OMI NO 2 for AQ Management Applications Rudolf Husar, CAPITA, Washington University NO2 Workshop, EPA HQ, Oct 30, 2007 Regulatory Support: Exceptional.

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Presentation transcript:

OMI NO 2 for AQ Management Applications Rudolf Husar, CAPITA, Washington University NO2 Workshop, EPA HQ, Oct 30, 2007 Regulatory Support: Exceptional Event Quantification Policy Development: Hemispherical Pollutant Transport

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

2009 HTAP Assessment Seek the reconciliation of models, observations, emissions Model Outputs Observations Emissions Emission Integration Emission comparisons, reconciliation Model Comparisons Model-model comparison, ensemble, Data Integration Data homogenization and integration

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

Initial HTAP Data Flow Network Loosely Coupled Data Network of Autonomous Nodes OGC WCS Data Access Protocol

Tropospheric OMI NO2 Average Model Obs. Emiss.

OMI NO2 – Mobil Emissions Model Obs. Emiss.

Tropospheric OMI NO2 Average Model Obs. Emiss.

OMI NO2 – Point Emissions Model Obs. Emiss.

Single Point Sources Power Plants Model Obs. Emiss.

Single Point Sources Model Obs. Emiss.

Georgia Sweetwater Fire Model Obs. Emiss. Sweat Water fire in S. Georgia (May 2007)

Georgia Sweetwater Fire Model Obs. Emiss. Sweat Water fire in S. Georgia (May 2007)

Friday/Sunday Ratio Biomass Burning Sunday Smoke Model Obs. Emiss.

OMI/NEI Emission Ratios Ohio River OMI/Emiss = 1 Northeast OMI/Emiss = 1.4 US NEI NOx EmissionOMI Tropo NO2 Model Obs. Emiss.

Total, Tropospheric, Upper Total Column NO2 Stratospheric NO2 Tropo NO2 ???? Model Obs. Emiss.

Model Surface NO2 OMI NO2 Model Obs. Emiss.

NOx Data on the Network EmissionAerosol NitrateOzone Precipitation Nitrate Emission

Surface Obs. Model Seasonal Weekly Secular Diurnal Model Obs. Emiss.

Exceptional Event Analysis Goal: Understand, quantify AQ impact of Events Approach: Community, collaboration ‘Harvesting’, aggregating resources in a wiki workspace Communal and individual analyses

October 2007 Southern California Fires Dust Smoke Santa Ana Winds

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, 2007Oct 22, 2007Oct 23, 2007Oct 24, 2007

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

By Oct 25, the smoke has drifted inland, toward N-NE The NAAPS model forecasted the smoke, the other models did not

Thank You Models Observations Emissions Looking forward networking with you Collecting EPA - Relevant Data Performing Iterative Analyses Characterize and ‘Understand’ NO2