1. Introduction
For some chemical compounds such as carbon dioxide and carbon monoxide, the total annual emission from biomass burning is comparable to what is emitted from anthropogenic sources (Crutzen et al. 1979). Chemical fluxes arising from fires are therefore a non-negligible source of emissions in forecasting system of the atmospheric composition, such as the European Copernicus Atmosphere Monitoring Services (CAMS). Since 2012, the Global Fire Assimilation System (GFAS) has been a CAMS-operated offline analysis system that estimates fire emissions by converting the energy released during fire combustion into gases and aerosol fluxes (Kaiser et al. 2012; Di Giuseppe et al. 2016a).
GFAS uses observations of fire radiative power (FRP) from two MODIS sensors on board the Aqua and Terra satellites (Kaufman et al. 2003). FRP measures the heat power emitted by fires as a result of the combustion process and is directly related to the total biomass combusted (Wooster et al. 2005). Using land-use-dependent conversion factors, GFAS converts FRP into emission estimates of 44 smoke constituents, such as CO, CO2, CH4, black carbon, and organic matter components of the aerosols (Kaiser et al. 2012). These fluxes are then ingested in the model managed by the CAMS to produce daily forecasts of the chemical composition at the global scale. GFAS converts MODIS observations into emissions and does not explicitly forecast fire behavior or fire evolution. Also the model operated by the CAMS does not include a fire model component for predicting the evolution of fire emissions. The emissions estimated at the initial analysis time by the GFAS are kept constant during the 5-day forecast (Flemming et al. 2015).
Weather is the most important factor in modulating fire intensities where fuel is available (Flannigan et al. 2009). Weather also affects the fire emissions by changing the fuel moisture content (French et al. 2011, 2014; Di Giuseppe et al. 2016a). A simple functional link between weather and fire is provided by the class of fire danger models as they describe the impact that atmospheric conditions have on fuel availability. However simple, they tend to perform very well where biomass moisture is a limiting factor to fire sustainability, as in temperate and tropical rain forests, where fuel is abundant. On the other hand, their performance instead deteriorates in fuel-limited ecosystems such as semiarid, tropical, and temperate savannas and in the Mediterranean. In these regions, the vegetation conditions are persistently hot and dry but ignitions and fuel availability are scarce except on rare and difficult to predict occasions (Di Giuseppe et al. 2016a). Among others, the Canadian fire weather index (FWI) is one of the most commonly used fire danger rating system. It was developed as a metric for fire danger in a standard jack pine stand (Pinus banksiana) typical of Canadian forests (Van Wagner 1974) and it only uses meteorological inputs, such as air temperature, wind speed, relative humidity, and 24-h cumulative rainfall (Van Wagner 1987). The relative simplicity of its formulation has contributed to its popularity and has resulted in its extensive use in other climate regions that are very dissimilar to Canada (Taylor and Alexander 2006), such as Australia (Cruz and Plucinski 2007), New Zealand and Malaysia (Taylor and Alexander 2006), and Indonesia (de Groot et al. 2007; Field et al. 2009). The FWI has been shown to provide useful information worldwide (Di Giuseppe et al. 2016a).
In this paper, we propose the use of the fire weather index to predict changes in fire emissions during the CAMS forecast, building on the idea that there is a strong link between emissions released during fires and the weather. Linear changes in the FWI are translated into linear changes in fire emission; this assumption substitutes the previously adopted persistence assumption in which no evolution takes place from the initial analysis date. The new method is assessed using 5 months of daily forecasts from June to October 2015. As only limited information is available on the mass fluxes released into the atmosphere during fires, the validation of the new scheme is performed indirectly by looking at the overall quality of the forecast from the point of view of aerosol prediction.
