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
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20051031 Biomass Smoke Emissions and Transport: Community-based Satellite and Surface Data Analysis
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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
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
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, absorption?
California Smoke 1999 Quebec Smoke 2002
2002 Quebec SmokeGOES East & ASOS Bext & MPL Lidar
July 6, 2002 8:15, 16:15 EST
Smoke Plumes over the Southeast
• Satellite detection yields the origin and location is the shape of smoke plumes
R 0.68 m
G 0.55 m
B 0.41 m
0.41 m
0.87 m
• The influence of the smoke is to increase the reflectance ant short wavelength (0.4 m)
• At longer wavelength, the aerosol reflectance is insignificant.
Real-Time Smoke Emission Estimation:Local Smoke Model with Data Assimilation
e..g. MM5 winds, plume model
Local Smoke Simulation Model
AOT Aer. Retrieval
Satellite Smoke
Visibility, AIRNOW
Surface Smoke
Assimilated Smoke Pattern
Continuous Smoke Emissions
Assimilated Smoke Emission for Available Data
Fire Pixel, Field Obs
Fire Loc, Energy
Assimilated Fire Location, Energy
NOAA, NASA, NFS NOAA, NASA, NFS NOAA, EPA, States
Emission Model
Land Vegetation
Fire ModelRegional AQ
Model
Kansas Agricultural Smoke, April 12, 2003
Fire Pixels PM25 Mass, FRM65 ug/m3 max
Organics35 ug/m3 max
Ag Fires
SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue
ASOS Visibility Monitoring System (1200 Sites)
• The Automated Surface Observing System, ASOS; weather every minute.
• The forward scattering (30-500) visibility sensor has a range 17 ft to 30 miles.
• The synoptic visibility data are truncated (<1/4, 1/4,..10+ miles)
• For smoke and haze events (vis. < 10 mile) truncation not a problem
Diurnal Cycle – Surface Bext, April 12, 2003
00 02 04 06
08 10 12 14
16 18 20 22
Night
Day
Night
High Night Bext Low Day Bext
Smoke
0504142345 HMS + GOES
0504142345 HMS + GOES
Current Air Quality Information ‘Ecosystem’ (Draft for Feedback)
AQ information includes emissions, ambient & satellite data and model outputs
The distributed data are produced and provided by agencies, mostly through portals
Providers have different access protocols, formats, and information usage conditions
This lack of interoperability causes the under-utilization of the rich data resources
Future Integrated AQ information System (Draft for Feedback)
DataMart
VIEWS
NEISGEI
AIRNow
AQMod
DAACs
ASOS
NEI
Emission
IDEA
GASP
Missions
WeaMod
Forecast
GloMod
FireInv
Data Federation Distributed, Virtual, Uniform
AQ Forecasting
AQ Compliance
Status and Trends
Network Assess.
Data Processing Filtering, Aggregation, Fusion
Info Products Reports, Websites
Data are maintained by custodians and exposed through ‘portals’ Mediators uniformly ‘wrap’ data and provide processing servicesAnalysts program the services to create application-specific productsResponsibility is shared among data providers and mediator/ integratorsESIPFed can provide the infrastructure and tools for the AQ info system
Run and click PPT Slideshow to see chart animations
Non-intrusive Linking & Mediation Data UsersData Providers
DataFed Description
DataFed VisionBetter air quality management and science through by effective use of relevant data
DataFed GoalsFacilitate the access and flow of atmospheric data from provider to usersSupport the development of user-driven data processing value chainsParticipate in specific application projects
Approach: Mediation Between Users and Data ProvidersDataFed assumes spontaneous, autonomous emergence of AQ data (a la Internet)Non-intrusively wraps datasets for access by web servicesWS-based mediators provide homogeneous data views e.g. geo-spatial, time...
End-user programming of data access and processing through WS composition (limited)
ApplicationsBuilding browsers and analysis tools for distributed monitoring data Serve as data gateway for user programs; web pages, GIS, science toolsDataFed is currently focused on the mediation of air quality data
Anatomy of a Wrapper Service: TOMS Satellite Image Data
• Given the URL template and the image description, the wrapper service can access the image for any day, any spatial subset using a HTTP URL or SOAP protocol:
• Wrapper classes are available for geo-spatial (incl. satellite) images, SQL servers, text files,etc. The mediator classes are implemented as web services for uniform data access, transformation and portrayal.
• Data are accessed from autonomous, distributed providers• DataFed ‘wrappers’ provide uniform geo-time referencing• Tools allow space/time overlay, comparisons and fusion
Near Real Time Data IntegrationDelayed Data Integration
– Data Catalog– Data Browser– PlumeSim, Animator– Combined Aerosol Trajectory Tool (CATT)
Consoles: Data from diverse sources are displayed to create a rich context for exploration and analysis
CATT: Combined Aerosol Trajectory Tool for the browsing backtrajectories for specified chemical conditions
Viewer: General purpose spatio-temporal data browser and view editor applicable for all DataFed datasets
Midwest HazeCam Image ConsoleImage Archive and Browser
• Hourly Midwest HazeCam Images are archived by DataFed data access system• Archived images for all cameras can be browsed through this console• HazeCam URL for a day: http://www.datafed.net/consoles/MWH_WebCams.asp?image_width=400&image_height=300&datetime=2005-01-31T13:00:00
• URL for a site and day: http://webapps.datafed.net/datasets/webcam/cincinnati/20050131-13mwhcincinnati.jpg
• URLs can be embedded as links into emails, bookmarks, web pages, PPT and PDF files.
Midwest HazeCam Image Browser
Select date and time Set image size and time MW HazeCam ConsoleOther FASTNET