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

1Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Turkey 2Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, Madison, WI, USA 3Spectroscopie de l’Atmosphère, Service de Chimie Quantique et de Photophysique, Université Libre de Bruxelles (U.L.B), Brussels, Belgium 4Southwest Anatolia Forest Research Institute, Antalya, Turkey 5King’s College, London, UK 6European Center for Medium-Range Weather Forecasts, Reading, UK 7Max Planck Institute for Chemistry, Mainz, Germany


Introduction
Fire is the main cause of forest destruction in countries of the Mediterranean basin, (JRC, 2008) where the fire season starts in April and can last until the end of November.In terms of emissions, the biomass burning contribution to PM 2.5 were comparable with the anthropogenic contribution during recent years (e.g., Sofiev et al., 2009) Emissions from open vegetation burnings are increasingly recognized as an important parameter in atmospheric modeling, and their accurate description is important for specific regions and seasons and for specific episodes.Recent studies have demonstrated that open biomass burning events, although episodic, may have important effects on the photochemistry in the Eastern Mediterranean (Poupkou et al., 2014).Furthermore the impact of biomass burning is expected to become more important in the Southeastern Mediterranean according to future scenarios on climate change indicate (Tolika et al., 2012;Migliavacca et al., 2013).
The recent improvements of air quality models, such as the Community Multiscale Air Quality (CMAQ) model, permit to simulate the chemical composition of the atmosphere at fine spatial and temporal resolutions.Therefore also emission inventories must be provided with higher level of detail, and this is particularly important for forest fires, which are characterized by high spatial and temporal variations.According to (Garcia-Menendez et al., 2014), in addition to adequate estimates of emitted mass, successfully modeling the impact of fires on air quality depends on an accurate spatiotemporal allocation of emissions.
Satellite remote sensing provides automated powerful means of locating and characterizing open vegetation burnings.Infrared fire detection from satellites takes advantage of the fact that as target temperature increases radiance increases faster at the shortwave end of the spectrum as opposed to the longwave end.By using two atmospheric window spectral channels, such as the 3.9 and 11 µm, fire locations and characteristics can be determined.Furthermore the cost of continuous monitoring of fires by ground and aircraft observations is prohibitive, whereas monitoring with satellite observations can be achieved in a cost effective way and with broader coverage, especially in remote areas.However, satellite observations are limited to cloud-free scenes.In the end, satellite observations can still provide a better understanding of fire issues.
Although designed for operational weather forecasting and not specifically for fire detection, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Figures series of the Meteosat Second Generation (MSG) geostationary satellites shows great potential for fire detection and characterization.Since this instrument is employed on a geostationary platform, it can sample a large region with high temporal frequency (one observation per 15 min).Under certain conditions (no opaque clouds, solar reflection, etc.) SEVIRI delivers important information on the temporal variability of active fires.
It has been demonstrated in small-scale experiments that the amount of radiant energy liberated per unit time during a vegetation fire, the so-called Fire Radiative Power (FRP), is related to the rate at which the fuel biomass is being consumed.Spaceborne sensors able to observe the Middle Infrared (MIR) spectral radiance around 3.9 µm that is coming from the Earth can measure the radiative component of the energy released by open fires (Wooster et al., 2003).The estimate of biomass burning emissions from FRP avoids using the complex parameters of fuel loading and burned area.Thus, it is a robust approach for the global estimates of biomass burning emissions from satellite observations (e.g.Ichoku and Kaufman, 2005;Heil et al., 2010;Yang et al., 2011).
Observed FRP has been successfully used to calculate biomass combusted from wildfires using the SEVIRI radiometer onboard the geostationary Meteosat-8 platform in Africa (Roberts et al., 2005) and MODIS data both in Africa (Ellicott et al., 2009) and globally (Kaiser et al., 2009;Darmenov and da Silva, 2013).
Different satellite techniques have been developed using high temporal resolution multi-spectral data in order to detect and characterize fire activity.The Wildfire Automated Biomass Burning Algorithm (WF_ABBA) and the EUMETSAT LSA SAF Fire Radiative Power provide operational fire radiative power products based on SEVIRI observations using different algorithms.
In 2008 most of the large forest damages in Europe occurred in the Southeastern Mediterranean countries, which were under the influence of extreme weather conditions that facilitated fire ignition and spread.The country that was most heavily damaged was Turkey, where the forest fire danger was high during the period May to October, and especially during the period July to September, which had very high tempera-Introduction