2. Method
The CAMS uses the emissions 
The FWI calculation is performed in two steps. In the first step atmospheric temperature, humidity, precipitation, and wind modulate the fuel moisture content of three surface fuel components representative of typical fuel beds in a boreal forest. In the second step the fuel state is used to calculate two fire behavior indices: the rate of fire spread, which mostly depends on the wind, and the total fuel availability for combustion. The combination of these two latter indices provides a unit-less index of general fire intensity potential, generally referred to as the fire weather index (Van Wagner 1985; Stocks et al. 1989). FWI calculations are available daily through the Global ECMWF Fire Forecast (GEFF) system (Di Giuseppe et al. 2016a). To take into account uncertainties in the fire indices due to uncertainties in the weather parameters, GEFF produces an ensemble of values using the 51 weather forecast realizations of the ECMWF ensemble forecasting system (Buizza et al. 1999). Forecasts for temperature, humidity, wind, and precipitation undergo a temporal interpolation to local noon (see Di Giuseppe et al. 2016a); the results are then used as atmospheric forcing for the FWI forecasts. The ensemble mean is assumed to be the best FWI estimation.
By substituting Eq. (2) into Eq. (3), it is clear that linear increments in the FWI are transformed into linear increments in emissions for each of the constituents. The assumption of linearity between FWI and the emissions has been verified by looking at the relationship between FWI and FRP, where FRP values are themselves linearly related to the emissions. Figure 1 takes into account all FRP > 0.5 W m−2 data points observed by MODIS (Aqua plus Terra) during 5 months from June to October 2015 and the corresponding FWI calculated from the GEFF. Data have been aggregated over a regular 9-km grid and three functions were tested. The linear model has a correlation coefficient that is similar to the quadratic model and was chosen because of its simplicity.
Relationship between FRP and FWI. The analysis is performed for 5 months and uses only data for which FRP > 0.5 W m−2 and fires lasted for more than 1 day. Several functional dependencies are shown with their regression coefficients.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Relationship between FRP and FWI. The analysis is performed for 5 months and uses only data for which FRP > 0.5 W m−2 and fires lasted for more than 1 day. Several functional dependencies are shown with their regression coefficients.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Relationship between FRP and FWI. The analysis is performed for 5 months and uses only data for which FRP > 0.5 W m−2 and fires lasted for more than 1 day. Several functional dependencies are shown with their regression coefficients.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Since 
Since FWI is positive defined and open ended, 
The pdf for the modulation factor 
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf for the modulation factor
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf for the modulation factor
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
In summary, even if 
3. Experiment setup
To understand the impact of the fire emission modification in the CAMS some extensive fire events in 2015 were analyzed. The year 2015 was characterized by a strong El Niño that favored extensive fires in Indonesia in the autumn (Benedetti et al. 2016). The fires in Alaska were also particularly severe in June and July. A series of experiments covering both events was performed using an experimental version of the CAMS model that releases biomass burning aerosol emissions at an injection height computed by a plume rise model that has recently been embedded into GFAS (Rémy et al. 2017). There are, however, significant uncertainties associated with these injection height estimates, which has an impact on the forecast plume. Two different runs were carried out to cover the Alaska fires during June–July 2015 and fires in Indonesia during September–October 2015. The control run uses the standard GFAS v1.2 configuration of Kaiser et al. (2012), where persistence is assumed during the 5-day forecast integration. This experiment is simply flagged as the control (CTRL). The second experiment uses the GFAS v1.2 configuration but applies the modification explained in the previous section where the emissions are modulated by the FWI function. This experiment is tagged as FWI.
As a benchmark model simulation, we also use the CAMS interim reanalysis dataset, CAMSiRA, in which, among other observations, fire emissions are constrained to the observed GFAS values (Flemming et al. 2017). CAMSiRA comprises 6-hourly reanalyses of the atmospheric composition for the period 2003–15. It has a horizontal resolution of about 110 km on a reduced Gaussian grid and a vertical discretization of 60 levels from the surface to a model top of 0.1 hPa. The most relevant differences between the CTRL and FWI experiments are that CAMSiRA uses an older model version than the operational model and several observations are assimilated during the reanalysis integration. Total columns of carbon monoxide from the Measurements of Pollution in the Troposphere (MOPITT) instrument (Deeter et al. 2003), MODIS aerosol optical depth (AOD), and several ozone total column and stratospheric profile retrievals constrain the values of the AOD fields. Therefore, differences among the reanalyses, the CTRL, and the FWI cannot be attributed to the different treatments of the fire emissions alone. Still, the comparison helps in sizing the improvement provided by the FWI implementation.
