scholarly journals The FireWork v2.0 air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System v2.03

2019 ◽  
Author(s):  
Jack Chen ◽  
Kerry Anderson ◽  
Radenko Pavlovic ◽  
Michael D. Moran ◽  
Peter Englefield ◽  
...  

Abstract. Biomass burning activities can produce large quantities of smoke and result in adverse air quality conditions in regional environments. In Canada, Environment and Climate Change Canada's (ECCC) operational FireWork 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's (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). 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-CFFEPS, has been evaluated over North America for July–September 2017 and June–August 2018, 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 PM2.5, O3, and NO2, and benchmarked against the operational FireWork system (FireWork-Ops). This comparison shows improved forecast performance and predictive skills for the FireWork-CFFEPS system. Modelled fire plume injection heights from CFFEPS based on fire energy thermodynamics show higher plume injection heights and larger variability. The changes in predicted fire emissions and injection height reduced the consistent over-predictions of PM2.5 and O3 seen in FireWork-Ops. On the other hand, there were minimal fire emission contributions to surface NO2, and results from FireWork-CFFEPS do not degrade NO2 forecast skill compared to the RAQDPS. Model performances statistics are slightly better for Canada than for the U.S., with lower errors and biases. The new system is still unable to capture the hourly variability of the observed values for PM2.5, but it captured the observed hourly variability for O3 concentration adequately. FireWork-CFFEPS also improves upon FireWork-Ops categorical scores for forecasting the occurrence of elevated air pollutant concentrations in terms of false alarm ratio (FAR), and critical success index (CSI).

2019 ◽  
Vol 12 (7) ◽  
pp. 3283-3310 ◽  
Author(s):  
Jack Chen ◽  
Kerry Anderson ◽  
Radenko Pavlovic ◽  
Michael D. Moran ◽  
Peter Englefield ◽  
...  

Abstract. 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 PM2.5, O3, and NO2, and benchmarked against the operational FireWork v1.0 system (FireWork-Ops). This comparison shows improved forecast performance and predictive skills for the FireWork–CFFEPS system. Modelled fire-plume injection heights from CFFEPS based on fire-energy thermodynamics show higher plume injection heights and larger variability. The changes in predicted fire emissions and injection height reduced the consistent over-predictions of PM2.5 and O3 seen in FireWork-Ops. On the other hand, there were minimal fire emission contributions to surface NO2, and results from FireWork–CFFEPS do not degrade NO2 forecast skill compared to the RAQDPS. Model performance statistics are slightly better for Canada than for the US, with lower errors and biases. The new system is still unable to capture the hourly variability of the observed values for PM2.5, but it captured the observed hourly variability for O3 concentration adequately. FireWork–CFFEPS also improves upon FireWork-Ops categorical scores for forecasting the occurrence of elevated air pollutant concentrations in terms of false alarm ratio (FAR) and critical success index (CSI).


2014 ◽  
Vol 14 (16) ◽  
pp. 23201-23236 ◽  
Author(s):  
P. A. Cleary ◽  
N. Fuhrman ◽  
L. Schulz ◽  
J. Schafer ◽  
J. Fillingham ◽  
...  

Abstract. Air quality forecast models typically predict large ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline Differential Optical Absorption Spectroscopy (DOAS) observations in southeastern Wisconsin, and as predicted by the National Air Quality Forecast System. From 2008–2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008–2010 measurements of ambient ozone conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI and Muskegon, MI up to 6 times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha with little dependence on position of the ferry or temperature but with highest differences during evening and night. Concurrent ozone forecast images from National Weather System's National Air Quality Forecast System in the upper Midwestern region surrounding Lake Michigan were saved over the ferry ozone sampling period in 2009. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. The model 1 and 8 h ozone mean biases were both 12 ppb higher than observed ozone, and maximum daily 1 h ozone mean bias was 10 ppb, indicating substantial ozone over-prediction over water. Trends in the bias with respect to location and time of day or month were also explored showing non-uniformity in model bias. Extreme ozone events were predicted by the model but not observed by ferry measurements.


2020 ◽  
Author(s):  
Raffaele Montuoro ◽  
Georg Grell ◽  
Li Zhang ◽  
Stuart McKeen ◽  
Gregory Frost ◽  
...  

<p>Significant progress has been made within the last couple of years towards developing online coupled systems aimed at providing more accurate descriptions of atmospheric chemistry processes to improve performance of global aerosol and air quality forecasts. Operating within the U.S. National Weather Service (NWS) research-to-operation initiative to implement the fully-coupled Next Generation Global Prediction System (NGGPS), cooperative development efforts have delivered two integrated online global prediction systems for aerosols (GEFS-Aerosols) and air quality (FV3GFS-AQM). These systems include recent advances in aerosol convective transport and wet deposition processes introduced into the SAS scheme of the National Center for Environmental Prediction’s (NCEP) latest Global Forecast System (GFS) based on the Finite-Volume cubed-sphere dynamical core (FV3). GEFS-Aerosols is slated to become the new control member of the NWS Global Ensemble Forecast System (GEFS). The model features an online-coupled version of the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model with a biomass-burning, plume-rise model and recent advances from NOAA Earth System Research Laboratory (ESRL), along with a state-of-the-art FENGSHA dust scheme from NOAA Air Resource Laboratory (ARL). FV3GFS-AQM incorporates a coupled, single-column adaptation of the U.S. Environmental Protection Agency’s (EPA) Community Multiscale Air Quality (CMAQ) model to improve NOAA’s current National Air Quality Forecast Capability (NAQFC). Both coupled systems’ design and development benefited from the use of the National Unified Operational Prediction Capability (NUOPC) Layer, which provided a common model architecture for interoperable, coupled model components within the framework of NOAA’s Environmental Modeling System (NEMS). Results from each of the described coupled systems will be discussed.</p>


2011 ◽  
Vol 13 (12) ◽  
pp. 3437 ◽  
Author(s):  
Claudio Carnevale ◽  
Giovanna Finzi ◽  
Enrico Pisoni ◽  
Vikas Singh ◽  
Marialuisa Volta

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