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Full  We calculate gridded FRP and emission inventories for a large region of the Eastern Mediterranean during the life time of the Antalya fire from the two operational SEVIRI FRP products.They are compared to the daily emission inventory based on MODIS at different spatial resolutions, 0.5 • ×0.5 • (GFASv1.0), and 0.1 • ×0.1 • (GFASv1.1).All four emission inventories are subsequently used as input in the Community Multiscale Air Quality (CMAQ) model.The simulated smoke plumes are then validated by comparison with the Aerosol Optical Thickness product from the Moderate Resolution Imaging Spectroradiometer (MODIS) and CO and NH 3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI), which were previously used to track the emission and transport of pollution and to measure reactive trace species in biomass burning plumes during the intense 2007 Greek forest fires (Turquety et al., 2009;Coheur et al., 2009).Introduction

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SEVIRI Fire Radiative Power
Fire Radiative Power (FRP) is a measure of the radiant energy liberated per unit time from burning vegetation via the rapid oxidation of fuel carbon and hydrogen.(Wooster et al., 2003) approximated the FRP as the difference of the Middle Infrared (MIR) spectral radiances between a fire pixel and ambient background pixels in a linear form.An alternative method uses the Dozier approximation of the instantaneous fire area and temperature (Dozier, 1981) to evaluate the FRP over the detected fire pixel by using Thermal and Middle infrared satellite observations.Following the Wooster approach the LSA SAF algorithm uses the SEVIRI band centered at 4 µm infrared window to evaluate the FRP associated with the detected fire pixels.While the WF_ABBA one uses a combination of both methods, depending on the availability of instantaneous area and temperature estimation over the detected fire pixel.
At the sub satellite point, the actual field of view of the SEVIRI thermal channels is about 4.8 km with an overlap of 1.6 km.The consequence of this effect is that radiance coming from a single location is at times present in several neighboring pixels.Although this fact may not significantly impact other applications, it is very important in the case of fire detection, since fires present complex geometric structures that make it difficult to identify the affected pixels (Calle et al., 2009).Also, the background temperature that is used in the FRP calculation is derived from neighboring pixels.The 4 µm channel of the SEVIRI sensor has a saturation temperature of 335 K. Saturation of the SEVIRI pixels will not impact on the ability of the fire algorithms to detect fires, but it will lead to an underestimation of the true fire radiative power for large fires, which can contribute significantly to an underestimation of FRP in a given region.
The fire products described in this paper are derived using SEVIRI level-1.5 radiometrically calibrated and geometrically corrected imagery product provided by the EU-Introduction

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Full METSAT LSA SAF.Resampling and regridding actions included in level-1.5 can act to mask the fire signal and impact both fire detection and characterization.
Previous studies have demonstrated that SEVIRI data can be used operationally to assist the detection of fires by improving the reliability in fire announcements (Laneve et al., 2006;Stoyanova et al., 2008) and allowing real time fire front monitoring in Southern Europe.The role of SEVIRI is especially useful as the fires increase in number and size (Sifakis et al., 2011).

Fire characterization data
The Wildfire Automated Biomass Burning Algorithm (WF_ABBA) is a dynamic contextual algorithm developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin-Madison using multispectral GOES data to monitor biomass burning in the Western Hemisphere (Prins andMenzel, 1992, 1994;Prins, 2001).Recently a global version of the WF_ABBA (Version 6.5) has been applied to MSG-SEVIRI data.The global geostationary WF_ABBA Version 6.5.006 includes the following: -Fire mask indicating where fire detection is not possible: opaque cloud coverage; block-out zones due to solar reflectance, clouds, extreme view angles, biome type, bad data, etc.
-Revised ASCII fire product output: latitude; longitude; satellite view angle; pixel size; observed 4 and 11 µm brightness temperatures; instantaneous estimates of fire size, temperature, and FRP; biome type; fire confidence flag.
The Meteosat Fire Radiative Power-Pixel (FRP-Pixel) product is derived at the Eumetsat Land Surface Analysis Satellite Applications Facility (LSA SAF: http://landsaf.meteo.pt/)from 15 minute temporal resolution Meteosat SEVIRI observations.In this paper we often refer to this product only as LSA SAF (instead of using its full name LSA SAF FRP-Pixel) in order to make its comparative analysis with other satellite products Introduction