Two horizontal and vertical resolutions were tested. The high-resolution experiment has a TL-1279 horizontal spectral resolution, which corresponds to a grid-box size of about 9 km, and has 137 vertical hybrid sigma-pressure levels. Experiments at the operational CAMS resolution TL-255 (which corresponds to a grid-box size of about 80 km) and with 60 vertical hybrid sigma-pressure levels were also performed. Table 1 summarizes the experiment setup.
Summary of the experiment setup, where TL represents the spectral truncation of the CAMS model and defines the horizontal resolution. This representation is used in the model to compute some horizontal derivatives and for the implicit computations.
4. Results
a. Modulation factor behavior
FRP changes due to the introduction of the modulation factor are shown as the global probability density function (pdf) of 
Daily variations in FWI translate into 
The pdf of the model errors. The model error is expressed as forecast minus observed FRP. Different curves refer to different lead times.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf of the model errors. The model error is expressed as forecast minus observed FRP. Different curves refer to different lead times.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf of the model errors. The model error is expressed as forecast minus observed FRP. Different curves refer to different lead times.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
One interesting aspect is that positive biases are reduced more than negative ones. This behavior can be explained by noting that by using FWI values only when fire events are observed the FWI distribution is conditionally sampled toward high values (Di Giuseppe et al. 2016a). This is evident from Fig. 4, where the PDF of the normalized FWI at the analysis time (t0) is shown. The vast majority of FWI values are above the upper quartile (>75%), as one would expect. From Fig. 4 it is also clear that FWI reaches an asymptotic high value above which the FWI increase is limited. However, precipitation and humidity rises can produce a large FWI decrease, which will translate into large negative values for the modulation factor. Despite this asymmetric behavior, the error in FRP is highly reduced (mean bias = 0.16 W m−2).
The pdf of the normalized FWI at the analysis time t0. Only points where a fire is detected by MODIS are considered. The FWI normalization was performed using an historical time series of FWI values for the period 1980–2014 and then estimating the cumulative distribution function and its inverse [see Di Giuseppe et al. (2016a) for more details]. For example, a value of 0.5 identifies points that have a 50% chance to occur.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf of the normalized FWI at the analysis time t0. Only points where a fire is detected by MODIS are considered. The FWI normalization was performed using an historical time series of FWI values for the period 1980–2014 and then estimating the cumulative distribution function and its inverse [see Di Giuseppe et al. (2016a) for more details]. For example, a value of 0.5 identifies points that have a 50% chance to occur.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The pdf of the normalized FWI at the analysis time t0. Only points where a fire is detected by MODIS are considered. The FWI normalization was performed using an historical time series of FWI values for the period 1980–2014 and then estimating the cumulative distribution function and its inverse [see Di Giuseppe et al. (2016a) for more details]. For example, a value of 0.5 identifies points that have a 50% chance to occur.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
b. Case studies
The Indonesian fires in 2015 extended to vast areas and were long lived. The fires started in early September and only diminished in intensity by the beginning of November, thanks to the onset of seasonal precipitation. Human-caused fires are not unusual in Indonesia, mostly as a result of logging practices (Field et al. 2009; Spessa et al. 2015). Further, in 2015 the effect of human practices was amplified by the extensive and persistent droughts that affected parts of Southeast Asia. These conditions contributed to making the situation far worse than usual. Areas of forest, particularly on peat, that are normally too wet to burn turned into fires, leading to extremely widespread and severe burning activities. The fire season began in August, and by September much of Sumatra, Kalimantan, and Singapore and parts of Malaysia and Thailand were covered in thick smoke, which affected the respiratory health of millions of people (Marlier et al. 2013). At its peak, visibility was reduced to less than 10% of normal in places, similar to conditions in El Niño years (Wang et al. 2004). Large parts of Borneo could not be seen from space. Preliminary estimates suggest that greenhouse gas emissions from the burning exceeded those of Japan’s mean annual fossil fuel emissions (Benedetti et al. 2016). The remnant pollution stretched halfway around the equator, even after the worst of the fire was over.
