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Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System. BlueSky Smoke Forecasting Team Meeting February 1 8 th -20 th 2014, Edmonton. Objective.

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Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System

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  1. Development of On-line Wildfire Emissions for the Operational Canadian Air Quality Forecast System BlueSky Smoke Forecasting Team Meeting February 18th -20th 2014, Edmonton

  2. Objective • To add near-real-time biomass burning emissions to the operational air quality forecast system of Environment Canada (GEM-MACH) • 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. 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 (results included in this presentation) • 2014 • Expected delivery to EC’s operations for evaluation (experimental mode) this spring (April/May)

  4. Current Operational Air Quality Forecast System GEM-LAM10 Grid • Regional system with a domain covering North America • 10 km horizontal grid spacing • 80 vertical levels with lid at 0.1 hPa • runs twice daily (00z, 12z) GEM-MACH10 Grid • On-line coupled meteorology and chemistry model GEM-MACH • One-way coupling (meteorology affects chemistry) • Full process representation of oxidant and aerosol chemistry • 2-bin sectional representation of PM size distribution (i.e., 0-2.5 μm and 2.5-10 μm) with 8 chemical components

  5. Current Operational Air Quality Forecast System (cont’d) • Initial Conditions for GEM-MACH • Regional 4DVar analysis for meteorological variables • No assimilation of chemical constituents • Boundary Conditions for GEM-MACH • Regional Deterministic (Weather) Prediction System for meteorological variables • Climatology for chemical constituents • Emissions Inventories • Canada (2006) – soon will be updated to 2010 • USA (2012 projection of 2005 NEI) • Mexico (1999) • Emissions processed via SMOKE • Area sources • Mobile sources • Point sources • Biogenics calculated online (BEIS3)

  6. Model-Ready Wildfire Emissions SMOKE software processes the daily total wildfire emissions from US and Canada into hourly values, and converts VOC, NOx, PM into explicit model species. Emissions are combined and re-formatted for input into GEM-MACH Some assumptions on emitted species, e.g: PM2.5 56% organic carbon, 34% Crustal material, 9% elemental carbon, 1% sulfates Organic gas  19% Ethene, 9% Propane, 6% Alkene, 1% Alkane..

  7. Wildfire emissions (2012/2013) • Data Flow Fire Information from AVHRR/MODIS Canada USA BLUESKY CWFIS Fuel loading Weather model output (GEM) Consumption BLUESKY FEPS only Emissions(SMOKE) GEM-MACH

  8. Wildfire emissions (2014 approach) • Data Flow Fire Information from AVHRR/MODIS Canada USA CWFIS CWFIS Weather model output (GEM) BLUESKY FEPS only BLUESKY FEPS only Emissions(SMOKE) GEM-MACH

  9. 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 Added value products Total column PM10 and PM2.5 concentrations PM2.5 maps and animations based on the difference FireWork minus Operational to isolate plumes Alternate AQHI with FireWork Visibility ….

  10. Sample Output (2013-07-02 run) • PM2.5 animations : FireWork - Operational

  11. Case Study: Forest Fire in Northern Quebec, June 28th – July 8th, 2013

  12. Aqua/MODIS (2013/181; 06/30/2013 18:00 UTC) image showing forest fires in Quebec Source: http://rapidfire.sci.gsfc.nasa.gov/gallery/

  13. GOES image showing forest fires in Quebec Source: GOES image incorporated into EC tools (Ninjo)

  14. Region with the highest observed PM2.5 concentrations Observed MAX was 209ug/m3 at 53901.        Stations With Available Observations in Northern Quebec Closest stations with available PM2.5 observations are more than 500km away from the wildfire sources!  -stations used in analysis

  15. PM2.5 Correlations (observations vs forecasts) PM2.5 correlations at station locations from hourly observed/forecasted values. Period: June 28th – July 4th 2013. Significant improvements in PM2.5 correlations for the 21 stations affected. In general, the average summertime PM2.5 correlation per region is around 0.30. The model version with wild fire emissions increased the average correlation to 0.50.

  16. Objective ScoresPeriod: June 30th – July 3rd 2013(Wildfires in Quebec) PM2.5 scores improved. O3 (R, URMSE) scores improved also!

