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![Page 1: OMI NO 2 for AQ Management Applications Rudolf Husar, CAPITA, Washington University NO2 Workshop, EPA HQ, Oct 30, 2007 Regulatory Support: Exceptional.](https://reader035.fdocuments.net/reader035/viewer/2022070400/56649f0e5503460f94c2285f/html5/thumbnails/1.jpg)
OMI NO2 for AQ Management Applications
Rudolf Husar, CAPITA, Washington UniversityNO2 Workshop, EPA HQ, Oct 30, 2007
Regulatory Support:
Exceptional Event Quantification
Policy Development:
Hemispherical Pollutant Transport
![Page 2: OMI NO 2 for AQ Management Applications Rudolf Husar, CAPITA, Washington University NO2 Workshop, EPA HQ, Oct 30, 2007 Regulatory Support: Exceptional.](https://reader035.fdocuments.net/reader035/viewer/2022070400/56649f0e5503460f94c2285f/html5/thumbnails/2.jpg)
Hemispheric Transport of Air Pollutants (HTAP)Data Network:
TF HTAP Workshop Forshungszentrum Juelich, Oct 17-19, 2007, Juelich, Germay
Application Examples for NOx Analysis
Collaborators:
Rudolf Husar, Washington U. St. Louis
Stefan Falke, Northrop, Wash U.
Greg Leptoukh, NASA, Goddard
Martin Schultz, FZJ, Juelich
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2009 HTAP Assessment
• Seek the reconciliation of models, observations, emissions
Model Outputs
Observations
EmissionsEmission Integration
Emission comparisons, reconciliation
Model Comparisons
Model-model comparison, ensemble,
Data Integration
Data homogenization and integration
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Iterative Air Pollution Analysis
• In the past, these activities were conducted separately; little mutual support
• Iterative linking would characterize the pollutants and create understanding
Models
Observations
Emissions Characterization
Understanding
Inverse Modeling
Emissions retrieval from observations;
Model Evaluation
Performance testing, improved formulation
Forward Modeling
Process-based simulation; source-receptor relationship
Reanalysis
Forward model with assimilated observations
Data Interpretation
Use of previous & tacit knowledge to explain data
Data Integration
Data homogenization and integration
Emission Integration
Emission comparisons, reconciliation
Model Comparisons
Model-model comparison, ensemble,
GOAL:
Knowledge Creation
Characterization of pattern; understating of processes
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Initial HTAP Data Flow NetworkLoosely Coupled Data Network of Autonomous Nodes
OGC WCS Data Access Protocol
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Tropospheric OMI NO2 Average
Model
Obs.
Emiss.
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OMI NO2 – Mobil Emissions
Model
Obs.
Emiss.
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Tropospheric OMI NO2 Average
Model
Obs.
Emiss.
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OMI NO2 – Point Emissions
Model
Obs.
Emiss.
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Single Point Sources
Power Plants
Model
Obs.
Emiss.
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Single Point Sources
Model
Obs.
Emiss.
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Georgia Sweetwater Fire
Model
Obs.
Emiss.
Sweat Water fire in S. Georgia (May 2007)
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Georgia Sweetwater Fire
Model
Obs.
Emiss.
Sweat Water fire in S. Georgia (May 2007)
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Friday/Sunday Ratio
Biomass Burning
Sunday
Smoke
Model
Obs.
Emiss.
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OMI/NEI Emission Ratios
• Ohio River OMI/Emiss = 1 • Northeast OMI/Emiss = 1.4
US NEI NOx Emission OMI Tropo NO2
Model
Obs.
Emiss.
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Total, Tropospheric, Upper
Total Column NO2
Stratospheric NO2
Tropo NO2
????
Model
Obs.
Emiss.
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Model Surface NO2
OMI NO2 Model
Obs.
Emiss.
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NOx Data on the NetworkEmission Aerosol Nitrate Ozone
Precipitation NitrateEmission
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Surface Obs.Model
Seasonal
Weekly
Secular
Diurnal
Model
Obs.
Emiss.
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Exceptional Event Analysis
Goal:Understand, quantify AQ impact of
Events
Approach:• Community, collaboration• ‘Harvesting’, aggregating
resources in a wiki workspace
• Communal and individual analyses
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October 2007 Southern California Fires
Dust
Dust
Smoke Santa Ana Winds
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Southern California Fires
• The hi-res OMI data provides columnar NO2 and Aerosol Index• The difference of their spatial pattern indicates smoke age(??)
OMI/TOMS - Absorbing Aerosol Index
OMI/TOMS – Tropospheric NO2
Oct 21, 2007 Oct 22, 2007 Oct 23, 2007 Oct 24, 2007
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• Consoles are multi-view panels of space-time synchronized data views• On Oct 21, note the burst of smoke, dust between 11 AM and 1:30PM
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• By Oct 25, the smoke has drifted inland, toward N-NE• The NAAPS model forecasted the smoke, the other models did not
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Thank You
Models
Observations
Emissions
Looking forward networking with you
• Collecting EPA - Relevant Data
• Performing Iterative Analyses
• Characterize and ‘Understand’ NO2