Continued Air Quality Forecast Support in Maryland using Ensemble Statistical

ModelsGregory Garner – Penn StateDr. Anne Thompson – Adviser

Air Quality Applied Sciences Team3rd Meeting

13 June 2012

2

BackgroundMid-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

3

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) Adapted and updated from Yorks et al., 2009.b) Adapted from Tang et al., 2009.

4

The Problem…

• Deterministic forecasts do not convey uncertainty.• Responsibility to decision makers?• Weather model ensembles…AQ model ensembles?

Short Range Ensemble Forecast (SREF) – Total PrecipitationMadison / Dane Regional Airport (KMSN)

5

The Problem…• Standard statistical approaches

fall short in forecasting high-ozone events.

• Need method for dealing with non-normally distributed response.

6

Proposed Solution• Build a statistical model…

Tree

Leaf

Node Node

Leaf

Node Node

Breiman, 1984; Torgo and Ribiero, 2003

recallprecision

recallprecisionmeasureF

2

2 )1(

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― 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

7

Data

Weather Datahttp://www.srh.noaa.gov/jetstream/

synoptic/wxmaps.htm

Ozone Datahttp://www.weather.gov/aq

Chronological Datahttp://lukas85.tumblr.com/post/18550727456/lousy-

smarch-weather

Tree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

http://school.discoveryeducation.com/clipart/clip/cowboys2.html

8

Statistical Model2011 Ozone Season (DISCOVER-AQ)

+ =

Edgewood, MDTree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

9

Statistical Model

+ =

Tree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

Tree

Leaf

Node Node

Leaf

Node Node

Edgewood, MD

2012 Ozone Season

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Web-Based Interface• Deliver product to regional air quality forecasters

http://www.meteo.psu.edu/~ggg121/aq

12

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

13

Acknowledgements & References

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-09-001, 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.

• 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)

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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