Biomass burning activities can produce large quantities
of smoke and result in adverse air quality conditions in regional
environments. In Canada, the Environment and Climate Change Canada (ECCC)
operational FireWork (v1.0) air quality forecast system incorporates
near-real-time biomass burning emissions to forecast smoke plumes from fire
events. The system is based on the ECCC operational Regional Air Quality
Deterministic Prediction System (RAQDPS) augmented with near-real-time
wildfire emissions using inputs from the Canadian Forest Service (CFS)
Canadian Wildland Fire Information System (CWFIS). Recent improvements to
the representation of fire behaviour and fire emissions have been
incorporated into the CFS Canadian Forest Fire Emissions Prediction System
(CFFEPS) v2.03. This is a bottom-up system linked to CWFIS in which hourly
changes in biomass fuel consumption are parameterized with hourly forecasted
meteorology at fire locations. CFFEPS has now also been connected to
FireWork. In addition, a plume-rise parameterization based on fire-energy
thermodynamics is used to define the smoke injection height and the
distribution of emissions within a model vertical column. The new system,
FireWork v2.0 (FireWork–CFFEPS), has been evaluated over North America for
July–September 2017 and June–August 2018, which are both periods when western Canada
experienced historical levels of fire activity with poor air quality
conditions in several cities as well as other fires affecting northern
Canada and Ontario. Forecast results were evaluated against hourly surface
measurements for the three pollutant species used to calculate the Canadian
Air Quality Health Index (AQHI), namely PM
With 28 % of the world's boreal forest (552 million ha) and 9 % of the
world's forests, Canada experiences frequent wildland fires
(Natural Resources Canada, 2018). These fires are an integral part
of the forest life cycle: they regulate pests, release essential nutrients,
and open the forest canopy to new growth. Key tree species such as jack and
lodgepole pines require the heat from fires to rejuvenate seedlings and
facilitate new growth (Block et al., 2016). However, from
the 19th until the later part of the 20th century, active fire
suppression campaigns across North America resulted in an accumulation of
fuel and an increase in tree density and fuel continuity in forests, which,
upon ignition, can result in large intense wildfires
(Block et al., 2016). In the past decades, both Canada
and the United States have experienced wildland fires with large areas
burned, causing significant economic costs and loss of life. In Canada, the
area burned by fires has significantly increased in this past decade
compared to the last 55 years
(Landis et al., 2018), and for the provinces of Alberta (AB) and British Columbia
(BC), the costliest fire seasons with the most people displaced in the last
few decades took place in 2016 and 2017, respectively (Abbott
and Chapman, 2018; MNP LLP, 2017). Similarly, according to statistics from
the US National Interagency Fire Center, the six years with the largest
annual fire burned area (
Large-scale wildfires can pose direct threats to life and property, but they
also produce large amounts of smoke with significant quantities of
atmospheric pollutants. Primary air pollutants such as fine particulate
matter (PM
In Canada, Environment and Climate Change Canada (ECCC) is mandated to
provide air quality (AQ) forecasts for populated communities across the
country (see Table S1 in the Supplement for a list of the
acronyms used in this paper). Air quality forecasts are disseminated to the
public as the Air Quality Health Index (AQHI)
(Stieb et al., 2008). The AQHI, which was
developed jointly with Health Canada, is a simple-to-understand threat level
index with a scale from 1 to
ECCC supports the AQHI program through the development and application of
operational numerical AQ forecast models. Since 2001, the operational
Regional Air Quality Deterministic Prediction System (RAQDPS) has provided
twice-daily, 48 h forecasts of
Other numerical AQ forecast systems similar to FireWork are available in
North America, albeit based on different approaches. The experimental High-Resolution Rapid Refresh-Smoke system operated by the US National Oceanic
and Atmospheric Administration (NOAA) provides 36 h forecasts for the
continental US utilizing emissions derived from satellite fire radiative power (FRP) and the WRF-Chem model without considering atmospheric chemistry
(
The predictive skills of chemical transport models (CTMs), especially those
that account for wildfire activity as does FireWork, depend critically on
the accuracy of the input emissions. In most regional CTMs, anthropogenic
emissions are based on annual national emissions inventories processed with
activity adjustments to be hourly and gridded to the model domain with
proxy spatial surrogates (Matthias et al., 2018).
On the other hand, biomass burning emissions, because of their high spatial
and temporal variability, require NRT updates of fire information, hourly
processing, and allocation to domain grid cells representing the fire
location. The quantification of biomass burning emissions and
parameterization of emissions distribution within AQ modelling systems have
been topics of recent research. Zhang et al. (2014)
demonstrated the sensitivity of the WRF-Chem CTM aerosol loading,
atmospheric transport, and radiative feedback to emissions inputs from seven
different fire emissions inventories.
