Abstract. 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 (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

Fires represent an air quality challenge because they are large, dynamic and transient sources of particulate matter and ozone precursors. Transported smoke can deteriorate air quality over large regions. Fire severity and frequency are likely to increase in the future, exacerbating an existing problem. Using the National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke data for North America for the period 2007 to 2014, we examine a subset of fires that are confirmed to have produced sufficient smoke to warrant the initiation of a U.S. National Weather Service smoke forecast. We find that gridded HMS-analyzed fires are well correlated (r = 0.84) with emissions from the Global Fire Emissions Inventory Database 4s (GFED4s). We define a new metric, smoke hours, by linking observed smoke plumes to active fires using ensembles of forward trajectories. This work shows that the Southwest, Northwest, and Northwest Territories initiate the most air quality forecasts and produce more smoke than any other North American region by measure of the number of HYSPLIT points analyzed, the duration of those HYSPLIT points, and the total number of smoke hours produced. The average number of days with smoke plumes overhead is largest over the north-central United States. Only Alaska, the Northwest, the Southwest, and Southeast United States regions produce the majority of smoke plumes observed over their own borders. This work moves a new dataset from a daily operational setting to a research context, and it demonstrates how changes to the frequency or intensity of fires in the western United States could impact other regions.

Increases in the severity and frequency of large fires necessitate improved understanding of the influence of smoke on air quality and public health. The objective of this study is to estimate the effect of smoke from fires across the continental U.S. on regional air quality over an extended period of time. We use 2006–2013 data on ozone (O 3 ), fine particulate matter (PM 2.5 ), and PM 2.5 constituents from environmental monitoring sites to characterize regional air quality and satellite imagery data to identify plumes. Unhealthy levels of O 3 and PM 2.5 were, respectively, 3.3 and 2.5 times more likely to occur on plume days than on clear days. With a two-stage approach, we estimated the effect of plumes on pollutants, controlling for season, temperature, and within-site and between-site variability. Plumes were associated with an average increase of 2.6 p.p.b. (2.5, 2.7) in O 3 and 2.9 µg/m 3 (2.8, 3.0) in PM 2.5 nationwide, but the magnitude of effects varied by location. The largest impacts were observed across the southeast. High impacts on O 3 were also observed in densely populated urban areas at large distance from the fires throughout the southeast. Fire smoke substantially affects regional air quality and accounts for a disproportionate number of unhealthy days.

The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 mm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers.There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow-and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseousphase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U. S. Environmental Protection Agency's (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005.Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA's AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 mu gm(-3) against a monthly average of 9.4 mu gm(-3) for the north-central United States.

Biomass burning has a significant and adverse impact on air quality, climate change, and various ecosystems. The Hazard Mapping System (HMS) detects fires using data from multiple satellite sensors in order to maximize its fire detection rate. However, to date, the detection rate of the HMS fire product for small fires has not been well studied, especially using ground-based fire records. This paper utilizes the 2011 fire information compiled from ground observations and burn authorizations in Georgia to assess the comprehensiveness of the HMS active fire product. The results show that detection rates of the hybrid HMS increase substantially by integrating multiple satellite instruments. The detection rate increases dramatically from 3% to 80% with an increase in fire size from less than 0.02km(2) to larger than 2km(2), resulting in detection of approximately 12% of all recorded fires which represent approximately 57% of the total area burned. The spatial pattern of detection rates reveals that grid cells with high detection rates are generally located in areas where large fires occur frequently. The seasonal analysis shows that overall detection rates in winter and spring (12% and 13%, respectively) are higher than those in summer and fall (3% and 6%, respectively), mainly because of higher percentages of large fires (>0.19km(2)) that occurred in winter and spring. The land cover analysis shows that detection rates are 2-7 percentage points higher in land cover types that are prone to large fires such as forestland and shrub land.

As the climate in California warms and wildfires become larger and more severe, satellite-based observational tools are frequently used for studying impact of those fires on air quality. However little objective work has been done to quantify the skill these satellite observations of smoke plumes have in predicting impacts to PM2.5 concentrations at ground level monitors, especially those monitors used to determine attainment values for air quality under the Clean Air Act. Using PM2.5 monitoring data from a suite of monitors throughout the Central California area, we found a significant, but weak relationship between satellite-observed smoke plumes and PM2.5 concentrations measured at the surface. However, when combined with an autoregressive statistical model that uses weather and seasonal factors to identify thresholds for flagging unusual events at these sites, we found that the presence of smoke plumes could reliably identify periods of wildfire influence with 95% accuracy.

