Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis

1. Biomass Smoke Emissions and Transport:Community-based Satellite and Surface Data Analysis R.B. Husar Washington University in St. Louis Presented at NARSTO Workshop on Innovative Methods forEmission-Inventory Development and Evaluation Austin, TX ; October 14-17, 2003

2. Fire Zones of North America FIRE and Norm. Diff. Veg. Index, NDVI The ‘Northern’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires. The ‘Northwestern’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires The ‘Southeastern’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires. The ‘Mexican’ zone over low elevation C America is the most intense fire zone, sharply separated from arid and the lush regions. Fires are absent in arid low-vegetation areas (yellow) and over areas of heavy, moist vegetation (blue).

3. Seasonality of Fire Dec, Jan, Feb is generally fire-free except in Mexico, and W. Canada Mar, Apr, May is the peak fire season in Mexico and Cuba; fires occur also in Alberta-Manitoba and in OK-MO region Jun, Jul, Aug is the peak fire season in N. Canada, Alaska and the NW US. Sep, Oct, Nov is fire over the ‘Northwest’ and the “Southeast’

4. Pattern of Fires over N. America The number of ATSR satellite-observed fires peaks in warm season Fire onset and smoke amount is unpredictable Fire Pixel Count: Western US North America

5. Smoke Emission and Concentration Pattern:Measured and Modeled Near Source: Smoke Emission Far Source: Transport & Pattern Model - CMAQ • Smoke emission is by Fire Model and by observations • Observed smoke emission rate is by assimilating surface and satellite data into a local dispersion model Emission Comparison Smoke Comparison Fire Model Model - MCarlo Measured Smoke Emission Measured Smoke Pattern Local Disp.Model Fire Location Satel. Aerosol Surface Visib. Surface Species Surface Species • Distant smoke concentration is estimated from aerosol species, mass, visibility and satellite data • Models simulate concentration pattern • Model – data comparison, reconciliation

6. Dimension Abbr. Data Sources Spatial dimensions X, Y Satellites, dense networks Height Z Lidar, soundings Time T Continuous monitoring Particle size D Size-segregated sampling Particle Composition C Speciated analysis Particle Shape/Form F Microscopy Ext/Internal Mixture M Microscopy Scientific Challenge: Description of smoke Particulate matter, incl. smoke is complex because of its multi-dimensionality It takes at leas 8 independent dimensions to describe the PM concentration pattern • Gaseous concentration: g (X, Y, Z, T) • Aerosol concentration: a (X, Y, Z, T, D,C,F, M) • The ‘aerosol dimensions’ size D, composition C, shape F, and mixing M determine the impact on health, and welfare.

7. Satellite-Integral Technical Challenge: Characterization • PM characterization requires many different instruments and analysis tools. • Each sensor/network covers only a fraction of the 8-D PM data space. • Most of the 8D PM pattern is extrapolated from sparse measured data. • Satellites, integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.

8. Smoke types: blue, yellow, white Quebec Smoke 2002 California Smoke 1999 Smoke from major fires comes in different colors, e.g. blue, yellow. The chemical, physical and optical characteristics of smokes are not known Can the reflectance color be used to classify smokes? Can column AOT be retrieved for optically thick smoke? Multiple scattering, absoption?

9. July 2020 Quebec Smoke Event Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002 – • PM2.5 time series for New England sites. Note the high values at White Face Mtn. • Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

10. 2002 Quebec Smoke Chemistry over the Northeast Smoke (Organics) and Sulfate concentration data from VIEWS integrated database DVoy overlay of sulfate and organics during the passage of the smoke plume

11. SeaWiFS, TOMS, Surface Visibility, May 98 Surface ozone depressed under smoke

12. Aerosol Optical Depth and Solar RadiationMexican Smoke Event, May 1998 Spectral aerosol optical thickness measured by the AERONET network at Bondville, IL. Solar radiation data derived from Shadowband Radiometer Network at Big Bend, TX.

13. Smoke Complexity Management:Real-Time Aerosol Watch (RAW) RAW is an open communal activity to study aerosol events(e.g. smoke and dust) , including detection, tracking and impact on PM and haze. The main asset of RAW is the community of data analysts, modelers, managersand others participating in the production of actionable knowledge from observations, models and human reasoning The RAW community is supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools. Initial web tools include the Community Websitefor open community interaction, the Analysts Console for diverse data access and the Managers Console for AQ management decision support.

14. Smoke Events: Community Websites • er

15. Analysts Console:Ad hoc Integration of distributed, heterogeneous Derived Aerosol Optical Depth, Fire Locations SeaWiFS Reflectance, PM2.5

16. Lose Federation of Heterogeneous Distributed Providers, Consumers and Value-Adders Federated information system schematics. Providers expose part data (green) to others Federation facilitates connectivity, exchange Schematics of a the value-adding network node Components embedded in the federated network

17. Surface wind vector PM/Bext time series Bext contours Back/Forw. Trajectories PM2.5 contours Satellite Image Temperature Dew point / relhum NAAPS model Webcam Real-time PM Monitoring DashboardExample Views – Selected from Dozens of spatial, temporal, height cross-sections Weather Satellite Aerosol PM/Haze Satellite Animation PM/Haze

18. Set Goals CAAA NAAQS Monitoring (Sensing) AQ Management Loop AssessmentCompare to GoalsPlan ReductionsTrack Progress AirQuality Controls (Actions) Satellite applications to Smoke/PM management Decision Support Systems Satellite applications to Smoke/PM management • Observation-based smokeemissions: input to dynamic and receptor models • Real-time event analysis/forecasting for regulatory and public needs • PM exceptional event waivers for NAAQS; • PM climatology for NAAQS; spatial analysis; complement NAAMS/Ncore • Policy and SIP development: NAAQS, Regional Haze rule; Treaties Tasking Distribution NASA ESE Information Cycle Data Distribution Handling Platforms, Sensors Processing Standards Based Products Exploitation