In summer 2015 intense wild fires also affected Alaska. Because of the unusually warm conditions, fires burned all the way down to the mineral soil. When this occurs, the frozen ground loses its insulation and the permafrost can thaw, sometimes so much that the ground sinks and becomes bumpy and hilly as it loses solid ice mass. The fire characteristics in boreal forests are very much different from the fires in Southeast Asia and those in Indonesia. Since these latter burns were sustained by an almost unlimited peat fuel availability, they tended to be long lasting and of low intensity while forest fires are usually short lived and intense. The boreal forest zone consists of a mixture of conifers (white and black spruce, jack pine, tamarack, and balsam fir), for which the FWI model was specifically calibrated; we therefore expect the FWI to be a better predictor of fire danger for this type of vegetation cover.
To gain an understanding of how the new modulation factor modifies the CAMS emissions, it is useful to consider the Alaska fire event in 2015. This was a collection of intense but localized fires that had their maximum of intensity on 7 July 2015, which was taken as the reference forecast. This study concentrates on the first 48 h of the forecast, but the same results were found to be valid at all lead times. By analyzing the difference between the FRP observed at t0 (7 July 2015) and t0 + 48 h (9 July 2015) (Fig. 5), it is clear that a decrease in fire activity was recorded.
Differences in FRP from the MODIS satellite as provided by the GFAS analysis at t0 + 48 h forecast and t0, where t0 is 7 Jul 2015. A negative increment marks a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Differences in FRP from the MODIS satellite as provided by the GFAS analysis at t0 + 48 h forecast and t0, where t0 is 7 Jul 2015. A negative increment marks a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Differences in FRP from the MODIS satellite as provided by the GFAS analysis at t0 + 48 h forecast and t0, where t0 is 7 Jul 2015. A negative increment marks a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The modulation factor 
Modulation factor increment between the t0 + 48 h forecast and t0, where t0 is 7 Jul 2016: (a) simulation performed at the operational CAMS resolution and (b) the high-resolution run. A negative increment corresponds to a prediction for a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Modulation factor increment between the t0 + 48 h forecast and t0, where t0 is 7 Jul 2016: (a) simulation performed at the operational CAMS resolution and (b) the high-resolution run. A negative increment corresponds to a prediction for a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Modulation factor increment between the t0 + 48 h forecast and t0, where t0 is 7 Jul 2016: (a) simulation performed at the operational CAMS resolution and (b) the high-resolution run. A negative increment corresponds to a prediction for a decrease in fire activity.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
As the modulation was negative at t0 + 48 h, the forecast aerosol emissions were also decreased in the experimental run when compared with the standard CAMS configuration that uses persistence at all lead times (Fig. 7). The effect is remarkable in the high-resolution experiment but becomes negligible at the standard CAMS resolution (not shown) as a consequence of the lower 
Differences in the forecast aerosol load between the FWI experiment and the operational CAMS setup (CTRL). A negative increment corresponds to a prediction for a decrease in aerosol.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Differences in the forecast aerosol load between the FWI experiment and the operational CAMS setup (CTRL). A negative increment corresponds to a prediction for a decrease in aerosol.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Differences in the forecast aerosol load between the FWI experiment and the operational CAMS setup (CTRL). A negative increment corresponds to a prediction for a decrease in aerosol.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Given the scarcity of the observations, it is challenging to verify if this reduction is also improving CAMS model errors. An encouraging result is nevertheless provided by the comparison with the AERONET station (Holben et al. 1998) at Bonanza Creek (64.997°N, 142.379°W), which is in the middle of the fire event and provides measurements of aerosol optical depth at 0.55 μm that can be compared with the model simulations (Fig. 8).
AOD forecast interpolated to the AERONET station of Bonanza Creek (64.997°N, 142.379°W). The interpolation is performed to the nearest grid point. The few available observations highlight a decrease in fire activity, which is well forecast by the introduction of the new modulation factor.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
AOD forecast interpolated to the AERONET station of Bonanza Creek (64.997°N, 142.379°W). The interpolation is performed to the nearest grid point. The few available observations highlight a decrease in fire activity, which is well forecast by the introduction of the new modulation factor.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
AOD forecast interpolated to the AERONET station of Bonanza Creek (64.997°N, 142.379°W). The interpolation is performed to the nearest grid point. The few available observations highlight a decrease in fire activity, which is well forecast by the introduction of the new modulation factor.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
The analysis of the Alaska fire allows for a detailed understanding of how the introduction of 
To understand the potential impact of 
(top) Bias and (bottom) RMSE for the CAMS AOD predictions in comparison with the 215 and 65 AERONET stations (a),(b) covering the globe and (c),(d) Southeast Asia only. The averaging period covers 2 months of simulations during the 2015 Indonesian fire. The FWI experiment is compared with the operational CAMS simulations. Only the standard CAMS resolution is shown (low-resolution experiment).
