2003-10-15 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data AnalysisPresentation Transcript
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
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
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’
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
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. Aerosol Surface 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
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.
Particulate matter, incl. smoke is complex because of its multi-dimensionality It takes at leas 8 independent dimensions to describe the PM concentration pattern 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
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.
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
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.
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
SeaWiFS, TOMS, Surface Visibility, May 98 Surface ozone depressed under smoke
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.
Smoke Complexity Management: R eal-Time A erosol W atch (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.
Smoke Events: Community Websites
Analysts Console: Ad hoc Integration of distributed, heterogeneous
SeaWiFS Reflectance, PM2.5
Derived Aerosol Optical Depth, Fire Locations
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
Real-time PM Monitoring Dashboard Example Views – Selected from Dozens of spatial, temporal, height cross-sections Satellite Animation Satellite Aerosol Weather PM/Haze PM/Haze Surface wind vector Back/Forw. Trajectories Temperature NAAPS model PM/Bext time series Bext contours PM2.5 contours Satellite Image Dew point / relhum Webcam
Satellite applications to Smoke/PM management
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