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 for Emission-Inventory Development and Evaluation Austin, TX ; October 14-17, 2003

2. 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). Fire Zones of North America

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 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 Satel. AerosolSurface Visib.Surface Species Measured Smoke Pattern Smoke Comparison Surface Species Model - MCarlo Model - CMAQ Far Source: Transport & Pattern Distant smoke concentration is estimated from aerosol species, mass, visibility and satellite data Models simulate concentration pattern Model – data comparison, reconciliation Fire Location Fire Model Local Disp.Model Measured Smoke Emission Emission Comparison Near Source: Smoke Emission

6. Scientific Challenge: Description of smoke 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. DimensionAbbr.Data Sources Spatial dimensionsX, YSatellites, dense networks HeightZLidar, soundings TimeTContinuous monitoring Particle sizeDSize-segregated sampling Particle CompositionCSpeciated analysis Particle Shape/FormFMicroscopy Ext/Internal MixtureMMicroscopy Particulate matter, incl. smoke is complex because of its multi-dimensionality It takes at leas 8 independent dimensions to describe the PM concentration pattern

7. 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. Satellite-Integral 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 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? California Smoke 1999 Quebec Smoke 2002

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 Radiation Mexican 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, managers and 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 Website for 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 LocationsSeaWiFS 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 Back/Forw. Trajectories Temperature NAAPS model PM/Bext time series Bext contours PM2.5 contours Satellite Animation Real-time PM Monitoring Dashboard Example Views – Selected from Dozens of spatial, temporal, height cross-sections Satellite Image Dew point / relhum Satellite Aerosol Webcam Weather PM/Haze

18. Satellite applications to Smoke/PM management Observation-based smoke emissions : 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 Decision Support Systems Standards Based Products Platforms, Sensors Data Distribution Handling Tasking Distribution Processing Exploitation NASA ESE Information Cycle Air Quality Assessment Compare to Goals Plan Reductions Track Progress Controls (Actions) Monitoring (Sensing) Set Goals CAAA NAAQS AQ Management Loop