Garcia-Menendez et al. (2014) showed
the high variability of modelled PM
In addition to the overall quantification of biomass burning emissions,
emission injection heights are another important parameter in simulating the
transport of smoke in global- and regional-scale CTMs
(Paugam et al., 2016). Fire emissions must
be vertically distributed within model grid columns to account for the
heights at which fires release emissions into the atmosphere. This is a
small-scale process that depends on the atmospheric environment and the
intensity of vertical transport generated by factors such as fire heat flux,
fire size, entrainment, and turbulence. An initial injection height either
within or above the planetary boundary layer (PBL) has profound consequences
for the transport of smoke constituents and, in turn, on surface PM
Recently, the Canadian Forest Service (CFS) of Natural Resources Canada has
developed the Canadian Forest Fire Emission Prediction System (CFFEPS) to
improve the representation of biomass burning emissions for AQ model
applications. CFFEPS improves the current fire emissions processing used by
FireWork through additional process-specific considerations, including an
updated North American fuel map, closer integration with forecast
meteorology for treating fire behaviour, updated emission factors, and an
efficient fire injection plume height parameterization based on a
fire-energy thermodynamic approach (Anderson et al., 2011).
The combined changes are aimed at improving the overall emission estimates
from biomass burning and the spatiotemporal representation of biomass
burning emissions for input to a CTM simulation. In this work, we have
integrated ECCC's FireWork system with CFFEPS v2.03 (referred to as FireWork
v2.0 or FireWork–CFFEPS) to improve NRT processing of biomass burning
emissions. The integration is aimed at improving FireWork forecasts of
surface-level PM
In this paper, we describe CFFEPS itself and how it is integrated with the FireWork system, and we then evaluate the changes in predictive skill of surface pollutant concentration forecasts important for regional AQHI. FireWork–CFFEPS system performance is also compared to that of the current operational FireWork system (referred to as FireWork v1.0 or FireWork-Ops). We evaluate the model forecast skill with standard model performance statistical metrics for one recent fire season in 2017 (July–September). In Sect. 2 we first describe the current FireWork modelling system (FireWork-Ops), the new CFFEPS, and its integration into the FireWork system (FireWork–CFFEPS). Section 3 provides details of the model experiment setup for forecast evaluation and presents the forecast comparison with the operational FireWork system. This is followed by a discussion in Sect. 4 and summary and conclusions in Sect. 5.
The ECCC operational Regional Air Quality Deterministic Prediction System
(RAQDPS) and the FireWork system with NRT biomass burning emissions were
previously described in detail
(Munoz-Alpizar
et al., 2017; Pavlovic et al., 2016a, b) so only a summary is provided
here. The RAQDPS is ECCC's operational regional AQ forecast system that
provides short-term forecasts of surface-level concentrations of PM
The boundary of the FireWork and RAQDPS domain is indicated by the
red outline. The four continental subregions (ECAN, EUSA, WCAN, WUSA)
considered for model performance evaluation are denoted by different
colours. The spatial extent of AB
The GEM-MACH chemistry is in effect a multi-phase, multi-pollutant CTM that
considers the interactions of gas-, aqueous-, and particle-phase chemical
components. The gas-phase chemistry mechanism is based on an updated version
of the ADOM-2 mechanism with 42 species and 114 reactions
(Lurmann and Stockwell, 1989). The aqueous-phase chemistry
mechanism is based on an updated version of the ADOM mechanism with 13
species and 25 reactions (Fung et al.,
1991). PM chemical composition is represented with eight chemical
components: sulfate, nitrate, ammonium, elemental carbon, primary organic
matter, secondary organic matter, crustal material, and sea salt. The
operational version of GEM-MACH assumes a simplified two-bin PM size
distribution with fine PM and coarse PM aerodynamic diameter size bins of
0–2.5 and 2.5–10
Emission files used by the RAQDPS include emissions from both anthropogenic
and biogenic sources. The anthropogenic emission inventories that are
considered are updated every few years. For the 2017 operational runs
considered here, RAQDPS anthropogenic emission files were based on the 2010
Canadian national Air Pollutant Emissions Inventory (APEI), the 2011 US
National Emissions Inventory (NEI), and the 1999 Mexican NEI. These
inventories were processed using the SMOKE emissions processing system to
generate files of hourly, gridded, chemically speciated emissions fields
(Zhang et al., 2018). Biogenic
emissions are calculated online in the RAQDPS based on the algorithm from
BEIS version 3.09 with BELD3-format vegetation land cover for Canada and the
US. It is worth noting that the RAQDPS anthropogenic input emissions were
updated in September 2018 based on the 2013 Canadian APEI, a projected 2017 US
NEI, and the 2008 Mexican NEI (Moran et al., 2018). In order to understand
the impact of using inventories for older base years, Table S2 compares
2010–2011 and 2017 inventory values for several western Canadian provinces
and northwestern US states. Over this period,
The current operational version of FireWork (FireWork-Ops) is identical to
the RAQDPS except for the inclusion of NRT biomass burning emissions. The
fire information used by FireWork is obtained in real time from the CFS
operational Canadian Wildland Fire Information System (CWFIS) (
At each fire hotspot location CWFIS assigns local noontime meteorology
(surface temperature, humidity, 10 m open wind speed, and cumulative
rainfall from the past 24 h) from measurements or GEM model forecasts and
modelled fire characteristics based on the Canadian Forest Fire Behavior
Prediction (FBP) system (Forestry Canada Fire Danger Group,
1992), including total fuel consumption for the associated fire and fuel
types. Inputs to FBP include fuel type, elevation, slope steepness, slope
direction, and outputs from the Canadian Forest Fire Weather Index (FWI)
system used to estimate fuel moisture (Lawson and Armitage,
2008). In FireWork-Ops, the area burned (per day) is assumed to be 38.5 ha per hotspot (350 m burn radius). The estimated daily total fuel
consumption for each hotspot is then combined with emission factors from the
USFS Fire Emission Production Simulator (FEPS), a component of the BlueSky
modelling framework (Larkin et al., 2009), to
calculate daily emissions of PM
Operationally, the FireWork system is run twice per day at 00:00 and 12:00 UTC
during the Canadian wildfire season from 1 April to 31 October. The RAQDPS
is run on the same schedule but throughout the year. FireWork and RAQDPS
have the same continental domain, as depicted in Fig. 1, and the same 772
by 642 latitude–longitude grid with 0.9
Although the FireWork system has demonstrated improved forecast skill for
PM
The Canadian Forest Fire Emissions Prediction System (CFFEPS) is a new model to predict smoke plume development for Canada. Currently, the system consists of a fire-growth model, a fire emissions model, and a thermodynamic-based model to predict the vertical penetration height of a smoke plume from fire energy. CFFEPS makes use of outputs from CWFIS and incorporates the Canadian Forest Fire Danger Rating System (CFFDRS), including the Canadian Forest Fire Weather Index (FWI) system (Van Wagner, 1987) and the FBP system (Forestry Canada Fire Danger Group, 1992) in order to allow for adjustments based on hourly forecast meteorological fields. The new model also follows techniques used in FEPS and CONSUME 3.0, both developed by the USFS (Anderson et al., 2004; Prichard et al., 2006).