Generation of available potential energy (APE) is computed for the warm sector of an extratropical cyclone containing intense convection. Three hourly mesoscale rawinsonde data from the 10–11 April day of AVESESAME 1979 are used to evaluate generation by five diabatic components. Convective latent heat release is found to be the dominant diabatic term during times of intense convection, whereas stable latent heating provides a relatively small contribution. Sensible heat transfer is important near the surface during the afternoon. Solar and infrared radiative processes are quite significant in regions of low-level stratus and convective activity. Solar absorption during midday is observed to be much greater than at the standard rawinsonde observation times. The use of subjectively specified cloud data and sophisticated radiative transfer models permit more detailed resolution of cloud effects thin possible in earlier studies of this type. Negative generation of APE is dominant during the 24 h period because convective latent heating is superimposed on areas of negative efficiency. The only consistent positive generation is due to infrared cooling. Sensible heating is the third largest generating component, while stable heating and solar absorption are least significant. Results document rapid temporal variations in generation as well as contrasts between energetics of the warm sector and those of entire cyclones.

Several real-time smoke prediction systems have been developed worldwide to help land managers, farmers, and air quality regulators balance land management needs against smoke impacts. Profiled here are four systems that are currently operational for regional domains for North America and Australia, providing forecasts to a well-developed user community. The systems link fire activity data, fuels information, and consumption and emissions models, with weather forecasts and dispersion models to produce a prediction of smoke concentrations from prescribed fires, wildfires, or agricultural fires across a region. The USDA Forest Service's BlueSky system is operational for regional domains across the United States and obtains prescribed burn information and wildfire information from databases compiled by various agencies along with satellite fire detections. The U.S. National Oceanic and Atmospheric Administration (NOAA) smoke prediction system is initialized with satellite fire detections and is operational across North America. Washington State University's ClearSky agricultural smoke prediction system is operational in the states of Idaho and Washington, and burn location information is input via a secure Web site by regulators in those states. The Australian Bureau of Meteorology smoke prediction system is operational for regional domains across Australia for wildfires and prescribed burning. Operational uses of these systems are emphasized as well as the approaches to evaluate their performance given the uncertainties associated with each system's subcomponents. These real-time smoke prediction systems are providing a point of interagency understanding between land managers and air regulators from which to negotiate the conflicting needs of ecological fire use while minimizing air quality health impacts.

An overview of the National Oceanic and Atmospheric Administration's (NOAA) current operational Smoke Forecasting System (SFS) is presented. This system is intended as guidance to air quality forecasters and the public for fine particulate matter (<= 2.5 mu m) emitted from large wildfires and agricultural burning, which can elevate particulate concentrations to unhealthful levels. The SFS uses National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS), which is based on satellite imagery, to establish the locations and extents of the fires. The particulate matter emission rate is computed using the emission processing portion of the U. S. Forest Service's BlueSky Framework, which includes a fuel-type database, as well as consumption and emissions models. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to calculate the transport, dispersion, and deposition of the emitted particulate matter. The model evaluation is carried out by comparing predicted smoke levels with actual smoke detected from satellites by the HMS and the Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product. This overlap is expressed as the figure of merit in space (FMS), the intersection over the union of the observed and calculated smoke plumes. Results are presented for the 2007 fire season (September 2006-November 2007). While the highest FMS scores for individual events approach 60%, average values for the 1 and 5 mu g m(-3) contours for the analysis period were 8.3% and 11.6%, respectively. FMS scores for the forecast period were lower by about 25% due, in part, to the inability to forecast new fires. The HMS plumes tend to be smaller than the corresponding predictions during the winter months, suggesting that excessive emissions predicted for the smaller fires resulted in an overprediction in the smoke area.

Environmental satellite data provides a unique capability to monitor large areas of the globe for the occurrence of fires and the smoke that they generate which can cause considerable degradation of air quality on a regional basis. The Hazard Mapping System (HMS) incorporates seven polar and geostationary satellites into a single workstation environment. While individual satellite platforms can provide important information that can be used in air quality models, integrating several platforms allows for the combined strengths of various spacecraft instruments to overcome their individual limitations. The HMS was specifically designed as an interactive tool to identify fires and the smoke emissions they produce over North America in an operational environment. Automated fire detection algorithms are employed for each of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via examination of the satellite imagery.Areas of smoke are outlined by the analyst using animated visible channel imagery. An estimate of the smoke concentration is assigned to each plume outlined. The automated Geostationary Operational Environmental Satellite (GOES) Aerosol and Smoke Product (GASP) is used as an aid in providing smoke concentrations and identifying areas of smoke.HMS analysts provide estimates on the size, initiation and duration of smoke emitting fires that are used as input to NOAA's national air quality forecast capability. This system is currently providing 48 hour smoke forecast guidance for air quality forecasters and utilizes the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model.