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Full easier to read; in fact, just like the homologous WF_ABBA, the FRP is one type of information delivered by this product.The product is created using an operational version of the prototype geostationary active fire detection and characterization algorithm presented in Roberts and Wooster (2008).Most uses of the FRP-Pixel product have so far been focused on Africa (e.g.Roberts et al., 2009), but the product also provides information on fires burning across Europe and also parts of South America.The FRP-Pixel Product is freely available in near-real time or in archived from the LSA SAF.For each processed region the FRP-Pixel algorithm generates two output files -FRP List: this has the format of a list.One entry for every fire pixel having an estimated FRP value.For each fire pixel, the FRP and an exhaustive list of relevant information such as the fire pixel background window mean temperature is also provided.
-FRP Quality Flag: this file provides the actual status of each pixel in the selected region, whether it contains a detected fire or not, and a number of other conditions such as whether the pixel is cloudy or clear sky, coastline etc. (Lattanzio et al., 2009).
Due to the higher spatial resolution observations and wide usage of the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire products, these are taken to be the reference standard against which the SEVIRI FRP product is assessed.
The Global Fire Assimilation System version 1.1 (GFASv1.1)calculates biomassburning emissions by assimilating Fire Radiative Power observations from the MODIS instruments onboard the Terra and Aqua satellites.It provides daily emissions on a global 0.1 The comparison of SEVIRI FRP to GFAS emissions should be regarded as a comparison between two independent data sets rather than a validation using a reference data set (Schultz and Wooster, 2008).Introduction

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Full In order to create a fire emission inventory at the same grid of GFASv1.1, we started from WF_ABBA and LSA SAF FRP-Pixel products and generated a gridded FRP product at 0.1 • × 0.1 • resolution containing area integrated FRP totals corrected for partial cloudiness at the grid-cell scale.The gridding method is the one described in (Govaerts et al., 2007).The FRP derived from the SEVIRI image (LSA SAF and WF_ABBA) acquired at time t are summed over a regular grid of resolution G • × G • grid box.For each grid point (X G , Y G ) the total FRP is: ( Where f s (t, i G , j G ) is the fraction of clear sky pixels1 over land within the G Then we averaged the gridded FRP over 1 h time period.As a consequence, the SEVIRI based fire emission inventories will have hourly time resolution in contrast with MODIS based one with constant diurnal pattern.

Emission factors for WF_ABBA and LSA SAF
The GFAS emission inventory represents our reference information on biomass burning activity over the Eastern Mediterranean domain.In order to make our SEVIRI FRP Introduction

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Full based fire emission inventory comparable with this dataset we followed a similar procedure as described in (Kaiser et al., 2012), to evaluate emission rates of main biomass burning pollutants calculated from WF_ABBA and LSA SAF FRP-Pixel fire characterization data.This approach also helps to discuss the differences between the three biomass burning emission inventories over the case study area and over the Antalya fire.
Accordingly to this, the hourly FRP gridded product was converted to major contaminant emission rate using conversion factors to dry matter combustion rate and emission factors based on an updated version of the compilation by Andreae and Merlet (2001).
By using satellite retrievals and top-down estimates of particulate matter, (Kaiser et al., 2012) concluded that emissions of particulate matter calculated with this method need to be boosted to reproduce the global distribution of organic matter and black carbon.According to this, in this work we used the proposed aerosol enhancement factor of 3.4.An alternative approach to estimate biomass burning smoke aerosols is to directly relate these to FRP, using smoke emission coefficients [kg MJ −1 ] proposed by (Ichoku and Kaufman, 2005).Specifically, the values we assigned to the main land cover types are: 0.06 kg MJ −1 for savannah and tropical forest, 0.084 kg MJ −1 for agriculture, and 0.02 kg MJ −1 for extra tropical forest.Table 2 shows a significant difference between smoke aerosol emissions evaluated with this approach and with the one described in (Kaiser et al., 2012), that are the ones we finally used in this work to simulate the atmospheric composition of Antalya fire with the CMAQ air quality model.

Vertical distribution
Vertical distribution of fire emission is critical for air quality simulation in presence of energetic wild fire episodes when the plume top height can strongly exceed the daily maximum of the boundary layer height.Below this height, fast turbulent mixing rapidly redistributes the emissions through out the boundary layer.Introduction

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Full The emissions calculated for each hour were distributed uniformly from the ground up to the height determined by using a semi-empirical formula suggested by (Sofiev et al., 2012).This methodology is based on three input parameters: boundary layer height, Brunt-Väisälä frequency in the free troposphere and fire radiative power.The first two parameters are derived by the meteorological conditions evaluated at each fireplace using output from the WRF meteorological simulation.While the presence of the FRP in the Sofiev formula means that a correct estimation of the diurnal cycle of the fire is crucial for a correct vertical allocation of the fire emissions.