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
(top) Bias and (bottom) RMSE for the CAMS AOD predictions in comparison with the 215 and 65 AERONET stations (a),(b) covering the globe and (c),(d) Southeast Asia only. The averaging period covers 2 months of simulations during the 2015 Indonesian fire. The FWI experiment is compared with the operational CAMS simulations. Only the standard CAMS resolution is shown (low-resolution experiment).
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
(top) Bias and (bottom) RMSE for the CAMS AOD predictions in comparison with the 215 and 65 AERONET stations (a),(b) covering the globe and (c),(d) Southeast Asia only. The averaging period covers 2 months of simulations during the 2015 Indonesian fire. The FWI experiment is compared with the operational CAMS simulations. Only the standard CAMS resolution is shown (low-resolution experiment).
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
c. Comparison with reanalysis
Verification is performed against the global AERONET network (Fig. 10). Only the first 24 h of forecast were used in the comparison. The reanalysis bias is, as expected, substantially smaller than the operational forecast since AOD observations are assimilated during the 24 h. The remaining bias represents the uncertainty due to the model component. The operational forecast, CTRL, displays a persistent negative bias that is constantly improved by the introduction of the new scheme. Also the RMSE is, in general, improved with the introduction of the new FWI scheme.
Daily skill scores for the FWI, CTRL, and reanalysis datasets spanning September–October 2015. The verification uses the first 24-h forecast for all of the experiments. In the reanalysis, AOD observations are assimilated.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Daily skill scores for the FWI, CTRL, and reanalysis datasets spanning September–October 2015. The verification uses the first 24-h forecast for all of the experiments. In the reanalysis, AOD observations are assimilated.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
Daily skill scores for the FWI, CTRL, and reanalysis datasets spanning September–October 2015. The verification uses the first 24-h forecast for all of the experiments. In the reanalysis, AOD observations are assimilated.
Citation: Journal of Applied Meteorology and Climatology 56, 10; 10.1175/JAMC-D-16-0405.1
5. Summary
There are several studies that show the difficulties of estimating fire emissions starting from fire detection. For example, French et al. (2011, 2014) show how fuel consumption is dependent on fuel moisture and how this is mostly controlled by weather. Not including the influence of atmospheric condition on fire burning produces large errors in the estimation of the associated emission.
This study has explored a simple way to include the effects of weather on fuel combustion by using changes in FWI to predict changes in the aerosol emissions from fires during the forecast. The differences between the operational forecast produced by the atmospheric composition model operated by the CAMS, which uses persistence of the fire contribution observed at the beginning of the forecast, and forecasts made by the new schemes indicated that FWI is a good predictor for fire intensity. FWI also provides a good metric for reducing or enhancing emissions with lead times up to 5 days.
A statistical analysis was performed for the Indonesian fires in 2015, which were extremely long lived (September–November) and of wide geographical extent due to a strong ENSO. The calculation of mean model biases and root-mean-square errors for the Southeast Asia AERONET stations and even the global AERONET network showed a small but positive benefit provided by the introduction of the FWI modulation factor at all lead times. However, even if this new approach is thought to be an improvement over persistence, it has to be stressed that it is still a simplification relative to the use of a fire predictive model, which could include interactive processes between vegetation, fuel, and weather.
Acknowledgments
This work was funded by EU Horizon 2020 Project ANYWHERE (Contract 700099) and Global Fire Contract 389730 between the Joint Research Centre and ECMWF. An extended version of the material presented in this paper is also available as a non-peer-reviewed ECMWF technical report (Di Giuseppe et al. 2016b). We also thank Luke Jones at ECMWF for the support in the AERONET verification. We are grateful for the comments of three anonymous reviewers.
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