Multiple observation data sets – Interagency Monitoring of Protected Visual
Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking
Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and
aircraft acetonitrile (
Wildfires and agricultural/prescribed burns are common in North America all
year round but predominantly occur during the spring and summer months (Wiedinmyer et al., 2006). These fires pose a significant
risk to air quality and human health (Delfino et al., 2009; Rappold et
al., 2011; Dreessen et al., 2016; Wotawa and Trainer, 2000; Sapkota et al.,
2005; Jaffe et al., 2013; Johnston et al., 2012). Since January 2015, smoke
emissions from fires have been included in the National Air Quality
Forecasting Capability (NAQFC) daily PM
Southeast Nexus (SENEX) was a NOAA field study conducted in the southeastern USA in June and July 2013 (Warneke et al., 2016). This field experiment investigated the interactions between natural and anthropogenic emissions and their impact on air quality and climate change (Xu et al., 2016; Neuman et al., 2016). In this work, the SENEX data set was used to evaluate the HMS–BlueSky–SMOKE–CMAQ fire simulations during the campaign period.
Two simulations were performed: one with and one without smoke emissions from fires during the SENEX field campaign. Due to the large uncertainties in the estimates of fire emissions and smoke simulations (Baker et al., 2016; Davis et al., 2015; Drury et al., 2014), the first step of the evaluation focused on the fire signal capturing capability of the system. Differences between the two simulations represented the impact of the smoke emissions from fires on the CMAQ model results. Observations from various sources were utilized in this analysis: (i) ground observations (Interagency Monitoring of Protected Visual Environments (IMPROVE)), (ii) satellite retrievals (Automated Smoke Detection and Tracking Algorithm (ASDTA) and HMS smoke plume shape) and (iii) aircraft measurements (SENEX campaign). Fire signals predicted by the modeling system were directly compared to these observations. Several criteria have been used to rank efficacy of the observation systems for fire-induced pollution plumes.
In this section the NAQFC fire modeling system used in the study was introduced. Uncertainties and limitations in the various modeling components of the system are discussed. Figure 1 illustrates the schematics of the system. There are four processing steps.
Schematics of fire emission and smoke plume simulation system used: data feed and/or modeling of physical and chemical processes were handled largely sequentially from top to bottom and from left to right. The right-hand four vertical boxes depict the submodel names: NESDIS Hazard Mapping System (HMS) for wildfire hotspot detection; the US Forest Service's BlueSky for fuel type and loading parameterization; the US EPA's Sparse Matrix Operator Kernel (SMOKE) for handling emission characterization; and lastly the Community Multiple-scale Air Quality model (CMAQ) for simulating the transformation, transport and depositions of the atmospheric constituents. The “SENEX” inset framed by bold red lines was the domain for this study.
The NOAA NESDIS HMS is a fire smoke detection system based on satellite retrievals. At the time of this study, the satellite constellation used consists of two versions of the Geostationary Operational Environmental Satellite (GOES-10 and GOES-12) and five polar-orbiting satellites: MODIS (Moderate-resolution Imaging Spectroradiometer) instruments on NASA Earth Orbiting Sysmte (EOS) Terra and Aqua satellites, and AVHRR (Advanced Very High Resolution Radiometer) instruments on NOAA 15, 17 and 18 satellites. HMS detects wildland fire locations and analyzes their sizes, starting times and durations (Ruminski et al., 2008; Schroeder et al., 2008; Ruminski and Kondragunta, 2006).
HMS first processes satellite data by using automated algorithms for each of the satellite platforms to detect fire locations (Justice et al., 2002; Giglio et al., 2003; Prins and Menzel, 1992; Li et al., 2000), which is then manually analyzed by analysts to eliminate false detections and/or add missed fire hotspots. The size of the fire is represented by the number of detecting pixels corresponding to the nominal resolution of MODIS or AVHRR data. Fire starting times and durations are estimated from close inspection of the visible-band satellite imagery. A bookkeeping file is generated at the end of this detection step, named “hms.txt” (Fig. 1). It includes all the thermal signal hotspots detected by the aforementioned seven satellites. During the analyst quality control step, detected potential fire hotspots lacking visible smoke in the retrieval's HMS (RGB real-color) imagery are removed, resulting in a reduced fire hotspot file called either “hmshysplit.prelim.txt” or “hmshysplit.txt” to be input into the BlueSky processing step.