Meteorological and Air Quality modeling
A series of model simulations were performed to reproduce the chemical composition of the atmosphere during the selected episode using the Weather Research and Forecasting model (WRF-ARW v3.3, (Skamarock and Klemp, 2008), (http://wrfmodel.org/)and the Community Multiscale Air Quality model (CMAQv4.7.1, Foley et al., 2010).The WRF-ARW model is widely used and its ability to reproduce the meteorological conditions, also in the region of interest (the Eastern Mediterranean basin) has been proven in previous studies (e.g., Im et al., 2010Im et al., , 2011)).The operational temperature, wind, humidity and pressure fields retrieved from the European Center for Medium-Range Weather Forecasting (ECMWF) with 0.25 • × 0.25 • lat/lon horizontal resolution and 6hourly temporal resolution were used to constrain the WRF meteorological simulation through nudging.The following physical options in the WRF meteorological simulations were used: WSM3 microphysics scheme (Hong et al., 2004); RRTM (rapid radiative transfer model) long-wave radiation scheme (Mlawer et al., 1997); Dudhia short-wave radiation scheme (Dudhia, 1989); NOAH land surface model (Chen and Dudhia, 2001); Yonsei University Planetary Boundary Layer scheme (Hong and Lim, 2006); and Kain-Fritsch cumulus parameterization scheme (Kain, 2004).

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Full cessor (MCIPv3.6,Otte and Pleim, 2010) was used to process the WRF meteorological output for the CMAQ simulations.The Carbon Bond-V (CB05) chemical mechanism (Yarwood et al., 2005) and the AERO5 module (Foley et al., 2010) were used for the gas-phase chemistry and aerosol and aqueous chemistry, respectively.
The WRF-CMAQ model simulations were performed for two nested domains.
A of mineral dust on PM concentrations can be neglected over the region of interest and for the simulated episode.Different emission inventories were used and created to describe the Antalya wildifre episode, and used in the WRF-CMAQ simulation.
The air quality simulations are first performed without fire emission information on both reference domains.The GFAS1.0 inventory (Kaiser et al., 2012) is used to provide biomass burning emission information over the coarse domain (covering all Europe).The WRF/CMAQ simulations at 30 km × 30 km are used only to provide boundary and initial conditions to the fine resolution simulations.For the simulations at 10 km × 10 km horizontal resolution, the more refined GFAS1.1 emission inventory represents the reference information on biomass burning activity over the Eastern Mediterranean domain, which is compared with two newly developed high temporal resolution emission inventories based on FRP derived by SEVIRI data with two different algorithms, WF_ABBA and LSA SAF.An additional simulation without fire emissions was performed to quantify the total impact of the different wildfire emission inventories.

Satellite observations MODIS AOT
As a source of information on the aerosol content in the atmosphere over the area affected by the Antalya fire at the beginning of August 2008 we used the MODIS Aerosol Product that monitors the ambient aerosol optical thickness (AOT) over the oceans globally and over a portion of the continents (Remer et al., 2005;Levy et al., 2007).
The MODIS instrument has near daily global coverage with a swath width of 2330 km.We collect MODIS AQUA Level 2 Aerosol Products, collection 5.1 (MOD04, LAADS Web-NASA).This product provides AOT data at 0.55 µm with a spatial resolution of 10 km 2 .Introduction

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Full The MODIS data often contain large areas of missing values, especially during the fires, due to the presence of clouds, copresence of fire plumes and clouds, and misclassification of fire plumes as clouds (Yang et al., 2011).

CO and NH 3 from IASI
The Infrared Atmospheric sounding Interferometer (IASI), the first of a series of three, is a passive remote-sensing instrument operating in nadir mode circling in a polar sunsynchronous orbit on board MetOp-A (Meteorological Operational) satellite.IASI provides a twice-daily global coverage of the Earth surface (9.30 a.m. and p.m. LT) with a relatively small footprint on the ground (circular pixel with 12 km diameter at nadir) (Coheur et al., 2009).Its large and continuous spectral coverage of the infrared region (645-2760 cm −1 ), combined with a medium spectral resolution (0.5 cm −1 apodized) and a low instrumental noise (≈ 0.2 K at 950 cm −1 and 280 K) (Clerbaux et al., 2009), allow the atmospheric concentrations of a variety of constituents in the atmosphere to be measured (Coheur et al., 2009;Clarisse et al., 2011), including carbon monoxide and ammonia, both emitted in large amounts by vegetation fires.
Total columns of CO are from the FORLI (Fast-Optimal Estimation Retrievals on Layers for IASI) near-real time retrieval software (Hurtmans et al., 2012).The retrieval is based on the optimal estimation method (OEM) described by (Rodgers, 2000).It minimizes, by iteratively updating a state vector (set of unknown parameters), the difference between an observed and a simulated spectrum, using constrains defined by an a priori profile (averaged value expected, x a ) and its variability (covariance matrix S a ), which represent our best knowledge of the system (Turquety et al., 2009).
Total columns of NH 3 are retrieved from IASI using the algorithm of (Van Damme et al., 2014), which is built on the detection method described by (Walker et al., 2011).The retrieval scheme includes two steps.First a so-called Hyperspectral Range Index (HRI) is calculated from each spectrum measured by IASI.The HRI is then converted into a NH 3 total column using look-up-tables of HRI built from simulated spectra under Introduction

