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Adjusting Numerical Model Data in Real Time : AQMOS

Adjusting Numerical Model Data in Real Time : AQMOS. Prepared by Clinton P. MacDonald, Dianne S. Miller, Timothy S. Dye, Kenneth J. Craig, Daniel M. Alrick Sonoma Technology, Inc. Petaluma, CA Presented at the 2010 National Air Quality Conferences Raleigh, NC March 15-18, 2010. 3805.

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Adjusting Numerical Model Data in Real Time : AQMOS

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  1. Adjusting Numerical Model Data in Real Time: AQMOS Prepared by Clinton P. MacDonald, Dianne S. Miller, Timothy S. Dye, Kenneth J. Craig, Daniel M. Alrick Sonoma Technology, Inc. Petaluma, CA Presented at the 2010 National Air Quality Conferences Raleigh, NC March 15-18, 2010 3805

  2. Introduction What: The Air Quality Model Output Statistics (AQMOS) forecasting tool provides air quality model predictions adjusted in real time. Why: Gridded numerical weather and air quality models have some errors in their predictions. How: Like model output statistics for weather models, AQMOS uses regression equations to correct for air quality model errors. • AQMOS is • Automated • Dynamically updated after each model run • City-, pollutant-, and model-specific

  3. + How AQMOS Works • AQMOS calculates regression equations from recent air quality observations and numerical model predictions (the critical component). • The forecasting tool applies these equations to the current numerical model predictions. Many days of observed and predicted data AQMOS prediction = numerical model prediction x correlation factor + constant

  4. AQMOS Data Sources The system’s flexibility allows new models and parameters to be added as data become available. • Air quality observations from AIRNow Gateway • Peak daily 8-hr ozone • Daily 24-hr PM2.5 • Model data • NOAA Air Quality Forecast Guidance (6Z and 12Z) • BlueSky Gateway Experimental CMAQ (0Z)

  5. Acquire Data Hourly NOAA grib data provides peak next-day 8-hr ozone forecast. Store daily predictions for each area and model run in AQMOS database. Extract the concentration at the geographic centroid of each ZIP Code and calculate the maximum concentration in the area (in this example, 53 ppb for Des Moines). AQMOS AIRNow Gateway files provide peak 8-hr ozone concentrations for each forecast area. AIRNowGatewayData File Store daily peak concentration data in AQMOS database.

  6. Calculate Regression • Match recent forecasts (within the last year) with observations • Calculate regression for each city using up to six months of data High Top 15% threshold (88 ppb for Dallas) Low Observation = recent numerical model prediction x correlation factor + constant

  7. current model prediction AQMOS prediction x slope + constant = AQMOS Website (1 of 2) Original NOAA forecast in AQI 62.8 ppb = 89.0 ppb x 0.73 + (-2.36 ppb) NOAA prediction: 89.0 ppb AQMOS prediction: 62.8 ppb Verification: 60.0 ppb

  8. AQMOS Website (2 of 2)

  9. >-4 and ≤-2 >-2 and ≤0 >0 and ≤2 >2 and ≤4 >4 and ≤6 >6 and ≤8 >8 and ≤10 >10 and ≤12 >12 and ≤14 >14 and ≤16 >16 and ≤18 AQMOS Performance (1 of 6) NOAA 6Z Next-Day OzoneApril 1-October 31, 2009 AQMOS Improvement (ppb) Improvement = avg (abs (raw model – observed)) – avg (abs (AQMOS – observed))

  10. AQMOS Performance (2 of 6) BlueSky Gateway 0Z Next-Day PM2.5April 1-October 31, 2009 AQMOS Improvement (µg/m3) >-2 and ≤0 >0 and ≤2 >2 and ≤4 >4 and ≤6 >6 and ≤8 Improvement = avg (abs (raw model – observed)) – avg (abs (AQMOS – observed))

  11. AQMOS Performance (3 of 6) Between April 1-October 31, 2009, AQMOS improved predictions in • 312 of 326 forecast cities for the next-day NOAA 6Z ozone model • 239 of 252 forecast cities for the next-day BlueSky Gateway PM2.5 model Improvement = avg (abs (raw model – observed)) – avg (abs (AQMOS – observed))

  12. AQMOS Performance (4 of 6) Days on which either observed or model-predicted air quality was USG or higher • Green – forecast improved on at least 75% of these days • Yellow– forecast improved on 50-75% of these days • Red – forecast improved on fewer than 50% of these days

  13. AQMOS Performance (5 of 6) Atlanta Next-Day 6Z NOAA Ozone ModelApril 1-October 31, 2009

  14. AQMOS Performance (6 of 6) Salt Lake City Next-Day 6Z NOAA Ozone ModelApril 1-October 31, 2009

  15. Summary • AQMOS is available to air quality agencies. • AQMOS should be used in conjunction with other guidance. • On most days, AQMOS shows improvement over the raw model output in predicting air quality for specific cities. • AQMOS showed improvement on at least 50% of critical days in the six cities evaluated. For more information, please visit http://aqmos.sonomatech.com or contact Dianne Miller dianne@sonomatech.com 707-665-9900

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