In general, hmshysplit.prelim.txt and hmshysplit.txt are very similar, and hmshysplit.txt is created later than hmshysplit.prelim.txt (Fig. 1). But the differences between hmx.txt and hmshysplit.txt (hmshysplit.prelim.txt) can be rather substantial. The reasons for the differences are that (1) many detected fires do not produce detectable smoke; (2) some fires/hotspots are detected only at night, when smoke detection is not possible; and (3) smoke emission HMS imagery is obscured by clouds and thus not detected by the analyst. Therefore, smoke emission occurrence provided by the HMS is a conservative estimate of fire emissions.
Through use of multiple satellites, the likelihood of detecting fires in HMS is robust. However, when the fire's geographical size is small, the HMS detection accuracy dramatically decreases (Zhang et al., 2011; Hu et al., 2016). Other limitations of the HMS fire detections include ineffective retrievals at nighttime and under cloud cover.
BlueSky, developed by the USFS, is a modeling framework for simulating smoke impacts on regional air quality (Larkin et al., 2009; Strand et al., 2012). In this study, BlueSky acted as a fire emission model to provide input for SMOKE (Herron-Thorpe et al., 2014; Baker et al., 2016). BlueSky calculates fire emission based on HMS-derived locations (Fig. 1).
Fire's geographical extent is reflected by the number of nearby fire pixels
detected by satellites in a 12 km CMAQ model grid. Fire pixels are converted
to fire burning areas in BlueSky based on the assumption that each fire
pixel has a size of 1 km
BlueSky does not iteratively recalculate fire duration according to the
modeled diminishing fuel loading or the modeled fire behavior. In the
aggregation process, when there is more than one HMS point in a grid cell
which have different durations, all points in that grid cell are
assigned the largest duration in all points. For example, if there were three
HMS points that had durations of 10, 10 and 24 h, the aggregation would
include three points (representing 3 km
HMS has no information about fuel loading. BlueSky uses a default fuel loading climatology over the eastern USA. BlueSky uses an idealized diurnal profile for fire emissions. Uncertainties in fire sizes, fuel loading and fire emission rates lead to large uncertainties in wildland smoke emissions (Knorr et al., 2012; Drury et al., 2014; Davis et al., 2015).
In SMOKE (Sparse Matrix Operator Kernel Emission), the BlueSky fire emissions data in a longitude–latitude map projection are converted to CMAQ-ready gridded emission files (Fig. 1). Fire smoke plume rise is calculated using formulas by Briggs (1975). The heat flux from BlueSky and NAM meteorological state variables are used as input (Erbrink, 1994). The Briggs algorithm calculates plume top and plume bottom; between plume top and bottom the emission fraction is calculated layer by layer assuming a linear distribution of flux strength in atmospheric pressure. For model layers below the plume bottom the emission fraction is assumed to be entirely in the smoldering condition as a function of the fire burning area.
A speciation cross-reference map was adopted to match BlueSky chemical
species to those in CMAQ using the US EPA Source Classification Codes
(SCCs) for forest wildfires (
The CMAQ version 4.7.1 was used. The CB05 gas phase chemical mechanism (Yarwood et al., 2005) and the AERO5 aerosol module (Carlton et al., 2010) were chosen. Anthropogenic emissions were based on the US EPA 2005 National Emission Inventory (NEI) projected to 2013 (Pan et al., 2014); biogenic emissions (BEIS 3.14) were calculated in-line inside CMAQ.
The NAM provided meteorology fields to drive CMAQ (Chai et
al., 2013). NAM meteorology is evaluated daily and results (bias, root mean square error
etc.) are posted at
There were several differences in system configuration between the NAQFC fire smoke forecasting and the “with-fire” simulation in this study. For models, the BlueSky versions used in NAQFC and in this study are v3.5.1 and v2.5, respectively; CMAQ versions used in NAQFC and in this study are v5.0.2 and v4.7.1, respectively. For simulations, current fire smoke forecasting in the NAQFC includes two runs: the analysis and the forecast (H. C. Huang et al., 2017). The analytical run is a 24 h retrospective simulation using yesterday's meteorology and fire emissions to provide initial conditions for today's forecast. The forecasting run is a 48 h predictive simulation using yesterday's fire emissions, assuming fires with duration of more than 24 h are projected as continued fires. The with-fire simulation in this study is exactly identical to the analysis run in NAQFC.