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Full The graph reveals the pronounced diurnal fire cycle driven by day/night differences in atmospheric humidity, temperature and wind, even if, during the second day of the Antalya fire, the nocturnal activity was also very strong.We can notice how the first part of the event (from 31 July to 3 August 2008) was particularly intense, reaching FRP values of 8000 MW (according to LSA SAF FRP-Pixel product).
MODIS observations, when available, confirm the SEVIRI ones but, due to its dependence on the scheduled day overpass of EOS AQUA and TERRA, this instrument could not observe the two most intense moments of the fire activity, both in the afternoon of 1 and 2 August 2008.Introduction

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Full On the entire region, GFASv1.1 emission estimation is significantly higher than the SEVIRI-based one (WF_ABBA and LSA SAF FRP-Pixel products) while they are comparable on Antalya fire area, where the LSA SAF FRP-Pixel based emission estimations are the highest (Fig. 3b and Table 1).Differences between GFASv1.1 and SEVIRI-based (WF_ABBA and LSA SAF) fireinduced emissions estimates, when they are integrated over the study area, are mainly due to the presence of agricultural waste burning, common this time of the year in Eastern Europe.In fact, SEVIRI coarse spatial resolution is such that numerous low intensity fires2 are undetected.
On the other hand the impacts of coarse spatial resolution is balanced by the very high temporal resolution of the geostationary observations.Thus SEVIRI could capture the complete Antalya fire life cycle that the much higher spatial resolution MODIS instruments on EOS Aqua and Terra could not describe despite their four-times per day overpasses.
The two SEVIRI-based FRP products used in this work describe the same fire episode differently (Fig. 2).The WF_ABBA data leads to lower emission estimates than those generated from LSA SAF during the intense fire period of 31 July-1 August, while both products are comparable when only smaller fires are present.Differences Introduction

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Full between the two algorithms, particularly in their handling of pixel oversampling, could explain why the difference is largest during peak burning.

Temporal and spatial allocation of fire emissions
A recent study by (Garcia-Menendez et al., 2014) has shown that, in addition to adequate estimate of emitted mass, horizontal and vertical distributions of emissions in gridded domains and their timing are key input to successfully model the impacts of fires on air quality.According to the same study the largest potential gains related to allocation fire-related emissions lie in better characterizing their temporal distribution.
The same analysis demonstrated that the fire emission allocated to each hour produce a response at downwind receptors lasting 2-3 h, concluding that better approximating the timing and progression of fire related emissions is a viable approach to improve model performance.
Figure 4a shows the hourly variation of FRP, Plume Injection height (Hp) (as defined by Sofiev formula) and PM 2.5 emission rate on 1 August 2008, for a strong emitting fire pixel in the model grid, belonging to the Antalya fire, as estimated by the three different fire emission inventories.
By looking at this figure, we can appreciate refinement in terms of temporal allocation of the fire emission achieved by using SEVIRI FRP data.Both SEVIRI based algorithms described a peak of emission at 04:00 LT and around 14:00 LT and a second maximum around 07:00 LT for WF_ABBA and 09:00 LT for LSA SAF, and less intensity fire activity after 18:00 LT.
The vertical allocation of the PM 2.5 emission rate for the same fire pixel at 15:00 LT is depicted in Fig. 4b.We can infer that the refinement in the emission inventory, achieved by using SEVIRI FRP data, in terms of a more detailed temporal allocation of the fire emissions, can lead to a more accurate vertical allocation.In fact, the FRP is one of the parameters used to evaluate the hourly vertical distribution of the fire emissions.For example, according to LSA SAF fire characterization data, at 15:00 LT of 1 August the fire activity became very strong on the fire pixel selected in Fig. 4, ejecting a large 17 Introduction