  17. PM2.5 (ug/m3) Time Series for Lac St-Jean / La Tuque region (Quebec) StationID: 53201 Station name: Pemonica StationID: 50604 Station name: Parc Tremblay

  18. Case study: Forest Fires in Northern Canada (Alberta, Saskatchewan, Manitoba and Northwest Territories) August 10th -20th, 2013

  19. Aqua/MODIS (2013/224; 08/12/2013; 19:25 UTC) image showing forest fires in AB, SK, MB and NWT NWT AB MB SK Source: http://rapidfire.sci.gsfc.nasa.gov/gallery/

  20. PM2.5 (ug/m3) Time Series for Fort Chipewyan (Alberta)

  21. Conclusions – for Canada • The goal of this project is to build the capacity of Environment Canada’s GEM-MACH operational air quality forecasting system to include wildfire emissions • Preliminary results show that fires producing a large amount of emissions can significantly impact PM2.5 forecast results • 2012/2013 evaluation: GEM-MACH captured general PM2.5 trends but underestimated magnitudes, especially for stations further downwind

  22. 2014 FireWork Suite Developpement

  23. 2013 FireWork Performance • Tested from May to October 2013 with CWFIS emissions for Canada and Gateway Inc emissions for USA • Canada • Improved scores (especially correlation) • Case studies for Quebec and Alberta – better results with FireWork • USA • Major problems with unrealistically high PM2.5 concentrations • USA forest fire emissions and advection had a bad impact on Canadian scores

  24. Additional Tests/Analysis • FireWork performance in Canada was correct which was not the case for the USA. Our study was focused on USA emissions for the months of July and August, when USA fire activity was intense. Three different wildfire sources for USA emissions were considered: • Gateway Inc (SFv1 files) • USFS files (SMOKE ready *csv files) • CWFIS hotspots.csv files

  25. Tests performed

  26. Average PM2.5 Concentrations (ug/m3) and Differences (AQN – GM_OPS) AQN – Average Concentration Difference : AQN – GM_OPS Forest fire emissions contribution to average PM2.5 concentrations

  27. AQN - Max Hourly Forecasted PM2.5 Concentrations (ug/m3)

  28. Average PM2.5 Concentrations (ug/m3) and Differences (AQM – GM_OPS) AQM – Average Concentration Difference : AQM – GM_OPS Forest fire emissions contribution to average PM2.5 concentration

  29. AQM - Max HourlyForecasted PM2.5 Concentrations (ug/m3)

  30. Average PM2.5 Concentrations (ug/m3) and Differences (AQO – GM_OPS) AQO – Average Concentration Difference : AQO – GM_OPS Forest fire emissions contribution to average PM2.5 concentration

  31. AQO - Max HourlyForecasted PM2.5 Concentrations (ug/m3)

  32. CWFISEmissions(input provided to GEM-MACH) for 2013-08-22 USFS Emissions(input provided to GEM-MACH) for 2013-08-22 Emission (PM2.5 (g/s) – only Crustal Materials) Time Series for one hotspot THERE IS ONLY ONE HOTPOT FOR THIS FIRE EVENT

  33. Conclusion for USFS (SFv2) SMOKE-Ready Emissions Files • For a specific fire event (in this case in California), when many fire sources are present in the USFS files, it will be represented by only one point. However, the emissions emitted by this one spot is a few hundred times higher than the emissions (summing) in the case of CWFIS emissions. • Here are the numbers for a USFS hotspot: • 06109SF11E74254 1 1 PM2_5  08/22/13GMT 54651.53709328100900F0 • 06109SF11E74254 1 1 PM10  08/22/13GMT 64488.813758 28100900F0 • 06109SF11E74254 1 1 CO 08/22/13GMT 672442.7697389999 28100900F0 • 06109SF11E74254 1 1 NH3 08/22/13GMT10951.305556000001 28100900F0 • 06109SF11E74254 1 1 NOX 08/22/13GMT 4788.048048999998 28100900F0 • 06109SF11E74254 1 1 SO2 08/22/13GMT 3711.822227 28100900F0 • 06109SF11E74254 1 1 VOC 08/22/13GMT157425.01751800004 28100900F0

  34. 2014 FireWork Planned ScenarioAQQ– Hourly Objective Scores Improved correlation in Canada, and very slight (non-significant) improvement in USA

  35. 2014 FireWork Planned Scenario AQQ – Daily Max and Categorical Scores Categorical Scores: USA Categorical Scores: Can Improved correlations and categorical scores (POD, FAR, CS)

