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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 - PowerPoint PPT Presentation
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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 involvingJohn E. White, Philip Dickerson, Shobha Kondragunta, and Tim Dye
Fall AGUSan Francisco
3 Dec 2012
More than 36 Million Americans (~40% of area) not Covered by Monitoring Networks of Fine Aerosol (PM2.5)
AirNow Operational Map(airnow.gov)
Without satellite data, no contouring is possible in the hatched areas
“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”
Quotes
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
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
Estimated PM2.5 = η· AOD
GEOS-ChemChemical Transport Model
vertical structure ▪aerosol properties▪
meteorological effects ▪
4
MISR- Multi-angle- 4 bands- 6-9 day
coverage
MODIS- Single
viewpoint- 36 bands- daily coverage
Significant Long-term Mean Agreement of Satellite-Derived PM2.5 with In Situ Measurements
SatelliteDerived
In-situ
Sat
ellit
e-D
eriv
ed [μ
g/m
3]
In-situ PM2.5 [μg/m3]
Ann
ual M
ean
PM
2.5 [
μg/m
3 ] (2
001-
2006
)
r
MODIS AOD 0.39
MISR AOD 0.39
Combined AOD 0.61
Combined PM2.5 0.77
van Donkelaar et al., EHP, 2010
But Original Estimates Have Limited Daily Skill
• 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)
Daily Error in Original (2004, 2006, 2008)
van Donkelaar et al., ES&T, 2012
Original for specific day (Jun 27, 2005)
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
• Reduces mean daily error by ~10%
Bias Correction Improves Daily Accuracy
Error in Original (2004, 2006, 2008)
Error in Bias-corrected (2004, 2006, 2008)
Used PM2.5 monitors from training dataset to identify seasonal bias
van Donkelaar et al., ES&T, 2012
Bias Corrected for Jun 27, 2005
• Regional mean daily error drops another 10% (41.9%)
Spatial Smoothing Improves Skill FurtherSmoothed Ratio vs Climatology
Error in Original (2004, 2006, 2008)
Error in Smoothed & Bias-corrected (2004, 2006, 2008)
van Donkelaar et al., ES&T, 2012
Bias Corrected & Smoothed, Jun 27, 2005
Near-real-time MISR AOD Would Improve Coverage in Southwest
MODIS&MISR
MODIS
van Donkelaar et al., ES&T, 2012
Mean Number of Annual Observations
Insignificant Change in Error (46.7% 46.4%)
No Penalty from Using AOD/PM2.5 from Different YearsEnables Offline Calculation of AOD/PM2.5
Daily AOD/PM2.5 for specific year Climatological daily AOD/PM2.5 from training dataset (2005, 2007, 2009)
Error vs validation dataset (2004, 2006, 2008)
van Donkelaar et al., ES&T, 2012
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
Implementation and Outreach
• 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.
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
Conclusions
• 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
Next steps in next talk by Szykman et al.