2003-10-15 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data...
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Biomass Smoke Emissions and Transport:Community-based Satellite and Surface Data Analysis
R.B. HusarWashington University in St. Louis
Presented at
NARSTO Workshop on Innovative Methods forEmission-Inventory Development and Evaluation
Austin, TX ; October 14-17, 2003
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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
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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’
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Pattern of Fires over N. AmericaThe number of ATSR satellite-observed fires
peaks in warm seasonFire onset and smoke amount is unpredictable
Fire Pixel Count:
Western US
North America
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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.ModelMeasured Smoke
Emission
Emission Comparison
Near Source: Smoke Emission
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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.
Dimension Abbr. Data SourcesSpatial 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
Particulate matter, incl. smoke is complex because of its multi-dimensionality
It takes at leas 8 independent dimensions to describe the PM concentration pattern
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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.
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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
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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.
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2002 Quebec Smoke Chemistry over the Northeast
Smoke (Organics) and Sulfate concentration data from VIEWS integrated databaseDVoy overlay of sulfate and organics during the passage of the smoke plume
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SeaWiFS, TOMS, Surface
Visibility, May 98
Surface ozone depressed under smoke
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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.
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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.
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Smoke Events: Community Websites
• er
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Analysts Console:Ad hoc Integration of distributed, heterogeneous
Derived Aerosol Optical Depth, Fire LocationsSeaWiFS Reflectance, PM2.5
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Lose Federation of Heterogeneous Distributed Providers, Consumers and Value-Adders
Federated information system schematics.Providers expose part data (green) to othersFederation facilitates connectivity, exchange
Schematics of a the value-adding network nodeComponents embedded in the federated network
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Surface wind vector Back/Forw. Trajectories Temperature
NAAPS model
PM/Bext time series
Bext contoursPM2.5 contours
Satellite Animation
Real-time PM Monitoring DashboardExample Views – Selected from Dozens of spatial, temporal, height cross-sections
Satellite Image
Dew point / relhum
Satellite Aerosol
Webcam
Weather
PM/Haze
PM/Haze
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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
TaskingDistribution
Processing
Exploitation
NASA ESE Information
Cycle
AirQuality
AssessmentCompare to GoalsPlan ReductionsTrack Progress
Controls (Actions)
Monitoring(Sensing)
Set GoalsCAAA
NAAQS
AQ Management
Loop