The prediction of smoke emissions and the energy generated from wildland fires requires estimating the amount of forest fuel consumed by fire, which in turn involves estimating the mass of fuel consumed; this is a product of area burned and fuel consumed per unit area.
Fire growth is dependent on fuel type, fuel moisture, weather conditions, terrain, and fire suppression activities. While a number of fire-growth models exist, ranging from simple elliptical growth to more sophisticated models capturing spread over heterogeneous fuel and terrain, tests conducted during the current implementation of CFFEPS with FireWork indicate that for smoke emissions estimation, fire growth is best captured by assuming daily persistence; that is, if a fire burns a certain area on a given day, it will burn an equal area the next day. Future attempts may be conducted to incorporate such fire-growth models in CFFEPS.
Over the course of a day, fire-growth rates vary as the temperature and wind speed typically decrease at night, while the relative humidity increases. The moisture content of the fuel in the litter layer on the forest floor varies correspondingly. Typically, the Fine Fuel Moisture Code (FFMC) reaches a peak with minimum fuel moisture at 17:00 LST (Van Wagner, 1987). This affects the rate of spread (ROS) of a fire, which, in turn, increases its intensity and area growth. The CFFEPS model provides two approaches to capture diurnal variations in fire growth. The first is a simple top-hat approach whereby the daily growth is spread evenly over a fixed period of time (09:00 to 21:00 LST). A second approach, which was applied in FireWork–CFFEPS, uses a weighting scheme following an average diurnal pattern of the rate of spread based on the FFMC, which is diurnally adjusted over time using the technique developed by Lawson et al. (1996).
CFFEPS calculates wildfire emissions following the bottom-up approach, whereby
a measure of activity, in this case effective biomass burned, is multiplied
by emission factors for different chemical species. The effective biomass
burned is calculated as total fuel consumption multiplied by the burn area.
In CFFEPS, total fuel consumption is calculated by the FBP system driven
with hourly forecast meteorology. This includes crown fuel consumption
(CFC), surface fuel consumption (SFC), and their sum, total fuel consumption
(TFC; units: kg of dry biomass m
FBP fuel types and bulk densities (g cm
The three combustion stages are considered to have three burn durations.
Flaming combustion is considered to occur within the first 15 min with
all emissions from flaming consumption immediately released into the
atmosphere. Smoldering and residual combustion last for several hours
depending on available fuel load. Assuming a constant forest-floor
smoldering rate of 1 cm h
Table 3 lists eight species-specific emission factors used in FireWork–CFFEPS. While FireWork-Ops uses average emission factors from FEPS, updated emission factors were chosen for CFFEPS based on recent literature (Urbanski, 2014). Depending on the three stages of combustion, time series of emissions released to the atmosphere are created for each pollutant in accordance with the emission factors and the duration of the combustion stages. These emission factors are applied for all input fuel types in the current application, although CFFEPS is now designed to allow for fuel-specific values as found in recent measurements (Liu et al., 2017).
Plume rise used in CFFEPS is based on the thermodynamic plume model developed by Anderson et al. (2011). This model predicts the penetration height of a plume based on the amount of energy injected by the fire into the atmosphere and an environmental lapse rate.