  36. 2014 FireWork Planned Scenario Average PM2.5 Concentrations (ug/m3) and Differences (AQQ – GM_OPS) AQQ – Average concentration Difference : AQQ – GM_OPS Forest fire emissions contribution to average PM2.5 concentrations

  37. 2014 FireWork Planned Scenario AQQ - Max HourlyForecasted PM2.5 Concentrations (ug/m3)

  38. Contribution of Different Fire Emissions Sources to Forecasted PM2.5 Concentrations Difference : AQP – GM_OPS Difference : AQN – GM_OPS USA emiss from Gateway Inc USA emiss from USFS Difference : AQQ – GM_OPS USA emiss from CWFIS

  39. Analysis of Forecasted vs Observed Time Series for PM2.5 With Different USA Emissions

  40. CWFIS FF emission contribution to average PM2.5 concentration Region 1 (lat/long = 49.78/-112.78)Forecasted and Observed PM2.5 Concentrations (ug/m3) Objective: to analyze PM2.5 advection from USA forest fires Correlation Coeficient (Forecasted & Observed PM2.5 values)

  41. CWFIS FF emission contribution to average PM2.5 concentration ZOOM: Region 1 (lat/long = 49.78/-112.78)Forecasted and Observed PM2.5 concentrations (ug/m3) AQQ represents realistically PM2.5 advection from USA Correlation Coeficient (Forecasted & Observed PM2.5 values)

  42. CWFIS FF emission contribution to average PM2.5 concentration Region 2 (lat/long = 44.92/-114.20)Forecasted and Observed PM2.5 concentrations (ug/m3) Model with USFS emissions has the best performance in this region Correlation Coeficient (Forecasted & Observed PM2.5 values)

  43. CWFIS FF emission contribution to average PM2.5 concentration Region 2 (lat/long = 44.92/-114.20)Forecasted and Observed PM2.5 concentrations (ug/m3) Correlation Coeficient (Forecasted & Observed PM2.5 values)

  44. CWFIS FF emission contribution to average PM2.5 concentration Region 3 (lat/long = 39.13/-119.98)Forecasted and Observed PM2.5 concentrations (ug/m3) Correlation Coeficient (Forecasted & Observed PM2.5 values) Station 1: Station 1 in Region 3 Station 2: Station 3:

  45. CWFIS FF emission contribution to average PM2.5 concentration Region 3 (lat/long = 39.13/-119.98)Forecasted and Observed PM2.5 concentrations (ug/m3) AQQ has forecasted PM2.5 concentrations close to observed values Correlation Coeficient (Forecasted & Observed PM2.5 values) Station 1: Station 1 in Region 3 Station 2: Station 3:

  46. Conclusions • Decision: The 2013 PAR suite will be rerun for the entire 2013 fire season (May to October) with CWFIS forest fire emissions for both USA and Canada • Delivery to Operations: Scheduled for May 2014. In this case FireWork will be a real-time suite in experimental mode

  47. Conclusions - CWFIS Emissions • Advantages of using CWFIS emissions for USA/Can • Only one external source instead of two • Data source inside Fed Gov (NRCan) • No unrealistic PM2.5 over-forecast • No false plume advection from USA

  48. Conclusions - CWFIS Emissions • RESULTS (good points) • Time series analysis showed model capacity to predict pollution advection • Improved categorical scores (POD, FAR, CS) • Improved correlations for daily max scores • Improved correlations for Canadian hourly scores • RESULTS (bad points) • PM2.5 peak values sometimes underestimated when sources far away from station – case in Canada • Deterioration of MB and URMSE in hourly and daily max scores General conclusion: System is able to provide the information about forest fire pollution and its distribution to the forecasters

  49. Future Work • Improve the representation of wildfire emissions in the model (especially for USA) • Update GEM-MACH science parameterizations • inline plume rise algorithm • inline fire behavior adjustment by weather • with rain, snow or humidity changes

  50. Acknowledgements • BC Ministry of Environment: Steve Sakiyama • Natural Resources Canada • Canada forest Services: Kerry Anderson, Peter Englefield • Canada Centre for Remote Sensing: Robert Landry • USDA Forest Service • Sonoma Technology Inc. • Environment Canada • Meteorological Services • GEM-MACH development team • EC Regional Offices • Parks Canada

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