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Fall AGU San Francisco 3 Dec 2012

Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates for North America. Aaron van Donkelaar , Randall V. Martin, Adam N. Pasch, James J. Szykman , Lin Zhang, Yuxuan Wang and Dan Chen As part of a broader project involving

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Fall AGU San Francisco 3 Dec 2012

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  1. Improving the Accuracy of Daily Satellite-Derived Ground-Level Fine Aerosol Concentration Estimates for North America • Aaron van Donkelaar, Randall V. Martin, Adam N. Pasch, James J. Szykman, Lin Zhang, Yuxuan Wang and Dan Chen • As part of a broader project involving • John E. White, Philip Dickerson, ShobhaKondragunta, and Tim Dye Fall AGU San Francisco 3 Dec 2012

  2. More than 36 Million Americans (~40% of area) not Covered by Monitoring Networks of Fine Aerosol (PM2.5) Without satellite data, no contouring is possible in the hatched areas AirNow Operational Map (airnow.gov) Quotes “I am sick with an environmental illness and have to check the air quality every day. The only site in Mississippi is the Gulf Coast. Will any other areas in North Mississippi be added in the future?” “HELP! I don’t live in a city, & your site does not help me know air quality except in cities. I have COPD & am beginning to suffer. I need to know what to expect here – not 60 or 70 miles West”

  3. Develop Satellite Remote Sensing for Use by AirNow AirNow is the national framework for acquiring and distributing air quality information • Collects, quality assures, and transfers real-time and forecasted air quality information to the public • Gathers data provided by 130 federal, state, and local air quality agencies • Issues weather/air quality news stories • Partners with national media and other agencies • Provides air quality education and outreach America’s “go to” resource for current and forecasted air quality information

  4. Begin by Inferring PM2.5 from Satellite Aerosol Optical Depth (AOD) and Simulated η (PM2.5/AOD) Excluded regions with biased AOD (>0.1 or 20%) as identified with AERONET • MISR • Multi-angle • 4 bands • 6-9 day coverage Estimated PM2.5 = η· AOD GEOS-Chem Chemical Transport Model vertical structure ▪ aerosol properties▪ meteorological effects ▪ • MODIS • Single viewpoint • 36 bands • daily coverage 4

  5. Significant Long-term Mean Agreement of Satellite-Derived PM2.5 with In Situ Measurements Annual Mean PM2.5 [μg/m3] (2001-2006) Satellite Derived Satellite-Derived [μg/m3] In-situ In-situ PM2.5 [μg/m3] van Donkelaar et al., EHP, 2010

  6. But Original Estimates Have Limited Daily Skill Original for specific day (Jun 27, 2005) Daily Error in Original (2004, 2006, 2008) Large daily error (67%) Driven by bias and noise in AOD & AOD/PM2.5 Practical means to address? Also included additional filters from Hyer et al. (2010) van Donkelaar et al., ES&T, 2012

  7. Climatological Bias Correction Informed by In Situ Observations from Training Dataset (2005, 2007, 2009) Regress 90-day running comparisons for 2005, 2007, and 2009 to identify bias Make correction surface using spatial interpolation of average

  8. Bias Correction Improves Daily Accuracy Used PM2.5 monitors from training dataset to identify seasonal bias Bias Corrected for Jun 27, 2005 Error in Original (2004, 2006, 2008) Error in Bias-corrected (2004, 2006, 2008) Reduces mean daily error by ~10% van Donkelaar et al., ES&T, 2012

  9. Spatial Smoothing Improves Skill FurtherSmoothed Ratio vs Climatology Bias Corrected & Smoothed, Jun 27, 2005 Error in Original (2004, 2006, 2008) Error in Smoothed & Bias-corrected (2004, 2006, 2008) Regional mean daily error drops another 10% (41.9%) van Donkelaar et al., ES&T, 2012

  10. Near-real-time MISR AOD Would Improve Coverage in Southwest MODIS&MISR Mean Number of Annual Observations MODIS Insignificant Change in Error (46.7%  46.4%) van Donkelaar et al., ES&T, 2012

  11. No Penalty from Using AOD/PM2.5 from Different YearsEnables Offline Calculation of AOD/PM2.5 Climatological daily AOD/PM2.5 from training dataset (2005, 2007, 2009) Daily AOD/PM2.5 for specific year Error vs validation dataset (2004, 2006, 2008) van Donkelaar et al., ES&T, 2012

  12. Similar Performance for Extreme EventsUsing Offline Calculation of AOD/PM2.5 Error vs validation dataset (2004, 2006, 2008) during extreme events (90th percentile) van Donkelaar et al., ES&T, 2012

  13. Implementation and Outreach Relevant Talk: White et al., GC13D-04 – EPA AirNow Satellite Data Processor (ASDP) for improved AQI Relevant Posters: Pasch et al., A21C-0065 – Performance of the AirNow Satellite Data Processor NOAA NESDIS receiving MODIS AOD & calculating AOD/PM2.5 Data feed  Sonoma Tech  Data fusion  AirNow Created videos to describe technique Created a committee to test, evaluate, and share findings using regional case studies.

  14. Conclusions Next steps in next talk by Szykmanet al. Practical near-real-time technique to infer PM2.5 from satellite AOD Bias correction and smoothing significantly reduce daily error Would benefit from further improvements to AOD retrieval and AOD/PM2.5 calculation Collocated AOD/PM2.5 measurements would provide valuable information to evaluate simulation Acknowledgements: NASA, EPA, NOAA, Environment Canada

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