The energy released from a wildland fire can be determined using Byram's
equation:
During combustion, not all of the energy released by a forest fire enters
the plume; instead, the fire's energy is partitioned such that a portion of
the energy is projected ahead of the fire to heat fuels to combustion
temperatures or into the ground beneath the fire. Thus, to calculate
the energy injected into the plume, the following energy balance for a
wildland fire was devised for CFFEPS:
The plume energy is injected into the atmosphere above the fire, modifying
the plume's temperature profile to a dry adiabatic lapse rate. The energy
required to modify the atmospheric column above the fire can be calculated
as the integral
The CFFEPS model allows for the entrainment of environmental air into the fire's
plume, which is represented by an entrainment angle
As a plume rises, the density of smoke diminishes, as does the density of
air. In CFFEPS, it is assumed that the mixing ratio of smoke emissions to
clear air is constant in the plume due to convective mixing. Given the total
mass of smoke emissions (
The CFFEPS model is integrated into FireWork for NRT processing of biomass burning emissions. Methods and data sharing between FireWork and CWFIS have been enhanced to enable the new features contained in CFFEPS. The structure of the integration of CFFEPS with GEM-MACH (Fig. 2) illustrates the flow of information. One key difference with FireWork-Ops is the replacement of FEPS and SMOKE by CFFEPS. Hotspot and meteorological information is collected by CWFIS and associated with forest fuels. Hourly meteorological forecasts for the hotspot location are then collected and passed to CFFEPS. Hourly fire activity, emissions, and plume-rise parameters are calculated by CFFEPS and provided to GEM-MACH. By combining the fire emissions with anthropogenic and biogenic emissions, GEM-MACH then simulates the atmospheric dispersion and chemistry of pollutants from all sources. Details about a number of changes needed to complete the integration of FireWork and CFFEPS follow.
Structure and data flow for the integration of CFFEPS with GEM-MACH.
Operationally, CWFIS continues to provide NRT fire data during the Canadian fire season. The CFS Northern Forestry Centre in Edmonton, Alberta, collects hotspots detected nationally from MODIS, NOAA/AVHRR, and VIIRS satellite imagery. In FireWork–CFFEPS, the actively burning area at the time of detection is assigned based on historical area burned and hotspot statistics for each province and fuel type. In Canada, provincial and territorial agencies provide annual data on area burned. Given knowledge of the number and locations of hotspots, an average fire size per hotspot can be calculated for each fuel type provincially and territorially, with a recalibration performed every year: the 2017 values ranged from 7.52 ha per hotspot for O1 (grass) in BC to 43.88 ha per hotspot for coniferous fuels in Quebec.
For each hotspot, a fire-growth simulation environment is assembled. This
includes the forest fuel type and 12:00 LST weather and fire-weather
conditions. Daily noon weather observations from over 2500 stations in
Canada are collected and used to produce fire-weather and fire-behaviour
maps based on CFFDRS. Surface conditions are interpolated between stations
using an inverse-distance-weighted approach; surface air temperature is
cooled at a standard atmosphere rate of 6.5
All of the information described to this point is collected and processed by
CFS as part of CWFIS, and it is then provided to ECCC's Canadian Centre for
Meteorological and Environmental Prediction for further processing. Once
active fire information is received from CWFIS, 72 h point forecasts are
created for each hotspot using the 10 km regional version of the GEM weather
forecast model. Forecasted values include surface conditions (temperature,
humidity, and wind speed) along with upper-air conditions (temperature and
height) at specific pressure levels (850, 700, 500, 250 hPa). CFFEPS then
uses the GEM forecasted hourly weather to calculate fire behaviour. This
includes hourly values of FFMC, ROS (m min
The next step in CFFEPS is the synchronization of fire characteristics with the detected hotspot. A detection time (in LST) is determined for fire-behaviour prediction purposes. The detection time is synchronized to the appropriate hour of the forecast (in UTC) and the diurnal growth of the fire is then calculated from the detection time, fire size, and hourly fire behaviour.
Once the hourly growth rate is established, CFFEPS calculates fuel
consumption for each fuel type by depth of burn and flaming, smoldering, and
residual times for fire energy and emissions. The fraction of energy
released during the three combustion stages is based on the allocation
factors in Table 2. Fuel consumption from the current hour's fire growth is
thus spread out over time. The flaming stage is assumed to occur in the
first 15 min of combustion. Afterwards, a fire is assumed to burn into
the forest floor at a rate of 1 cm h
Combustion-stage allocation factors for each fuel type.
Energy values are then calculated over time using the hourly fire growth (i.e. the change in area burned from one hour to the next) and TFC. While growth is dictated by the persistence scheme used, hourly and daily changes in FWI values provide diurnal and daily changes to the TFC and thus to the energy released. Hourly energy values injected into the fire plume (see Eq. 3) are next used to estimate hourly plume rise. Plume rise is calculated in one of two ways. The traditional approach, as described in Anderson et al. (2011), heats the air above a fire, adjusting the environmental lapse rate above the fire to a dry adiabat. The environmental lapse rate used for the column above the fire is a single average value, though the choice of lapse rate will vary depending on the predicted plume height. For example, if the lapse rate from the surface to 850 hPa predicts a plume height of over 2000 m, then the lapse rate from the surface to 700 hPa will be used, but if that predicted plume is above 4000 m, then the lapse rate from the surface to 500 hPa will be used. A new alternative method that calculates plume rise using all measurements from the detailed upper-air profile and integrating the energy piecewise through the atmosphere is now included in CFFEPS, although it was not used in this study.
