Continued Air Quality Forecast
Support in Maryland using Ensemble
Statistical Models
Gregory Garner – Penn State
Dr. Anne Thompson – Adviser
Air Quality Applied Sciences Team
3rd Meeting
13 June 2012
Background
Mid-Atlantic Region
– 13th most ozone-polluted
metropolitan area in US
– American Lung Association
average ozone pollution grade: F
– 8.5+ million people
State of the Air, 2012; Mintz, 2009
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The Problem…
• Operational numerical model over-predicts in urban areas.
• Forecast orange…observe yellow  Decisions?
• Numerical model is valuable for certain decisions, but there is
room for improvement (Garner and Thompson, 2012).
a)
b)
Adapted and updated from Yorks et al., 2009.
Adapted from Tang et al., 2009.
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The Problem…
Short Range Ensemble Forecast (SREF) – Total Precipitation
Madison / Dane Regional Airport (KMSN)
• Deterministic forecasts do not convey uncertainty.
• Responsibility to decision makers?
• Weather model ensembles…AQ model ensembles?
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The Problem…
• Standard statistical approaches
fall short in forecasting highozone events.
• Need method for dealing with
non-normally distributed
response.
5
Proposed Solution
• Build a statistical model…
Tree
Leaf
Node
(  2  1)  precision  recall
F measure 
 2  precision  recall
Leaf
Node
Node
Node
yˆ i   0  1 x1,i   2 x2,i     j x j ,i
― Accounts for small-scale weather phenomena currently
unresolved by operational numerical models
― Runs quickly and efficiently to enable ensemble
(probabilistic) predictions
― Caters to “extreme value” predictions
Breiman, 1984; Torgo and Ribiero, 2003
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Data
Ozone Data
Weather Data
Chronological Data
http://www.weather.gov/aq
http://www.srh.noaa.gov/jetstream/synoptic
/wxmaps.htm
http://lukas85.tumblr.com/post/18550727456/lousysmarch-weather
http://school.discoveryeducation.com/clipart/clip/cowboys2.html
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Statistical Model
2011 Ozone Season (DISCOVER-AQ)
Edgewood, MD
+
=
8
Statistical Model
2012 Ozone Season
Edgewood, MD
+
=
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Web-Based Interface
• Deliver product to regional air quality forecasters
http://www.meteo.psu.edu/~ggg121/aq
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Summary
• AQ Modeling efforts are difficult in the Mid-Atlantic
– Urban centers and complex coastal environments
– Susceptible to small-scale meteorological phenomena
– Statistical distribution of ozone is not normal
• Statistical Model Development
– Bootstrap-aggregation of regression trees
– F-measure for splits and node evaluation
– Operational forecasts with SREF
• Web-based Model Interface
– www.meteo.psu.edu/~ggg121/aq
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Acknowledgements & References
• NASA AQAST (Daniel Jacob, Harvard Univ.), DISCOVER-AQ
(Jim Crawford, NASA Langley; Ken Pickering, NASA GSFC)
• Laura Landry (MDE), Dan Salkovitz (VA-DEQ), Bill Ryan (PSU),
Sunil Kumar (MWCOG)
• EPA – STAR Fellowship Program (FP–91729901–0)
• Gator Research Group (PSU – Meteo)
Breiman, L., 1984: Classification and regression trees. Wadsworth statistics/probability series, Wadsworth International Group.
Efron, B. and R. J. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman and Hall, 436pp.
Garner, G. G., A. M. Thompson, 2012: The value of air quality forecasting in the mid-atlantic region. Wea. Climate Soc., 4, 69–79. doi:
10.1175/WCAS-D-10-05010.1
Johnson, D. L. et al., 1997: Meanings of environmental terms. J. Environ. Quality, 26, 581-89.
Mintz, D., 2009: Technical assistance document for the reporting of daily air quality: The Air Quality Index (AQI). Tech. Rep. EPA-454/B-09001, Environmental Protection Agency, 31 pp.
State of the air 2012. Tech. rep., American Lung Association, http://www.stateoftheair.org/2012/city-rankings/most-polluted-cities.html
Torgo, L. and R. Ribeiro, 2003: Predicting outliers. Knowledge Discovery in Databases: PKDD 2003, N. Lavrac, D. Gamberger, L. Todorovski,
and H. Blockeel, Eds., Springer Berlin / Heidelberg, Lecture Notes in Computer Science, Vol. 2838, 447-458.
Tang, Y. et al., 2009: The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continential
United States. Environ. Fluid. Mech., 9, 43–58, doi: 10.1007/s10652-008-9092-5
Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3d ed., Elsevier, 676 pp.
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EPA Disclaimer
This presentation was developed with
support from STAR Fellowship Assistance
Agreement no. FP–91729901–0 awarded by the
U.S. Environmental Protection Agency
(EPA). It has not been formally reviewed
by EPA. The views expressed in this
presentation are solely those of Gregory
Garner, and the EPA does not endorse any
products or commercial services mentioned
in this presentation.
The Simpsons Movie
http://www.behindthevoiceactors.com/_img/chars/char_45470.jpg
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