1 / 13

Continued Air Quality Forecast Support in Maryland using Ensemble Statistical Models

Continued Air Quality Forecast Support in Maryland using Ensemble Statistical Models. Gregory Garner – Penn State Dr. Anne Thompson – Adviser Air Quality Applied Sciences Team 3 rd Meeting 13 June 2012. Background. Mid-Atlantic Region 13 th most ozone-polluted metropolitan area in US

kapono
Download Presentation

Continued Air Quality Forecast Support in Maryland using Ensemble Statistical Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

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

  4. 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?

  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 • 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 Build a statistical model… Breiman, 1984; Torgo and Ribiero, 2003

  7. Data Ozone Data http://www.weather.gov/aq Weather Data http://www.srh.noaa.gov/jetstream/synoptic/wxmaps.htm Chronological Data http://lukas85.tumblr.com/post/18550727456/lousy-smarch-weather http://school.discoveryeducation.com/clipart/clip/cowboys2.html

  8. Statistical Model Edgewood, MD + = 2011 Ozone Season (DISCOVER-AQ)

  9. Statistical Model Edgewood, MD + = 2012 Ozone Season

  10. Web-Based Interface http://www.meteo.psu.edu/~ggg121/aq Deliver product to regional air quality forecasters

  11. 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

  12. 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-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.

  13. EPA Disclaimer This presentation was developed with support from STAR Fellowship Assistance Agreement no. FP–91729901–0awarded 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

More Related