A detailed evaluation of NOAA's Smoke Forecasting System (SFS) is a fundamental part of its development and further refinement. In this work, particulate matter with a diameter less than or equal to 2.5-mu m (PM2.5) concentration levels, simulated by the SFS, have been evaluated against satellite and surface measurements. Four multiday forest fire case studies, one covering the continental United States, two in California, and one near the Georgia-Florida border, have been analyzed. The column-integrated PM2.5 concentrations for these cases compared to the satellite measurements showed a similar or better statistical performance than the mean performance of the SFS for the period covering 1 September 2006-1 November 2007. However, near the surface, the model shows a tendency to overpredict the measured PM2.5 concentrations in the western United States and underpredict concentrations for the Georgia-Florida case. Furthermore, a sensitivity analysis of the model response to changes in the smoke release height shows that the simulated surface and column-integrated PM2.5 concentrations are very sensitive to variations in this parameter. Indeed, the model capability to represent the measured values is highly dependent on the accuracy of the determination of the actual injection height and in particular to whether the smoke injection actually occurred below or above the planetary boundary layer.

The National Oceanic and Atmospheric Administration (NOAA) produces a daily satellite based smoke and fire analysis for the US. The capability to specify smoke concentration levels and to identify the start time, duration and areal extent of a particular fire which is producing smoke emissions has recently been added. Smoke concentration values are obtained from visual inspection of animated Geostationary Operational Environmental Satellite (GOES) visible band imagery in conjunction with output from the automated GOES Aerosol and Smoke Product (GASP). There are three categories of smoke concentration utilized: 5, 16 and 27 μg/m3, which correspond to light, medium, and dense smoke respectively. The smoke concentration values are available for air quality managers and other users to download and/or view as GIS shapefiles. The initial time and duration of smoke emissions are also obtained through inspection of animated GOES visible imagery by an analyst. This information is incorporated into the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model which produces a 48 hour forecast of smoke dispersion and transport. In addition to approximating smoke emissions and the diurnal variations observed in wildfires the specification of emission duration allows for the representation of short duration agricultural and prescribed burns as well. A number of examples depicting various smoke concentrations, including fires of different size and duration, will be shown. HYSPLIT performance in depicting the observed smoke will also be presented.

The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as an interactive tool to identify fires and the smoke emissions they produce over North America in an operational environment. The system utilizes 2 geostationary and 5 polar orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery. Areas of smoke are outlined by the analyst using animated visible channel imagery. A quantitative assessment of the smoke concentration is not performed at this time. However, integration of automated aerosol and smoke products into the HMS, such as the GOES Aerosol and Smoke Product (GASP) and the MODIS aerosol product in early 2006 and the aerosol product from the Ozone Monitoring Instrument (OMI) later in 2006 are expected to aid in providing smoke concentrations and identifying areas of smoke. HMS analysts denote fires that are producing smoke emissions. These fire locations are used as input to the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. In 2005 the model was upgraded to adjust emission rates based on the Blue Skies framework as opposed to using a constant emission rate. Future improvements include specification of fire duration and start/end time of emissions. It is expected that these enhancements will increase the accuracy of the total amount of emissions and their dispersion.

The Hazard Mapping System (HMS) was developed in 2001 by the National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite and Data Information Service (NESDIS) as an interactive tool to identify fires and the smoke emissions they produce over North America in an operational environment. The system utilizes 2 geostationary and 5 polar orbiting environmental satellites. Automated fire detection algorithms are employed for each of the sensors. Analysts apply quality control procedures for the automated fire detections by eliminating those that are deemed to be false and adding hotspots that the algorithms have not detected via a thorough examination of the satellite imagery.Areas of smoke are outlined by the analyst using animated visible channel imagery. A quantitative assessment of the smoke concentration is not performed at this time. However, integration of automated aerosol and smoke products into the HMS, such as the Geostationary Operational Environmental Satellite (GOES) Aerosol and Smoke Product (GASP) and the MODIS aerosol product in early 2006 and the aerosol product from the Ozone Monitoring Instrument (OMI) in late 2006 are expected to aid in providing smoke concentrations and identifying areas of smoke.HMS analysts also denote fires that are producing smoke emissions detected in satellite imagery as well as the start and end times of the emissions. These fire locations are used as input to the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. HYSPLIT utilizes a dynamic emissions rate for these fires as specified by the Blue Skies framework.