thermal6041–6054, and infrared, doi:10.5194/acp-9-6041-2009 IASI/MetOp Coheur, 2009. sounder, P.-F.: Monitoring Atmos.14 Chem. of Phys., 9, atmo- of reactive trace speciesdoi:10.5194/acp-9-5655-2009, in5, 2009. 14 biomass burning plumes, Atmos. Chem. 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L., Mathur, R., Sar- Calle, A., Casanova, J.-L., and Gonzalez-Alonso, F.: Impact ofChen, point F. and spread Dudhia, function J.: of Coupling MSG- an advanced land surface-hydrology modelClarisse, with the L., Penn R’Honi, Y., Coheur, P.-F., Hurtmans, D., and Clerbaux, C.: Thermal in- Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, members of the biomass burningStudies group (CIMSS), at and the Jean Cooperativeat Institute M. the for Phillips Meteorological University at Satellite of the Wisconsin-Madison Space for Science their and assistance, Engineering guidance and Center support. (SSEC) References G. Baldassarre would like to thank Elaine M. Prins, Jason C. Brunner and Jay P. 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J., 5 5 10 30 15 25 10 20 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cients described in (Kaiser et al. , ) 2012 ffi 32 31 3181 1380.1 6267 697.5 Turkey Antalya fire 300.3548.4 50.8 243.2 1586.6 163.2 7645.71782.7 2234.4 9721.4 288.3 942.4 2250.6 1027.8 158 894.5199 287.6 10 559.1 50 551.8 37 019.9 1513.1 18 817.529 371.4 2286.9 10 948.5 17 507.158 115.2 7150 4846.2 102 613.0370 204.9 40 087.6 27 279.7 111 233.7 7448.2 x 2.5 2 SO NO PM OC 10 394.2BC 1493.5 1193.8 214.7 SpeciesCO Turkey Antalya fire 63 338.5NMHC 8373.4 5710.3 466.7 Land SAF 48 090.6 18 992.3 WF_ABBA 29 411.9 3967.1 Total Particulate Matter estimates [tons] in the study area and for Antalya fire from Total fire emission estimates of the principal pollutants [tons] in the study area and for cients. The estimates based on (Ichoku and Kaufman, 2005) are set in italics below the ffi ones referring to (Andreae and Merlet , 2001). (referring to Andreaecoe and Merlet, 2001) and (Ichoku and Kaufman, 2005) smoke emission Table 2. WF_ABBA and LSA SAF FRP-Pixel2008) products during using Antalya conversion fire lifetime factors (31 and July and emission 5 August coe Table 1. Antalya fire from WF_ABBAfire and lifetime LSA (31 SAF July FRP-PixelWF_ABBA and products and 5 and LSA SAF GFASv1.1 August during FRP-Pixel 2008). (in Antalya The bold) GFASv1.1 based values ones. are italic below the value for Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Total Fire Radiative Power (b) WF_ABBA (MSG-SEVIRI) detections of (b) 34 33 Burned area as reported by the Antalya Forestry department (c) LSA SAF (MSG-SEVIRI) detections of Antalya fire. Study area. Daily average FRP (MW) over the Antalya fire. (d) (FRP) detected over Eastern6 Mediterranean August and 2008. over The Antalya datasquare fire are indicate derived between the from FRP 30 observed thetion July over WF_ABBA by 2008 the the and and area two LSA of operational SAF MODIS the products. instruments. Antalya Open fire black at a lower temporal resolu- Figure 2. (a) Antalya fire. (right) and over a MODIS Blue Marble image (left). Figure 1. (a) Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and (a) emission rate 2.5 36 35 emission rate over the same pixel at 15:00 LT of the same day. 2.5 from 30 July to 6 August 2008. Cyan and blue line: hourly and daily (b) Temporal variation of FRP, Plume Injection height (Hp) and PM Total Particulate Matter emission rate observed over Eastern Mediterranean Vertical distribution of PM Figure 4. (a) for a strong emitting fire(b) pixel in the modelThe grid black belonging dashed to line the defines Antalya the fire PBL. on 1 August 2008. Figure 3. over Antalya fire WF_ABBA FRP derived. GreyGreen line: and GFASv1.1. black line: hourly and daily LSA SAF FRP-Pixel derived. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | based fire emission inventories. (f) Concurrent MODIS level 2 aerosol optical FRP based fire emission inventories. Black line: (b) 38 37 (c) and WF_ABBA (e) and WF_ABBA , LSA SAF (b) (d) erent simulations. ff , LSA SAF MODIS (true color composite from visible wavelengths) over the Eastern Mediter- , GFAS1.1 (a) (c) Horizontal allocation of Antalya fire emitting pixels during 1 August 2008 as derived by GFASv1.1 ground Reported Burned Areameans (On 24:00 the UTC upper of 1 right August). corner of this picture, 03:00 LT of 2 August ranean basin on 1 August 2008, 14:30 LT. Figure 6. (a) thickness product. Concurrent CMAQGFAS1.0 simulated changes inThe AOT changes due in to the firesdashed AOT line made are connects by the calculated using cellsAntalya by of fire subtracting the plume. model the grid MODIS background havingin AOT emissions. the AOT colorscale in maximum The is simulated the white the AOT di along same the as the one for the simulated changes Figure 5. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (c) concentration , LSA SAF 2.5 (b) , GFAS1.1 (a) based fire emission inventories. (d) concentration across the main plume of 2.5 40 39 and WF_ABBA (c) , LSA SAF (b) based fire emission inventories. The changes in the PM (d) , GFAS1.1 1 August 2008, 15:00 LT. Vertical cross section, across the concurrent maximum (a) 1 August 2008, 03:00 LT. CMAQ simulated changes in AOT due to fires made by using and WF_ABBA are calculated by subtracting the background emissions. Black dashed line defines the PBL. The changes in thedashed AOT line connects are the calculated cellsAntalya by of fire the subtracting plume. model the grid background having the emissions. maximum The simulated AOD white along the GFAS1.0 Figure 8. Figure 7. simulated AOT, showing theAntalya fire changes for in the CMAQ the simulations performed PM with GFAS1.0 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (c) concentration , LSA SAF 2.5 (b) , GFAS1.1 (a) linear regression and associated (b) concentration across the main plume of 2.5 42 41 erent simulations have been performed using various emission ff Temporal variations of modeled and observed CO emitted by Antalya (c) based fire emission inventories. The changes in the PM cients between modeled and observed CO total columns averaged over the ffi (d) Temporal variations of modeled and observed CO total columns from 31 July 1 August 2008, 03:00 LT. Vertical cross section, across the concurrent maximum and WF_ABBA are calculated by subtracting the background emissions. Black dashed line defines the PBL. Figure 10. (a) to 3 August 2008 averaged over the area of study and Pearson’s R coe same period and area. scenarios: CMAQ (GFAS1.1), CMAQ(without fires). (LSA SAF), CMAQ (WF_ABBA) and CMAQ (wo-fires) fire. The contribution of thearea Antalya fire has on been the CO evaluatedthe observed by upper and subtracting simulated left above the panel. the case Four minimum test di value of the respective time series in Figure 9. simulated AOT, showing theAntalya fire changes for in the CMAQ the simulations performed PM with GFAS1.0 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | total column from 31 July to 3 ), from top to bottom: by IASI, by 2 − cients between IASI CO and CMAQ ffi 44 43 Temporal variations of modeled and observed NH Daily a.m. and p.m. CO total columns (molec cm 3 August 2008 averagedsurements over the within area the of studiedsurements study. following The area, Eq. averaged weighted (2). values by are the a relative mean retrieval of error all mea- of ammonia mea- Figure 12. CMAQ(GFAS1.1), by CMAQ(LSA SAF) and3 by August CMAQ(WF_ABBA) 2008 between over 31 the July area 2008 of and study. Pearson’s R coe Figure 11. CO simulations are given below. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , (b) based fire emission inventories. 46 45 (e) and WF_ABBA (d) AQUA MODIS AOT over the Eastern Mediterranean basin from 1 to 4 August , LSA SAF Coarse and fine domains used for the WRF/CMAQ simulations. (c) Figure 14. (a) 2008. Concurred CMAQ simulated changes in AOT due to fires made by using GFAS1.0 GFAS1.1 Figure 13.