# Presentation: 939238

## 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

## 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

- Forecasting tools and process
- Daily operations
- Meteorological and air quality processes
- Similarities between regions
- Local issues and processes
- Verification
- All programs
- Tool comparison
- Observations

- Outline

## 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

## Overview of Programs

- Issued forecasts for current- and next-day’s 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

## 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

## Forecasting Tools

- Qualitative and quantitative tools that aid forecasters in predicting air quality

| City | Tools |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | Criteria | Regression | CART | Photochemical Model | Conceptual Experience |
| Sacramento, CA |  |  |  | 2 |  |
| Columbus, OH |  | 1 |  |  |  |
| Minneapolis, MN |  |  |  |  |  |
| Nashville, TN |  | 1 |  |  |  |
| Memphis, TN |  |  |  |  |  |

- 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.

## 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

## 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 <65%
At least Moderate AQI 8-hr ozone levels upstream on previous day
500-mb ridge axis east of Minneapolis

- Moderate (>6 m/s) southerly winds overnight
- Maximum temperature >80oF
- Average daytime (9 a.m. to 6 p.m.) relative humidity <65%
- At least Moderate AQI 8-hr ozone levels upstream on previous day
- 500-mb ridge axis east of Minneapolis

- Forecasting Tool - Criteria

## Forecasting Tool - CART

- Sacramento, CA
- A decision tree was developed in 1999
- Easy to use with decent accuracy

## Forecasting Process

- Used a variety of tools to reach a final forecast

## 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

## 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

## 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

## 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

- 	“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

- Local Processes

## Local Processes

- Memphis, TN
- Monitoring network is limited
- “Memphis” peak can change with slight wind direction shift

- 8-hour maximum of 0.114 ppm at Frayser

- 8-hour maximum of .091 ppm at Crittenden and 0.70 ppm at Frayser

## 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 NOAA’s Hysplit trajectory model.

## Verification - All Programs

- Verify next-day (24-hr) forecast
- Compare observed and forecasted categories
- Focus on simple performance measures
- Evaluate two-category forecast:
- Good–Moderate
- Unhealthy

## 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.

## Verification - All Programs

- Verification results for all cities

|  | Sacramento | Nashville | Memphis | Columbus | Minneapolis |
| --- | --- | --- | --- | --- | --- |
| Percent Correct | 82 | 91 | 87 | 90 | 97 |
| False Alarm Rate | 38 | 67 | 67 | 62 | 75 |
| Probability of Detection | 72 | 75 | 33 | 45 | 33 |
| Number of exceedences | 53 | 8 | 15 | 8 | 3 |

## 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

## Verification – Tool Comparison

## Compute verification metrics
Evaluate ability to predict Unhealthy AQI

- Evaluate ability to predict Unhealthy AQI

- Verification – Tool Comparison

## 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!

## 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