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Full quantity of particulate matters above the Planetary Boundary layer.We can also notice from Fig. 4a that GFAS hourly injection height is not constant during the day.In fact, even if the pixel based GFAS FRP does not change in a daily time frame, the other parameters used to calculate this information in Sofiev formula are driven by meteorological condition that are hourly changing.
Previous studies have shown that vertical mixing of fire emission within the PBL is rapid, while correctly determining plume penetration into the free atmosphere is critical (Garcia-Menendez et al., 2014;Konovalov et al., 2014).
Figure 5 shows the horizontal allocation of the TPM fire emission at the beginning, at the middle and at the end of the most intense day of the Antalya fire.
In presence of large wildfires also the horizontal allocation of the emissions became critical, because these fires can travel over a vast area and affect different pixels of the model domain during the same day.In fact, as we can see from SEVIRI based TPM emission rate in Fig. 5b and c, during 1 August, the Antalya fire moved from North-West border of the ground reported burned area to South-East direction (see also Fig. 2a), emitting aerosol with different emission rates in the pixels of the model grid that during the hours of the day became affected by the burning.This level of description of the horizontal allocations of the fire emission cannot be achieved with the daily GFASv1.1 (Fig. 5a).(Garcia-Menendez et al., 2014) demonstrated that model performance could benefit from more accurately positioning emissions.In fact the responsiveness of simulated fire pollutant concentrations to small variations in the horizontal allocation of fire emissions also reflects a strong influence from meteorological inputs.Introduction

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Full During the first 3 days, the most intense period of the Antalya fire, winds over the study area were mostly southerly, which transported the smoke from the fire source region to the Mediterranean Sea. Figure 6 shows the smoke plume from the Antalya fire as it was measured by MODIS visible wavelength imagery (Fig. 6a) and by MODIS level 2 aerosol optical thickness product (Fig. 6b) on 1 August 2008 at 14:30 LT.First of all we can notice that the MODIS AOT data contain large areas of missing values (see also Fig. 14a in the Supplement), which is probably due to misclassification of fire plumes as clouds.We can also notice a wrong attribution of low values of AOT to pixels strongly affected by the fire plume as evident from the concurrent visible wavelength imagery.
The coincident simulated changes in AOT due to the smoke plume dispersion based on hourly estimates are shown in Fig. 6c  Figure 7 shows the vertical cross sections of simulated changes in PM 2.5 concentrations due to the different fire emission inventories, along the maximum simulated AOT (white dashed lines in Fig. 6c-f).
Very high PM 2.5 concentrations (more than 40 µg m −3 ) above the Planetary Boundary Layer (PBL) (black dashed line in Fig. 7) are observed for the CMAQ simulation with the LSA SAF (Fig. 7c), while lower concentration are simulated with GFAS1.0,GFAS1.1 and WF_ABBA (Fig. 7a, b and d respectively).This is a combined result of 19 Introduction

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Full In presence of a large biomass burning event, the vertical allocation of the fire emissions is an important parameter that can strongly determine the correct description of the spatiotemporal evolution of the fire plume.In fact, in this case the top plume height can strongly exceed the daily maximum of the boundary layer height and catch a completely different atmospheric dynamic.This can be observed more clearly if we look at the same outputs of the CMAQ simulations 12 h before, at 03:00 LT of 1 August (Figs. 8 and 9).From this figures we can see that only the simulation performed with LSA SAF FRP-Pixel based emission inventory (Figs.8c and 9c) describes a cluster of aerosols above 2000 m of altitude moving towards Cyprus.The same that 12 h later will determine that particular shape of the fire plume, expanding upon southern-west coast of this island, confirmed by the MODIS observations.

Top-down information on total columns of CO and NH 3
We compared the CMAQ simulated CO and NH 3 total columns with IASI measurements.We considered for this purpose only simulated vertical columns at the same time and spatial location of IASI measurements inside a selected area that includes the fire plume originated from Antalya (red dashed box in Fig. 11).There is a good correlation between modeled and observed IASI CO total column averaged over the selected area for the LSA SAF (Pearson's R coefficient 0.81) and the GFAS1.1 (Pearson's R coefficients 0.57) simulations (Fig. 10).No correlation is observed between WF_ABBA simulation and the observations (Pearson's R coefficient −0.08) which could be explained by the more conservative approach used in WF_ABBA for the calculation Antalya fire emitted radiant energy, which results in lower emission estimations.This can be seen in Fig. 10a, where CO total columns from the CMAQ (WF_ABBA) simulation are close to those without fires.Generally, the CMAQ simulations seem to underestimate the CO total columns except for the LSA SAF at high concentrations (Fig. 10).

