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Developing and Operating Air Quality Forecasting Programs for Five Cities

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Sacramento, CA. A decision tree was developed in 1999. Easy to use with decent accuracy ... Sacramento Metropolitan Air Quality Management District ... – PowerPoint PPT presentation

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Title: Developing and Operating Air Quality Forecasting Programs for Five Cities


1
Developing and Operating Air Quality Forecasting
Programs for Five Cities
  • Prepared by
  • Clinton P. MacDonald
  • Charley A. Knoderer
  • Timothy S. Dye
  • Beverly S. Thompson
  • Dianne S. Miller
  • Craig B. Anderson
  • Sonoma Technology, Inc.
  • Petaluma, CA
  • EPA National Air Quality Conference
  • Forecasting and Public Outreach
  • February 4-6, 2002
  • San Francisco, CA

901201-2148
2
Outline
  • Overview of the programs
  • Forecasting tools and process
  • Daily operations
  • Meteorological and air quality processes
  • Similarities between regions
  • Local issues and processes
  • Verification
  • All programs
  • Tool comparison
  • Observations

3
Overview of Programs
  • Five cities - three sponsors
  • 1. Sacramento, CA
  • 6th year forecasting
  • 2. Columbus, OH
  • Nashville and Memphis, TN
  • 1st year forecasting
  • 3. Minneapolis, Minnesota
  • 1st year forecasting

4
Overview of Programs
  • Issued forecasts for current- and next-days
    regional 8-hr peak additional 5-day outlook in
    Sacramento
  • Forecasts issued by late morning/early afternoon
  • Operated 7 days per week
  • Season
  • May 1 October 31 for Sacramento
  • May 1 September 30 for other cities

5
Overview of Programs
  • Program activities
  • Collected and analyzed historical data to
    understand local air quality and meteorology
  • Developed forecasting tools
  • Developed infrastructure
  • Data acquisition
  • Forecasting procedures
  • Forecast dissemination methods
  • Interacted with clients and media
  • Verified and analyzed forecasts

6
Forecasting Tools
  • Qualitative and quantitative tools that aid
    forecasters in predicting air quality

1 Regression equations were developed and used
at the end of the ozone season. 2 Photochemical
models were developed and used at the end of the
ozone season.
7
Forecasting Tool - Criteria
  • Minneapolis Why Criteria?
  • Difficult to develop accurate objective methods
    for this region
  • Regression equations had poor correlations
  • Best r2 was 0.57
  • r2 of at least 0.65 is desired to have any
    confidence in the equation
  • Two causes for poor correlations and inability to
    predict high ozone concentrations
  • Equation does not capture ozone transport
  • Too few high ozone days in the developmental
    dataset to be reliable

8
Forecasting Tool - Criteria
  • Guidelines for ozone 80 ppb Transport Case
  • Moderate (6 m/s) southerly winds overnight
  • Maximum temperature 80ºF
  • Average daytime (9 a.m. to 6 p.m.) relative
    humidity
  • At least Moderate AQI 8-hr ozone levels upstream
    on previous day
  • 500-mb ridge axis east of Minneapolis

9
Forecasting Tool - CART
  • Sacramento, CA
  • A decision tree was developed in 1999
  • Easy to use with decent accuracy

10
Forecasting Process
  • Used a variety of tools to reach a final forecast

11
Daily Operations
  • Timeline of activities
  • Quick-cast to decide where to spend effort each
    day
  • Staffing to plan for simultaneous high ozone days
    in multiple regions
  • Disseminate forecast to districts through
    multiple methods to ensure delivery
  • Direct e-mail, fax, phone
  • Indirect SmogWatch and AIRNow web pages

12
Meteorological and Air Quality Processes -
Similarities
  • Ridge
  • Causes sinking motion
  • Inversion that traps pollutants and limits
    vertical mixing
  • Generally light surface winds
  • Warm temperatures
  • Mostly clear skies

13
Meteorological and Air Quality Processes - Local
Issues
  • Many large-scale similarities, but smaller scale
    meteorology and air quality must be understood to
    produce accurate forecasts
  • How large-scale weather influences local weather
  • Local carryover and transport
  • Local flow patterns
  • Importance of thunderstorms and clouds
  • Local emissions patterns
  • Monitor locations and characteristics
  • Event duration
  • Ramp-up time
  • Using several forecasting tools helps better
    capture the influence of these phenomena on air
    quality

14
Local Processes
  • Sacramento, CA
  • Hot spots of ozone due
  • to smoke from forest fires
  • Columbus, OH
  • Clouds and weak fronts are critical
  • Difficult to predict the exact position of these
    features 12 to 36 hrs in advance

15
Local Processes
  • Memphis, TN
  • Monitoring network is limited
  • Memphis peak can change with slight wind
    direction shift

8-hour maximum of .091 ppm at Crittenden and 0.70
ppm at Frayser
8-hour maximum of 0.114 ppm at Frayser
16
Local Processes
  • Minneapolis, MN Long-range transport is
    important

General 24-hour air parcel paths for a June 2001
ozone episode based on trajectories. Created
using NOAAs Hysplit trajectory model.
17
Verification - All Programs
  • Verify next-day (24-hr) forecast
  • Compare observed and forecasted categories
  • Focus on simple performance measures
  • Evaluate two-category forecast
  • GoodModerate
  • Unhealthy

18
Verification - All Programs
  • Compute performance measures
  • Percent correct (PC) - Percent of forecasts that
    correctly predicted the Good-Moderate or
    Unhealthy AQI categories
  • False alarm (FA) - Percent of times a forecast of
    Unhealthy did not actually occur (crying wolf).
  • Probability of detection (POD) - Percent of
    actual Unhealthy days correctly predicted.

19
Verification - All Programs
  • Verification results for all cities

20
Verification Tool Comparison
  • Two ozone forecasts issued in Nashville
  • Objective using regression (TVA)
  • Subjective using conceptual/experience (STI)
  • Independently forecasted throughout summer 2001
  • Compared these two approaches to evaluate their
    strengths and limitations

21
Verification Tool Comparison
22
Verification Tool Comparison
  • Compute verification metrics
  • Evaluate ability to predict Unhealthy AQI

23
Observations
  • Regression underpredicts peaks (lower POD)
  • Conceptual/experience method overpredicts peaks
    (higher bias), but correctly predicts more peak
    days (higher POD)
  • Combined forecast (objective with subjective)
    beats both the human forecasts alone and
    regression alone!

24
Acknowledgments
  • Sponsors
  • U.S. Environmental Protection Agency
  • Sacramento Metropolitan Air Quality Management
    District
  • Lake Michigan Ozone Directors Consortium
  • Agencies
  • Minnesota Pollution Control Agency
  • The Mid-Ohio Regional Planning Commission
  • Ohio Environmental Protection Agency
  • Tennessee Department of Environment and
    Conservation
  • Metropolitan Nashville and Davidson County Health
    Department, Pollution Control Division
  • Memphis and Shelby County Health Department
  • AIRNow program staff
  • California Air Resources Board
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