Given the fire area growth and the fuel consumption, total emissions over
time are calculated. The estimated hourly plume injection height at each
fire location is used directly in GEM-MACH to distribute fire emissions
below the representative model layers. Fire emissions from all three stages
of combustion are distributed below the injection height, through the model
grid column based on the calculated smoke mixing ratio. A smoke mixing ratio
(
Similar to the method for fire energy, CFFEPS manages emissions per species
by accounting for hourly TFC at each stage of combustion. Once the hourly
TFC (kg m
Emission factors (g kg
Prior to input in GEM-MACH, hourly lumped emissions of non-methane
hydrocarbons (NMHCs), PM
The current operational version of FireWork (FireWork-Ops) uses FEPS and
SMOKE modules for fire emissions input into the GEM-MACH forecasts. The new
setup, FireWork–CFFEPS, presented here replaces those modules with the
CFFEPS module for fire dynamics and emissions. The principal differences
between FireWork-Ops and FireWork–CFFEPS are the following.
FireWork-Ops uses static hotspot sizes of 38.5 ha for all hotspots and
fuel types; FireWork–CFFEPS uses yearly updated hotspot sizes categorized by
fuel type and by province and territory. FireWork-Ops uses the hotspot size (38.5 ha) as the area burned on the first
day; FireWork–CFFEPS uses reverse growth from the detection time and fire
size to create fire sizes for the initial hours of the forecast. FireWork-Ops uses TFC as the flaming consumption and the difference between
forest-floor fuel consumption (de
Groot et al., 2009) and TFC as the smoldering consumption; FireWork–CFFEPS
uses TFC, breaking it down into flaming, smoldering, and residual combustion
stages by fuel type and depth of burn (Table 2). FireWork-Ops applies a fixed diurnal profile for hourly allocation of the
combined flaming and smoldering emissions from daily total fire emissions;
FireWork–CFFEPS allots 15 min for flaming and establishes the remaining
period of burn using the depth of burn and an assumed burn rate of 1 cm h FireWork-Ops does not consider fire energy; FireWork–CFFEPS calculates fire
energy over time and uses that value to calculate plume injection height. FireWork-Ops uses the Briggs plume-rise parametrization with fixed plume
temperature and plume velocity; FireWork–CFFEPS uses the fire-energy
thermodynamics approach to estimate hourly plume injection height and to
distribute smoke based on smoke mixing ratio and air density. FireWork-Ops uses fixed emissions factors predefined by FEPS for seven
species (PM FireWork-Ops allocates lumped NMHC fire emissions to GEM-MACH model VOC
species following a default profile; FireWork–CFFEPS allocates lumped NMHC
fire emissions using separate flaming and smoldering speciation profiles.
To assess the forecast performance of FireWork–CFFEPS, the system was run in
hindcast mode for 2017, a recent year with high fire activity, and results
were compared against the forecast performance of the operational
FireWork-Ops system. Model forecast performance was assessed by comparing
simulation results with available hourly, continuous surface measurements
from the Canadian National Air Pollution Surveillance (NAPS
Locations of Canadian NAPS and US AQS stations used for 2017 model evaluation with 75 % measurement completeness criteria. Some stations measure more than one species.
Categorical score definitions for a binary event.
In both Canada and the US, 2017 was a significant fire year, with record fire starts and burned areas, mostly in western states and provinces. In Canada, the total burned area for the 2017 season is shown in Fig. 4 against a 10-year average. Although the fire season started slowly in May and June, with numbers below the 10-year average, fire activity then picked up very rapidly in July with several large fires in BC. Fire starts continued in August with fires in the Northwest Territories (NT), northern Alberta (AB), northern Saskatchewan (SK), central Manitoba (MB), and western Ontario (ON). Wildfires in western Canada were active until early September, with most fires occurring in south-central BC during July and August. Across Canada, BC had the highest number of fire hotspots, accounting for more than 50 % of the Canadian total. Due to the severity of the wildfires in BC, the province declared a state of emergency from 7 July until 15 September. More than 1.2 million ha was burned and more than 65 000 people were evacuated during this period. The Plateau Complex fire in south-central BC was the single largest fire in the province on record, with a combined total fire area of 545 151 ha (Abbott and Chapman, 2018).
National fire burn area in Canada by week starting for the 2017 fire season (blue vertical bars) and previous 10-year average (red line).
In the US, 2017 was the one of the most expensive years on record with respect to total firefighting costs, with total federal spending close to USD 3 billion (National Interagency Fire Center, 2018). Total burned area nationally was reported to be more than 4 million ha from 71 000 fires, significantly higher than the 10-year average of about 2.7 million ha. For states near Canada, fire activity was significant from August through mid-September for Washington (WA), northern Idaho (ID), western Oregon (OR), and western Montana (MT). Most notable was the Lodgepole Complex fire in MT that burned 110 000 ha. It was the largest fire in MT history and also the largest in the US for the 2017 season. The Chetco Bar fire in OR started in mid-July and burned 77 000 ha, while the Diamond Creek fire in WA burned 52 000 ha. As a result of smoke plumes from these local fires and smoke plumes from BC wildfires, several cities in WA, OR, and ID issued AQ advisories, with the air quality index reaching the highest, “hazardous”, level.
