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Improving Cost Effective Air Quality Forecasting UPDATE 9/25/2008:

Improving Cost Effective Air Quality Forecasting UPDATE 9/25/2008:. Revised NAQFS Analysis Addition of 2007 NAQFS data Prelim. Background research 2007 Model Evaluation MD/VA Free Ride Program numbers. Revised NAQFS Analysis. Update Yorks, 2007 with 2007 NAQFS 1hr and 8hr forecasts

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Improving Cost Effective Air Quality Forecasting UPDATE 9/25/2008:

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  1. Improving Cost Effective Air Quality ForecastingUPDATE 9/25/2008: Revised NAQFS Analysis Addition of 2007 NAQFS data Prelim. Background research 2007 Model Evaluation MD/VA Free Ride Program numbers

  2. Revised NAQFS Analysis • Update Yorks, 2007 with 2007 NAQFS • 1hr and 8hr forecasts • 2007 forecast data inconsistent with 04-06 • Reviewed methods • 8hr average forecast values used as 1hr! • 8hr averages  1hr average • 8hr averaged 8hr averages  8hr average • 04-06 maximum forecasts appeared low

  3. Original NAQFS Analysis (Yorks, 2007)

  4. Updated NAQFS Analysis

  5. Original NAQFS Analysis (Yorks, 2007)

  6. Updated NAQFS Analysis

  7. Original NAQFS Analysis (Yorks, 2007)

  8. Updated NAQFS Analysis

  9. Original NAQFS Analysis (Yorks, 2007)

  10. Updated NAQFS Analysis

  11. NAQFS Model Evaluation (Eder, 2008?) • Model Updates • WRF-NMM  Meteorology (NCEP) • CMAQ  Air Quality (EPA) • CONUS Domain • Surface layer roughness length parameters under stable conditions • Effects on AQ under investigation • Canopy uptake (plant respiration) • NOx and CO • Discrete Evaluation • RMSE, N/MB, N/ME, r • Categorical Evaluation • A, B, FAR, CSI, WSI  (Kang, 2007)

  12. NAQFS Model Evaluation • Discrete Evaluation (Eder, 2006)

  13. NAQFS Model Evaluation • Categorical Evaluation • Ozone threshold exceeded? • Was it forecasted to exceed? Forecast Threshold • a  MISS HIGH • b  HIT HIGH • c  HIT LOW • d  MISS LOW

  14. NAQFS Model Evaluation • Categorical Evaluation (Eder, 2006) • A Accuracy (%) • B  Bias (<1, underpredict; >1 overpredict) • CSI  Critical Success Index (%) • FAR  False Alarm Rate (%) • WSI  Weighted Severity Index (%) (Kang, 2007)

  15. NAQFS Model Evaluation (Eder, 2008?) • High Accuracy misleading! Large number of correctly forecasted non-exceedence days • High FAR, Low CSI, and ~40% increase on WSI over CSI  Lots of mis-forecasted days, all very close to threshold • Bias >1  Forecast generally higher than observed

  16. NAQFS Model Evaluation (Eder, 2008?) • Low correlation days associated with cloud cover days

  17. NAQFS Model Evaluation (Eder, 2008?)

  18. MD/VA Free Ride Code Red Program • Free bus rides on forecasted “AQI Red” days • Attempt to reduce emission of ozone precursors • Promotes awareness of air quality issues • Educates public on steps to improve their air • Less health problems

  19. MD/VA Free Ride Code Red Program • Northern VA • Funded 100% by Congestion Mitigation and Air Quality Program (CMAQ) • 06  2 days, 07  1 day, 08 (so far)  2 days • Cost to the 8 transit providers… • $120,000 per day • Count of riders taken by the drivers • Rider count up 4% on code red days • ~ 54,000 vehicle trips reduced • NVTC estimates numbers are more like 7-8%

  20. MD/VA Free Ride Code Red Program • Maryland • DC does NOT take part in this program • MDOT funds 35-40%, local jurisdiction remainder • 06  2 days, 07  1 day, 08 (so far)  2 days • Cost to MDOT… • $35,000 - $40,000 per day • Count taken again by drivers • Rider count up 5-7% on code red days • ~ 4,750 – 6,650 boardings

  21. MD/VA Free Ride Code Red Program Effectiveness • 0.0752 tons of NOx reduced per day • 0.026 tons of VOC reduced per day • Cost per ton of NOx and VOC reduction • $1,003,819 • Not too bad…more effective Transportation Emission Reduction Measure (TERM) • RT-Bus schedule and info  $34,074 • Neighborhood Circulator Busses  $129,576 • 1000 more parking spaces @ commuter stations  $430,602 • Less effective TERM • Free off-peak bus service  $1,408,268 • Free Bus/Rail Rail/Bus X-fer  $1,994,610

  22. Where to from here? • Investigate why models over-predict • How can we incorporate said findings into the model? • Use AirNow/CASTNet/other AQ data sources to help verify forecasts • Determine data needed • Temporal range • Locations to look at • Types of cost data • Determine the best method of evaluating the value of an AQ forecast

  23. References • Desimone, J., 2008; Memorandum: Free Ride on Code Red Program. Provided by Joan Rholfs, MWCOG. • Eder, B., D. Kang, R. Mathur, S. Yu, K. Schere, 2006. An Operational Evaluation of the Eta-CMAQ Air Quality Forecast Model. Atmospheric Environment40, 4894 – 4905 • Eder, B., D. Kang, R. Mathur, J. Pleim, S. Yu, 2008?. An Evaluation of the National Air Quality Forecast Capability for the Summer of 2007. • Kang, D., R. Mathur, K. Schere, S. Yu, B. Eder, 2007. New Categorical Metrics for Air Quality Model Evaluation. Journal of Applied Meteorology. 46, 549-55

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