Carbon monoxide (CO) has a relatively long lifetime in the air and is
emitted by biomass burning. CO was used as a fire tracer in the prediction.
The CO difference (
It is almost impossible to assess the uncertainty of each specific physical
process of smoke. In each modeling step in HMS, BlueSky, SMOKE and CMAQ, the
modeling system accrues uncertainties. Such uncertainties were likely
cumulative and might lead to larger error in succeeding components
(Wiedinmyer et al., 2011). For example, heat flux
from BlueSky influenced plume rise height in SMOKE and consequently
influenced plume transport in CMAQ. It is also noteworthy that when modeled
The SENEX campaign occurred in June and July, and our model simulations were from 10 June to 20 July 2013. Throughout the campaign all available observation data sets were used, including ground-, air- and satellite-based acquired data. Each data set had its unique characteristics, and linking them together gave an overall evaluation. At the same time, in each data set our evaluations included as many observed fire cases as possible. Both well-predicted and poorly predicted cases are presented to illustrate potential reasons for the modeling system's behavior.
In the 4 km SENEX domain,
Table 1 lists observed and modeled CO vertical profiles for the with-fire and without-fire cases during the SENEX campaign. Observed CO concentrations between the surface and 7 km a.g.l. (above ground level) in the SENEX domain area remained greater than 100 ppb during all 40 d of the campaign. The highest CO concentrations were measured closer to the surface. The maximum measured CO concentration of 1277 ppb was observed during a flight on 3 July at 974 m a.s.l. (above sea level). During this flight strong fire signals were observed, but the fire simulation system missed those signals as discussed below.
Observed and simulated CO (ppb) during NOAA SENEX.
Identified fire signals from IMPROVE measurements during SENEX.
Notes: (ratios for EC, OC and
CO concentrations were underestimated by the model in almost all cases even
when the model captured CO contribution from fire emissions
spatiotemporally. Mean
During the field experiment the general lack of large fires made evaluation
of modeled fire signature difficult since it was easier to capture large
fire signals than the smaller fires. We postulated that a clear fire signal
simulated in the HMS–BlueSky–SMOKE–CMAQ system could be indicated by
Figure 3 displays the simulated
CMAQ-simulated
The Interagency Monitoring of Protected Visual Environments (IMPROVE) is a
long-term air visibility monitoring program initiated in 1985
(
There were 14 IMPROVE sites in the SENEX domain (Fig. 4). Potential fire
signals were identified using CMAQ-modeled
Five fire events were observed at four IMPROVE sites. Table 2 lists measured
EC, OC,
For the four identified fire cases,
Another IMPROVE site located upwind of MACA, CADI, was also potentially under the influence of that fire event; however, data from CADI on 24 June did not indicate a fire influence, possibly due to the frequency of IMPROVE sampling that eluded measurement or because the smoke plume was transported above the surface in disagreement with what was modeled. Within the four fire cases identified by the IMPROVE data during SENEX (Table 2), the model successfully captured three out of four events. The model missed the fire signal on 3 July at MACA. The following section is dedicated to the 3 July SENEX flight.
Simulated
HMS determines fire hotspot locations associated with smoke and upon
incorporating the smoke plume shape information from visible satellite
images. HMS provides smoke plume shapefiles over much of North America,
which is a two-dimensional smoke plume spatial depiction collapsing all
plume stratifications to a satellite's view seen from high above. For modeled plumes, we
integrated modeled
Figure of merits in space (FMS) (Rolph et al., 2009)
is a statistic for spatial analysis and was calculated as follows:
Figure 5 summarizes FMS during the SENEX campaign. Average FMS was 22 % with its maximum at 56 % on 6 July and minimum at 1.2 % on 17 June 2013. Figure 6a exhibits the HMS-detected smoke plume and CMAQ-calculated smoke plume over CONUS on 6 July. The FMS score was 56 %, meaning that the modeled plume shape was consistent with that of HMS. However, HMS–BlueSky–SMOKE–CMAQ emissions system might have underestimated the intensive fire influence areas along the border of California and Nevada. Subsequently, the model also underpredicted its associated influence in North Dakota, South Dakota, Minnesota, Iowa and Wisconsin.
FMS (figure of merits in space) (%) from 11 June to 19 July in 2013 during the SENEX campaign.