FireWork–CFFEPS was run with the same model setup as the operational
FireWork-Ops for the July–September 2017 period. Figure 5 shows the monthly
total effective biomass burned from FireWork–CFFEPS and FireWork-Ops for
hotspots greater than 5000 t burned per month. The spatial
distributions of fire locations as clusters of hotspots for the two systems
were generally similar, which was expected given that the same fire
information was provided by CWFIS in both cases. However, the
effective biomass-burned total and the number of fires above the 5000 t threshold were different. The largest driver of the difference is the
estimated burn area, which changed from a constant 38.5 ha per hotspot in
FireWork-Ops to varying burn areas by province and fuel type in
FireWork–CFFEPS. In BC, an overall reduction in burn area ranging from 7.5 ha per hotspot for grass fuels (O1) to 14.5 ha per hotspot for boreal
mixed wood (M1) greatly reduced the effective biomass burned across the
province for all three months. Similarly, for SK, the increases in effective
biomass burned can be attributed, in part, to increases in estimated burn
area to approximately 40 ha per hotspot for boreal spruce and pine fuels
(
Monthly total effective biomass burned by hotspot for
FireWork–CFFEPS
In addition, the number of hotspots produced by fires above the 5000 t threshold is different, especially in August with more hotspots in FireWork–CFFEPS than FireWork-Ops for areas of northern AB, SK, MB, and western ON. The changes are due to the combination of changes in estimated burn area and changes in fuel consumption driven by hourly forecast meteorology in CFFEPS. Variations in hourly meteorology can change the diurnal variation in biomass burned in CFFEPS, whereas in FireWork-Ops, the total effective biomass burned is calculated from daily totals based on local noontime meteorology at each hotspot location.
Emissions totals were also quite different between the two systems as a
result of the combined changes in effective biomass burned and in the
process-dependent species emission factors. Table 5 summarizes the emission
totals for the same three months from FireWork-Ops and the percentage
difference for FireWork–CFFEPS by species and by country, as well as for
individual provinces in Canada and US states near Canada that were
selected for their high fire activities. At the continental scale,
FireWork–CFFEPS has consistently lower emissions than FireWork-Ops for VOC,
Total fire emissions (kilotonnes) in Canada, the US, and selected provinces and states with high fire activity from FireWork-Ops for the July–September 2017 period and percentage differences (in italics) for FireWork–CFFEPS.
Model performance statistics for daily maximum surface
Fire-plume injection heights are calculated hourly at each hotspot location in both FireWork-Ops and FireWork–CFFEPS but by different methods, and fire emissions are distributed vertically within the model grid column below the modelled plume injection heights. FireWork-Ops parameterizes the plume injection height based on the Briggs parametrization, similar to those used in anthropogenic point sources specific to facility stacks. FireWork–CFFEPS applies the new fire-energy thermodynamic balance approach with forecasted hourly environment lapse rate at hotspot locations (see Sect. 2.2.3).
Figure 6 shows the injection height frequency distribution by 200 m altitude bin for all BC fire hotspots in August 2017 grouped by forecast hour as predicted by FireWork–CFFEPS and FireWork-Ops 48 h 00:00 UTC forecasts. The frequency distributions for both FireWork-Ops and FireWork–CFFEPS display clear diurnal variability in modelled injection height throughout the forecast period with generally higher injection heights during local daytime (f00, f03, f18, f21). There are also large differences, however, in the distribution of injection heights between the two systems for the same hour, with FireWork–CFFEPS typically showing wider distributions and higher modelled injection heights than FireWork-Ops. During local daytime, the FireWork–CFFEPS injection height frequency distribution ranged mostly from 2 to 6 km with its mode at around 4 km, whereas FireWork-Ops has a narrower distribution ranging from 1 to 3 km with its mode at around 1.5 km. The highest plume injection heights for FireWork–CFFEPS reach as high as 6 km, whereas FireWork-Ops modelled injection heights are always below 4 km under the same conditions. During local night-time (f06, f09, f12, f15), the injection height distribution for FireWork–CFFEPS ranges from 1 to 4 km with its mode at either 1.8 km or 3.2 km, whereas the injection height distribution for FireWork-Ops is consistently below 2 km, with most hours having 50 % or more of the injection heights below 200 m.
Modelled plume injection height frequency distribution by 200 m altitude intervals for all fires in British Columbia, Canada, in August 2017 by forecast hour for 00:00 UTC forecasts by FireWork–CFFEPS (left) and FireWork-Ops (right).
The large differences in modelled plume injection heights between FireWork-Ops and FireWork–CFFEPS result from the CFFEPS parameterization considering fire growth, fire energy, and the forecast environmental lapse rate. FireWork-Ops parameterizes plume injection height based on a prescribed constant fire emission temperature, initial height, and modelled hourly PBL following the Briggs parameterization. In a recent study on model plume-rise parameterization, Akingunola et al. (2018) demonstrated that the current implementation of Briggs in GEM-MACH under-predicts measurements from facility stacks and can be further improved with a layered lapse-rate approach that is not currently used in the RAQDPS. A separate analysis also showed that FireWork-Ops injection height is limited to hourly PBL height with maximum injection height always equal to or less than the PBL height. This confines the vertical distribution of fire emissions to near the Earth's surface and limits the amount of emissions penetrating into the free troposphere, where stronger winds enhance long-range transport. A detailed verification comparing CFFEPS-derived fire-plume injection heights with surface observations and satellite-based estimates is beyond the scope of this paper. Nevertheless, the altitude range of FireWork–CFFEPS is in general agreement with a recent global fire-plume injection height analysis from satellite remote sensing for the region (Val Martin et al., 2018), and the injection heights calculated by CFFEPS are not restricted to below PBL height as in FireWork-Ops.
