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FireWork Wildfire Emissions in the Canadian GEM-MACH Air Quality Forecast System

This project aims to integrate near-real-time biomass burning emissions into Environment Canada's operational air quality forecasting system (GEM-MACH). FireWork, the wildfire version of GEM-MACH, will provide valuable information for the Air Quality Health Index (AQHI) and other applications like smoke dispersion and visibility. The project timeline, development process, and current modeling strategy are detailed, highlighting the importance of accurate wildfire emissions forecasting. Efforts to improve wildfire detection and air quality forecasts, including using TEMPO data, are also discussed.

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FireWork Wildfire Emissions in the Canadian GEM-MACH Air Quality Forecast System

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  1. FireWork Wildfire Emissions in the Canadian GEM-MACH Air Quality Forecast System AQMAS Radenko Pavlovic TEMPO Workshop Dorval, Canada, 13 - 14 November 2013

  2. Objective The goal of this project is to build the capacity of the operational air quality forecasting system of Environment Canada (GEM-MACH) to include near-real-time biomass burning emissions GEM-MACH with wildfire emissions can also serve as an input for the Air Quality Health Index (AQHI*) Some other valuable information can also be provided: smoke dispersion information visibility information *AQHI = (10/10.4)*100*[(exp(0.000871*NO2)-1) + (exp(0.000537*O3)-1) + (exp(0.000487*PM2.5)-1)]

  3. Edmonton without smoke Grant MacEwan University

  4. Edmonton, August 19, 2010, 2:01 PM (Hourly PM2.5 ~250 μg/m3) Bad air quality due to wildfires Grant MacEwan University ??

  5. Timeline of FireWork Development 2011 Developed fire emissions method for GEM-MACH Processed fire information from Canada and USA for historical cases Established a case study for a 2010 wildfire in British Columbia 2012 The model was run for the entire summer (May-October) at 15-km grid spacing; the new wildfire version of GEM-MACH is named “FireWork” 2013 The model ran for the entire summer with operational settings at 10-km grid spacing 2014 Expected delivery to EC’s operations for evaluation (experimental mode)

  6. Wildfire emissions • Data Flow Fire Information from AVHRR/MODIS Canada USA BLUESKY CWFIS Fuel loading Fuel loading Weather model output (GEM) Consumption Consumption BLUESKY FEPS only Emissions(SMOKE) GEM-MACH

  7. Current FireWork Modelling Strategy The experimental set-up uses the same configuration of the operational version of GEM-MACH to execute a separate run that takes into account fire emissions Run in parallel with wildfire emissions: Run twice a day Delay = ~1hr after the operational forecast Products PM2.5 maps and animations based on the difference FireWork minus Operational to isolate plumes Alternate AQHI with FireWork

  8. Available productsForest fire contribution to surface PM2.5, estimated from FireWork forecast, July 1st-3rd 2013

  9. Wildfire Emissions and Forecast Uncertainties • MODIS images • Not always available • Interpreted by Hazard Mapping System (HMS) analyst: can be subjective; potential human errors • Operational considerations: reliability and timeliness of remote sensing data • Wildfire emissions estimates for US and Canada come from different sources • Treated differently • Interruptions in data availability • Limited and/or missing observations for northern parts of Canada • Limited information on the dispersion and injection into the atmosphere of wildfire emissions Some efforts are already underway to improve wildfire detection and related air quality forecasts (ex: CWFMS Microsatellite mission selected by the Canadian Space Agency for proposal evaluation)

  10. How can TEMPO help FireWork? • Improve wildfire detection and HMS analysis with available observations (UV/visible, NO2, SO2, aerosol optical depth, etc) • Improve FireWork performance analysis especially where little or no AQ stations available • Better understand wildfire emissions dispersion in the atmosphere • Serve as a potential tool/information for forecasters: compare TEMPO mappings with forecasted FireWork fields

  11. Thank you

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