Figure 6b exhibits the worst case on 17 June 2013 with a FMS score of 1.2 %. There are two reasons for this: (i) CMAQ missed the fire emissions from Canada. Those fire sources were located outside the CONUS modeling domain, and our simulation system used a climatologically based static LBC. Secondly on 17 June, there were a lot of fire hotspots in the southeastern USA, i.e., in Louisiana, Arkansas and Mississippi along the Mississippi River. Hotspots were detected, but they lacked associated smoke in the corresponding HMS imagery (Fig. 6c). This could be due to cloud blockage or to small agricultural debris clearing, burns in underbrush or prescribed burns. These conditions prevented the HMS from identifying fires, and hence emissions were not modeled for those sources.
Daily HMS-observed plume shape versus CMAQ-predicted daily
averaged plume shape on
It is noteworthy that the FMS evaluation contained uncertainties contributed from both modeled and observed values. The calculated campaign duration and SENEX-wide average FMS was 22 %. It is significantly higher than that achieved by similar analyses done by HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) smoke forecasting for the fire season of 2007 (6.1 to 11.6 %) (Rolph et al., 2009). The primary reason is that due to retrieval latency and cycle-queuing problems in HMS, HMS fire information is delayed by 1 d, which means that today's HMS list can only reflect yesterday's fire information, so HYSPLIT smoke forecasting can only use yesterday's fire information. However, our model simulation in this study was from a retrospective module using current-day fire information. Such discrepancies have been discussed by Huang et al. (2020). The secondary reason is plume rise: although the HYSPLIT and CMAQ fire plume rise were both estimated by the Briggs equation, the HYSPLIT plume rise was limited to 75 % of the mixed layer height (MLH) during daytime and 2 times MLH at nighttime, whereas the CMAQ fire plume rise did not have these limitations.
GOES-detected AOD influenced by fires using the ASDTA diagnostic method
(summed over 10:00 to 14:00 local time). Color-shaded region
represents the fire-smoke-influenced areas, and the color denotes the
magnitude of the retrieved AOD on
The Automated Smoke Detection and Tracking Algorithm (ASDTA) is a combination of two data sets: (1) the NOAA geostationary satellite (GOES-13), which retrieves thermally enhanced aerosol optical depth due to fires using visible channels and produces a product called GOES Aerosol/Smoke Product (GASP) (Prados et al., 2007), and (2) NOAA NESDIS HMS fire smoke detection. First, the observation of the increase in AOD near the fire is attributed to the specific HMS fire; AOD values not associated with fires are dropped. Second, a pattern recognition scheme uses 30 min geostationary satellite AOD images to track the transport of this smoke plume away from the source. ASDTA provides the capability to determine whether the GASP is influenced by one or multiple smoke plumes over a location at a certain time.
Vertical distributions of CMAQ-simulated
ASDTA, originally generated to provide operational support for verification
of the NOAA HYSPLIT dispersion model, predicts smoke plume direction and
extension (Draxler and Hess, 1998). These data are also suitable for
model performance evaluation in this study. For each simulation, modeled AOD
was calculated for each sensitivity test (with fire or without fire),
and
Figure 7a illustrates a GOES-retrieved AOD (summed over 10:00 to
14:00 local time) contour plot that reflects influences by smoke plumes
over the CONUS domain on 14 June 2013. Figure 7b presents similar results
but for simulated
Plots for the 3 July 2013 case:
Similar plots for 25 June are shown in Fig. 7d, e and f for ASDTA, CMAQ and HMS, respectively. The ASDTA (Fig. 7d) diagnosed an overestimation in fire influences in the South, including Texas and the Gulf of Mexico, and an underestimation in the northeastern USA. On the other hand, the model predicted two strong fire signals clearly: near the border between Arizona and Mexico, and in Colorado (See Fig. 7e). All the fire-influenced areas in Fig. 7e were seen in the observations by HMS in Fig. 7f.