The contributions of biomass burning emissions to modelled PM
Mean monthly surface fire PM
The overall spatial extent of fire PM
Model forecast performance for PM
For western Canada (WCAN) and the western US (WUSA), Table 6 shows much
larger differences between the three model versions due to the influence of
fire activities in the area. Since western wildfires were a large
contributor to PM
The categorical score comparisons for the three modelling systems for
PM
Categorical scores for July–September 2017 by geographic region for
hourly
Wildfire activity was most severe in August and early September 2017,
especially between 6 and 19 August (weeks 15–16 in Fig. 4), with a record
burned area of more than 1.1 million ha across Canada. Although most
of the fires occurred in central BC, there was also significant fire
activity in WA, ID, OR, and MT (Fig. 7), which caused widespread
PM
Figure 8 shows mean daily maximum PM
Time series of mean daily maximum PM
The model performance statistics for both western regions (Table 8) show
systematic improvement of FireWork–CFFEPS over FireWork-Ops, with higher
Model performance statistics for daily maximum surface PM
PM
For the northern Canada region, due to the sparsity of measurement stations
over this large area and to stations being situated further away from fire
hotspots and from sources of anthropogenic emissions, mean daily maximum
surface PM
Model performance by forecast hour was also analysed for these regions to
compare the diurnal variability predicted by the three AQ forecast systems.
Surface PM
Mean PM
It is evident from Fig. 9 that for the AB
Similar analyses were carried out for predicted daily maximum surface
Comparing Fig. 9 with Figs. S3 and S4 it is evident that there is
stronger diurnal variability in the hourly concentrations of surface
One additional evaluation was conducted for the 1 August to 18 September 2017
period to examine model performance for just those stations and days
observed to be affected by wildfire plumes. Table S10 presents performance
statistics for model predictions of daily maximum PM
PM
Fire PM
The satellite image shows a large cluster of fires in NT, just north of the
border with AB and SK, as well as other fires burning across northern AB,
SK, and MB and in central BC. The dense smoke plume, influenced by the
upper-level jet stream, blankets the entire NT region with tendrils
extending southward along the eastern borders of AB and SK. Comparison of
the next-day satellite image with the 1 d forecasts of fire PM
Time series of hourly surface PM
The model performance evaluation for the 2017 fire season presented in Sect. 3 gives us confidence in the improvements in model forecast skills that the new FireWork–CFFEPS system has over FireWork-Ops. Additional analysis for the most recent 2018 fire season, summarized in Sect. S3 of the Supplement, showed similar and consistent changes when benchmarked against the operational FireWork-Ops system.
Despite the overall forecast improvements with FireWork–CFFEPS shown in
Sect. 3 and the Supplement, there are important science
questions that remain to be investigated. Although we have quantified the
changes in estimated fire emissions and in modelled plume injection heights,
we have not independently verified these values. Verification of fire
emission values is challenging as there are no direct measurements;
nevertheless, it is possible to compare daily emissions predicted by
FireWork–CFFEPS with many global fire emission inventories that implement
the top-down, satellite-derived FRP approach to estimate emission totals.
Similarly, new techniques that calculate hotspot-specific fire
Although FireWork–CFFEPS represents an important step forward in NRT
modelling of fire emissions for regional air quality forecast systems, it
still has some known limitations.
Fire detection is from the operational CWFIS, which is based on sensors on
polar orbiting satellites. Although multiple sensors on multiple satellites
are used, they still have limited temporal coverage of about six times a
day. Fire starts after satellite overpasses at nadir (typically 13:00 LST)
will not be considered until the next forecast simulation, and detected fires
are assumed to continue burning for the next forecast simulation day.
Current hotspot retrievals are also limited by the presence of thick cloud
or smoke, which can result in missing hotspot detection and hence missing
fire emissions. Similarly, small fires with low heat signatures, including
prescribed burns or agricultural burning, may be undetected due to low
sensor resolution. Prescribed burns make up a significant fraction of the
US PM Most fire-behaviour models, including the FBP system used in CFFEPS, assume
that fires grow freely without suppression. The same emission factors are now applied for all input fuel types in
CFFEPS, but emission factors can vary by fuel type as found in recent
measurements (Liu et al., 2017), and
fuel-type-specific emission factors can be considered in future. Although fire growth is now closely tied to forecast hourly meteorology in
CFFEPS, the key input, fire size or burn area per day, is still a
predetermined parameter that is based on an annual climatology of recorded
fire area by province and the total number of hotspot retrievals. The daily
fire size is also assumed to be persistent for the second-day forecast and
is not based on estimates from a fire-growth model driven by meteorology. Fire emissions are still treated as point sources and their location data
are still assigned to model grid cells by geographic coordinates. This is
necessary as fire injection height is specific to each hotspot. However, as
fire area gets larger and model grid resolution becomes finer, grouping
fire hotspots as area aggregates may be a more favourable approach. This
would allow for the spatial tracking of fire front by areas of flaming and
smoldering combustion processes and the quantification of three-dimensional
fire growth over area and depth of burn.
One important limitation of the current model setup is the neglect of the
interactions of fire behaviour with microphysics. Large, intense fires can
affect local weather through the release of surface heat flux and latent heat
from water vapour. This energy can further increase the buoyancy of fire
plumes, generating strong updrafts and accelerating the vertical transport
of smoke. In the case of large fires, the increased buoyancy can even cause
the formation of pyrocumulus or pyrocumulonimbus that further transport
smoke aloft, sometimes into the stratosphere. The differences in vertical
wind shear resulting from higher injection heights can also alter the
horizontal development of smoke plumes and impact the long-range transport
of smoke.