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Full A good positive correlation is found between each individual CO IASI observation and the coincident modeled CO with the CMAQ simulation with the three different fire scenarios (Pearson's R coefficients ranging from 0.38 to 0.91), except for 31 July PM data (Pearson's R coefficients ranging from 0.15 to 0.23) (see Fig. 11).It implies that the CMAQ model provides a relatively accurate representation of the temporal pattern of the emission and transport in comparison to IASI.
IASI NH 3 total column observations have a large relative error (≥ 100 %) for most of the measurements.This is due to relatively small NH 3 total columns and low thermal contrasts above sea (Van Damme et al., 2014), where most of the currently studied fire plume is located.Despite this, we can observe a good correspondence between temporal variations of modeled and measured NH 3 total columns averaged over the area under study with average values of the same order of magnitude (Fig. 12), reaching ≈ 2×10 16 molecules cm −2 by IASI on 2 August a.m.Daily average total column for NH 3 have been calculated using the weighted averaging method as following (Van Damme et al., 2014): Where ω = 1/σ 2 and σ is the relative error on the retrieved column.

Summary and conclusions
We explored the use of WF_ABBA and LSA SAF (MSG-SEVIRI based) FRP products to describe biomass burning emissions of principal pollutants over the Eastern Mediterranean during a strong wild fire event occurred in the South of Turkey in August 2008.We analyzed the estimates comparing them with the MODIS based GFASv1.1 (Fig. 3 and Table 1).
The SEVIRI based fire emission estimates are comparable with the GFASv1.1 ones when they describe the Antalya fire; for example 2.3 and 10.9 Gg of PM 2.5 and 8.4 Introduction

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Full and 40.1 Gg of CO are estimated for the entire Antalya episode from WF_ABBA and LSA SAF, while the GFASv1.1 based on MODIS estimates are 7.4 and 27.3 Gg for PM 2.5 and CO, respectively.While they tend to be quite lower when integrated on the entire Eastern Mediterranean basin.The presence of low energetic agricultural burning (common in Eastern Europe during summertime), undetected by SEVIRI because of its coarse spatial resolution, could be the main cause of this difference.Also the impact of the use of different conversion factors available in literature illustrates the large uncertainties of currently available biomass burning emission estimates (Table 2).
The analysis of the CMAQ-simulated aerosol and trace gas transport from the Antalya fire shows the importance of a correct estimation of the emissions not only in terms of their magnitude, but also in terms of emission timing and vertical distribution.The PM 2.5 concentration along the fire plume (Figs. 7 and 9) shows that a better estimation of the plume penetration above the PBL (in the simulation performed with the LSA SAF based emission inventory) led to a more accurate description of its features as confirmed by MODIS observations (Fig. 6).
In comparison with IASI total columns of CO and NH 3 , the simulations performed using GFASv1.1 and LSA SAF FRP-Pixel based fire emission inventories provide a more accurate representation of the temporal pattern of emissions and transport, while the WF_ABBA based one tends to underestimate the concentration of these species along the simulated fire plume.
The high correlation found between CMAQ simulation with LSA SAF-based emissions and IASI measurements (Fig. 10) shows that this dataset provides the most accurate description of the emission emitted by Antalya fire both in terms of their magnitude and in terms of their spatiotemporal distribution.
We conclude that SEVIRI observations allow refining biomass burning emissions, which can subsequently be used in regional scale air quality models like CMAQ in order to improve the prediction of chemical composition of the atmosphere in presence of large biomass burning episodes.Introduction

