Smoke Emission Draft

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

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. AmericaThe number of ATSR satellite-observed fires

peaks in warm seasonFire 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.ModelMeasured 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.

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

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.

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 databaseDVoy 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 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.

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.

Smoke Events: Community Websites

• er

Analysts Console:Ad hoc Integration of distributed, heterogeneous

Derived Aerosol Optical Depth, Fire LocationsSeaWiFS Reflectance, PM2.5

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

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

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