Additionally, increases in primary PM emissions and in secondary aerosol
formation in smoke plumes can increase atmosphere opacity, or AOD, along a
plume trajectory. This can suppress turbulent mixing near the surface,
causing stagnation and promoting the accumulation of surface pollutants. AOD
increases can also attenuate overall photolysis rates, which may reduce the
chemical formation of surface
Active research is currently underway towards integrating CFFEPS directly into the research version of the GEM-MACH model with direct and indirect two-way meteorology–chemistry feedbacks (Gong et al., 2015). Through close coupling of meteorology and chemistry, and now with inputs of fire energy and emissions from CFFEPS enhancing vertical transport and impacting model microphysics, research using such an integrated system may provide the means to further examine the complex systems of direct and indirect feedbacks that fire activities have on regional meteorology and chemistry.
FireWork is one of the first operational high-resolution regional air quality forecast systems with NRT wildfire emissions over a large North American domain. Since it became operational in 2016, the system has become an important guidance tool for air quality meteorologists in assessing potential air pollution episodes from the impact of forest fire smoke and issuing AQHI advisories for communities across Canada. In the initial development of the FireWork v1.0 system a number of compromises and assumptions were made to simplify the NRT wildfire emissions processing. In this work, we introduce a new process-based fire emission prediction system – CFFEPS – that has been integrated into FireWork to improve the representation of the dynamics of fire behaviour and smoke emissions while still ensuring the timely delivery of forecast products.
The new FireWork–CFFEPS (FireWork v2.0) system represents a significant step forward in the simulation of wildland fire smoke behaviour and fire emissions for regional CTMs. The changes listed in Sect. 2.4 have improved several aspects of fire emissions modelling and have resulted in better emission quantification. These changes include the introduction of location- and fuel-type-specific fire size, a revised North American fuel map, and updated emission factors. Fire emissions estimates are now process-based such that emission duration and temporal variation are driven by hourly meteorology and fuel depth of burn, and their influence on combustion processes is considered. This approach allows for an improved application of combustion-phase-specific emission factors and more detailed chemical speciation that can be further extended by fuel-type dependence in the future. Also, fire-energy thermodynamics are now parameterized to calculate an hourly fire-plume injection height that varies by fire size and fire intensity and that equilibrates with the hourly forecast environmental lapse rate at fire locations. The height of the modelled fire plume directly influences surface pollutant concentrations as well as long-range transport downwind of fire locations. The availability of hourly fire-energy estimates also paves the way for ongoing research on large wildfires as sources of heat energy for input to the microphysics scheme of the GEM-MACH coupled meteorology–chemistry model.
It is clear from the performance evaluation of the three AQ forecast systems
reported here for three summer months in both 2017 and 2018 that the
combined changes introduced in FireWork–CFFEPS have resulted in significant
and consistent forecast improvements over FireWork-Ops for surface
PM
CFFEPS represents a new process-oriented approach to model fire emissions suitable for operational air quality forecasting as demonstrated with FireWork–CFFEPS. The process-based approach with bottom-up fire emissions estimates allows for flexibility in updating fuel-dependent emission factors and provides a more realistic yet computationally efficient plume-rise parameterization. Logistically, CFFEPS is also a bridge that brings together the science developed by two Canadian federal departments such that ECCC is able to access and utilize state-of-science fire-behaviour research from the CFS and to couple the CFFEPS system with the latest understanding in meteorology and atmospheric chemistry embodied in ECCC's two operational air quality forecast systems, the RAQDPS and FireWork.
The air quality monitoring data used for model evaluation are available for
download from the Canadian National Air Pollution Surveillance (NAPS)
Network and the US Air Quality System (AQS) data repositories through the
Internet URLs provided in Sect. 3. The code for CFFEPS v2.03 and the
accompanying user manual are available from the Zenodo website:
The supplement related to this article is available online at:
JC led the development of the ECCC FireWork system. KA is the principle developer of CFFEPS. JC, KA, and RP designed the FireWork–CFFEPS model framework. MDM is responsible for the development of the RAQDPS operational system and for reviewing this study. RMA contributed the emission factors used in the current FireWork–CFFEPS study. PE provided CWFIS integrations for CFFEPS, the calculation of hotspot areas, and all hotspot data files used in hindcast simulations. DT provided the CFFEPS fuel map integration for the North America model domain. RMA and HL were responsible for code integration and model simulations. JC conducted the majority of the analyses and model evaluation for this study. JC and KA prepared the paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
The authors gratefully acknowledge the near-real-time fire information
retrievals from the NOAA National Environmental Satellite, Data and
Information Service (NESDIS), the NASA Fire Information for Resource
Management System (FIRMS), the USFS GTAC, and the University of Maryland VIIRS
active fire group. We also thank Samuel Gilbert (ECCC) for his assistance in
using the Verification of Air Quality Models (VAQUM) database system for
data analysis, Sétigui Keita (ECCC) for generating the monthly mean
fire PM
This paper was edited by Samuel Remy and reviewed by two anonymous referees.