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Full Higher spatial resolution observations from a future imager in geostationary orbit would help to realize improved fire detection and characterization products in the effort to fill the gap we observed with available polar observations of low intense fire activity.
The Supplement related to this article is available online at doi:10.5194/acpd-15-1-2015-supplement.ucts, Remote Sens. Environ., 86, 83-107, 2003. 4, 6 Yang, E.-S., Christopher, S. A., Kondragunta, S., and  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | coarse domain has a resolution of 30 km × 30 km(192 × 160 cells)  and it is covering all Europe.A fine domain has the resolution of 10 km × 10 km (186 × 156 cells), centered in the Marmara Sea region and including south Turkey (see Fig.13in the Supplement), where the studied fire episode occurred, close to the city of Antalya.24 vertical layers, from surface to ca. 26 km, are used for both domains, the layer thickness increases from surface to the top, and the first 8 levels have a spacing of ≈ 100 m.The initial chemical concentrations and boundary conditions for the coarse domain were provided from the Monitoring Atmospheric Composition and Climate (MACC) data service, which provide a comprehensive reanalysis of atmospheric composition for the period 2003-2010 (http://gmes-atmosphere.eu/,Inness et al., 2013), while the output of the coarse domain was used to create initial concentrations and boundary conditions for the nested domain.The TNO/MACC_2005 emission inventory (Denier van der Gon et al., 2005) was used for anthropogenic sources of the main gaseous and aerosol atmospheric pollutants (CO, NO x , SO 2 , NMVOC, NH 3 , PM 2.5 and PMcoarse).The emissions from vegetation, biogenic volatile organic compounds (BVOC, e.g.isoprene and terpenes) were estimated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN,Guenther et al., 2006) according to the simulated temperature and radiation fields from the meteorological model.Sea salt aerosol emissions are calculated on-line by CMAQ model as a function of wind speed(Kelly et al., 2009).Mineral dust emissions, were not included in this study, nevertheless during the studied episode dust outbreaks from North Africa and Arabian Peninsula were not forecasted over the Mediterranean Sea and South Turkey (not shown, BSC-DREAM8b v1.0, http://www.bsc.es/earth-sciences/mineral-dust/catalogo-datos-dust), the impact Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |various atmospheric conditions.For a detailed description of the NH 3 product used here, see(Van Damme et al., 2014).burning activities in the province of Antalya from their beginning, as confirmed by ground reported observations, in the early afternoon of the 31 July and monitored the entire lifetime of the fire till the end, the 5 August 2008.The Antalya fire was an extreme event also in terms of energy output.Figure2ashows the spatiotemporal evolution of the biomass burning in terms of daily average FRP as estimated by WF_ABBA and LSA SAF fire algorithms by using SE-VIRI observations.In Fig.2bthe estimated FRP, based on SEVIRI observations, over the study area and over Antalya fire between 30 July 2008 and 6 August 2008 is depicted.(The time scale for all the results figures is in local time (LT) that is more easily linked to the diurnal cycle of the fires.)In the same graph we can also see the FRP observed by MODIS over the Antalya fire.
Discussion Paper | Discussion Paper | Discussion Paper | 3.2 Magnitude of fire emissions over the Eastern Mediterranean basin and over the Antalya fire The hourly and daily average Total Particulate Matter (TPM) emission rates, integrated over the study area and over the Antalya fire, between 30 July 2008 to 6 August 2008, are presented in Fig. 3.They are plotted together with the GFASv1.1 one (in green) over the same areas and time period.
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | for GFAS1.0,Fig. 6d for GFAS1.1,Fig. 6e for LSA SAF and Fig. 6f for WF_ABBA.(Animations covering 31 July to 2 August 2008 are available in the Supplement.)The changes in the AOT due to the fires are calculated by subtracting the AOT simulated without including biomass burning emission inventory.The simulations performed with the four different fire emission inventories reproduce the observed plume features: an intense plume right downwind of the fire, above the bay of Antalya that broadens in southern direction.But only with LSA SAF FRP-Pixel based emission inventory, the plume reaches the southern west coast of Cyprus as it does in the MODIS observations.
Discussion Paper | Discussion Paper | Discussion Paper | different emission magnitude, timing and injection height estimated by the four emission inventories.
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tures, very low humidity and effective wind.In Turkey, the coastline, which starts from Hatay and extends over the Mediterranean and Aegean up to Istanbul, has the highest fire risk.In other words, approximately 60 % (12 million ha) of Turkey's forest area is located in fire sensitive areas(JRC, 2009).A large forest fire occurred in Turkey's most touristic province of Antalya on 31 July 2008 (Fig.1).It burned for 5 consecutive days and affected 15 795 ha of forestland mainly dominated by Turkish Red Pine (Pinus brutia Ten.), typical fire adapted ecosystems of Eastern Mediterranean basin (Kavgacıet al., 2010).In this fire, tens of homes and many farming building were destroyed.During the fire suppression works, 227 technical personals, 1680 fire fighters, 75 forest workers, 20 local managers, 1450 villagers and 80 gendarmes were employed.In those works, 197 fire suppression trucks with sprinklers, 45 bulldozers, 38 trailers, 5 road graders, 63 pickups, 9 helicopters and 7 planes were occupied (Internal fire hazard report).In this paper, we investigate the applicability of the available SEVIRI-based FRP products for air quality simulations with the Community Multiscale Air Quality (CMAQ) model, and compare to simulations based on the daily fire emissions derived from MODIS.

Table 1 .
Total fire emission estimates of the principal pollutants[tons]in the study area and for Antalya fire from WF_ABBA and LSA SAF FRP-Pixel products and GFASv1.1 during Antalya fire lifetime (31 July and 5 August 2008).The GFASv1.1 values are italic below the value for WF_ABBA and LSA SAF FRP-Pixel (in bold) based ones.