Comparing ASDTA plots and CMAQ
A backward-trajectory analysis for
Detected fire hotspots on 3 July 2013 as daily composite:
SENEX (Southeast Nexus) was a field campaign conducted by NOAA in
cooperation with the US EPA and the National Science Foundation in June and
July 2013. Although SENEX was not specifically designed for fire studies,
its airborne measurements included PM
Figure 8a shows a CMAQ-simulated
For CMAQ-simulated
A similar phenomenon was seen on SENEX flight #0710, which occurred during
flight transects from Tennessee to Tampa, FL. Figure 8b is a similar graph
to Fig. 8a. Based on
Observations from IMPROVE, HMS and SENEX identified fire signals on 3 July 2013. ASDTA retrievals were not available. Those signals were missed by the model. In this section, all of the evaluation methods addressed above were used to study potential causes of failure of the model to reproduce the fire signals.
At the MACA IMPROVE site on 3 July 2013, the wind direction at the surface was southeasterly, with no fire hotspots (solid black circle) located upwind of MACA (Fig. 9a). Without any identified hotspots upwind, the model missed fire signals observed at MACA on 3 July 2013.
Flight #0703 was a night mission targeting power plants in Missouri and
Arkansas. The flight path is shown in Fig. 9b and is colored by measured
Enhancements of CO and OC were also measured concurrently with
Figure 9e shows model-simulated
The reasons the model missed these fire observations are not clear. Figures 10, 11a and 11b suggest a few clues. Figure 10 is a backward-trajectory
analysis plot for the observations obtained during the SENEX flight on 3 July with observed
In support of the NOAA SENEX field experiment in June–July 2013, simulations were conducted including smoke emissions from fires. In this study, a system accounting for fire emissions in a chemical transport model is described, including a satellite fire-detecting system (HMS), a fire emission calculation model (BlueSky), a pre-processing of fire emissions (SMOKE) and simulation over the SENEX domain by CMAQ. The focus of this work is to evaluate the system's capability to capture fire signals identified by multiple observation data sets. These data sets included IMPROVE ground station observations, satellite observations (HMS plume shapefile and ASDTA) and airborne measurements from the SENEX campaign.
For the IMPROVE data, potential fire signals were identified by measured
potassium concentrations in PM
Generally, using HMS-detected fire hotspots and smoke data was useful for predictions of fire impacts and their evaluation. The HMS–BlueSky–SMOKE–CMAQ fire simulation system, which is also used in NAQFC, was able to capture most of the fire signals detected by multiple observations. However, the system failed to identify fire cases on 17 June and 3 July 2013 – thereby demonstrating two problems with the simulation system. One identified problem was the lack of a dynamical fire LBC bounding the CONUS domain to represent the inflow of strong fire signals originating outside the simulation domain. Secondly, the HMS quality control procedure eliminated fire hotspots that were not associated with visible smoke plumes, leading to an underestimation.
We were keen on understanding and quantifying the various uncertainties and observational constraints of this study; therefore the following rules of thumb were observed: (1) a holistic evaluation approach was adopted so that the fire smoke algorithm was interpreted as a single entity to avoid deadlock due to over-interpretation of uncertainty of the single component in the system. (2) An analysis conclusion applicable to the entire simulation period was drawn so that the episodic characteristics of the cases embedded in the simulation were averaged and generalized. This new methodology may benefit NAQFC. (3) We took advantage of the multiple perspectives of the observation systems that offered a wide spectrum of temporal and spatial variabilities intrinsic to the systems; (4) We were intentionally conservative in discarding data so that we maximized the sampling pool for statistical analysis and avoided unwittingly discarding poorly simulated cases, good outliers and weak but accurate signals.
Quantitative evaluation of fire emissions and their subsequent influences on ozone and particulate matter in this fire and smoke prediction system is challenging. Future work includes applying these findings to the NAQFC and improving the NAQFC system's capabilities to simulate fires accurately.
The source code used in this study is available online at
LP, HCK, PL, YHT, DT, BB and JM developed NAQFC fire smoke simulation system. RS, DT, SK, CYX and MGR provided simulation input and observation data. LP, HCK and WWC carried out model simulation and analyzed the result. LP, PL, RS and IS wrote the paper.
The authors declare that they have no conflict of interest.
The authors are thankful to Joost De Gouw and Martin G. Graus of the Earth System Research Laboratory, NOAA, for sharing the SENEX campaign data used in this study. Although this work has been reviewed by the Air Resources Laboratory, NOAA, and approved for publication, it does not necessarily reflect their policies or views.
This research has been supported by the AQAST (grant no. NNH14AX881).
This paper was edited by Fiona O'Connor and reviewed by two anonymous referees.