# Presentation: 900137

## The use of multiple numerical models to improve air quality predictions

**Carlos Cardelino**
- Georgia Institute of Technology
**Bill Murphey**
- Georgia Environmental Protection Division

## Atlanta Air Quality Prediction Program

**Since 1995**
- Daily prediction of next-day maximum 8-hour average O3 concentration  (May 1 to September 30)

**Since 2003**
- Daily prediction of next-day midnight 24-hour average PM2.5 concentration (All year around)

## Classification of Days

- COLOR

**Air Pollution**
**Alert**

- BINARY

**No Action Day**

- PPBV

- 125

- 85

- 105

- 65

## Atlanta Ozone Monitoring Stations

**Atlanta Ozone Monitoring Stations**

## Observations

- Percent

- AQI categories
- 2000 - 2004

**Observations**

## Based on historical data (1987 to 2003)

## Statistical Numerical ModelsPredictor Variables

- Ambient temperature
- Relative humidity
- Cloud cover
- Wind speed
- Wind direction
- Atmospheric pressure

- Previous day’s peak pollutant concentration
- Day of the week
- Length of day       (Julian day)

**Meteorology**

**Chemistry**

- Daily average, maximum, minimum, 7-11 AM, 1-5 PM

## Multiple Linear Regression Model

- Ozone O3 and Temperature T

## Multiple Linear Regression Model

- General Form :

- where

- A0 = intercept with the O3-axis
- Ak = regression coefficients
- O3 = forecast next-day peak ozone concentration
- Xk = next-day forecasted dependent variables

## Close-Neighbor Model

**Close-Neighbor Model**

- Historical database point

## Close Neighbor Model

**Close Neighbor Model**

- Distance d

- N = number of variables
- Wk = normalized weight, it is proportional to the correlation coefficient of variable X with O3

## Decision Tree ModelSelection of days

## Decision Tree ModelBinary Tree

- Yellow   4
- Orange  5
- Red       2

- Yellow 4
- Orange 2

- Orange 3
- Red      2

- Orange 2

- Yellow 4

- Orange 3

- Red 2

**OZONE**

**CLOUD COVER**

**WIND SPEED**

**TEMPERATURE**

## Comparison of Models

**Comparison of Models**

**Percent of Successful Predictions**
**From 2000 to 2004**

## Predicted and Observed Data

- Predicted Data (**P**) :
- All variables are predicted

- Mixed Data (**M**) :
- Observed previous-day ozone

- Real Data (**R**) :
- All variables are observed

## Can we do better?

**Phillip D. Thompson**
**Monthly Weather Review, 1977**
- “The purpose of this note is to draw attention to a fact that does not appear to be widely recognized or accepted, but which was probably known to Gauss in 1802. This is the incontrovertible fact that two or more inaccurate but independent predictions of the same future event may be combined in a very specific way to yield predictions that are, on the average, more accurate that either or any of them individually”

**Combination of Models**
**A Probabilistic Approach**

## Combination of models

- Percent of Successful Predictions

- “Best” Model                           86.7          61.8

## Probabilistic approach

**Selection by majority of models or**
**                 ****by majority of selected days**

## Yearly comparisons

**Yearly comparisons**

## Another application

- Days in which all models (or most of them) performed quite poorly with BOTH sets of data: predicted and observed.

**Meso-scale and meso-synoptic features that are not captured by average meteorological surface data**
**For example: back-door cold fronts, shallow short waves, low-level jets, dying fronts, afternoon convection**

## Conclusions