A heads up on air quality

New NCAR technique slated to improve air pollution forecasts

Mar 11, 2015 - by Staff

March 11, 2015 | Air pollution in the United States costs thousands of lives and billions of dollars every year. But what if forecasters could issue detailed air quality forecasts days in advance?

Such forecasts may be coming. NCAR and its research partners recently received a $1.3 million grant from NASA to develop the capability to produce detailed 48-hour forecasts of ground-level ozone and fine particulate matter.

Forecasting air quality: photo of Los Angeles in summer smog
Summer smog obscures Los Angeles. NCAR scientists are working to create a system to produce more detailed air pollution forecasts. (Photo by Massimo Catarinella via Wikipedia Commons.)

This will provide an important advance to air quality forecasts, which are issued by the National Oceanic and Atmospheric Administration, or NOAA. The current forecasts provide just a single-value prediction. A forecast might state, for example, that concentrations of ozone (the chief ingredient in smog) are expected to be in the “moderate” category on the following day. But it will not specify how likely it is that ozone levels will actually turn out to be moderate.

The goal for the new, three-year project is to generate more detailed, probabilistic forecasts. Just as a weather forecast, for example, might warn of a 60 percent chance of rain in the afternoon, new air quality forecasts might warn of a 60 percent chance of high ozone levels during certain times of the day.

These improved forecasts offer the promise of significant benefits for society. High concentrations of ozone and fine particulates (with a diameter of 2.5 microns or less) cause respiratory and cardiovascular problems and even premature death, as well as costs associated with health care, missed work, and damage to crops and forests. Poor air quality in the United States causes as many as 60,000 premature deaths each year and costs $100–$150 billion per year, according to NOAA estimates.

More detailed forecasts would enable people to plan their outdoor activities for periods of better air quality.

“If the forecasts can reduce the impacts of these pollutants by even 1 percent, that would save 600 lives and more than $1 billion each year,” said NCAR scientist Luca Delle Monache, who is leading the project. “That would be more lives saved than are lost in an average year by all severe weather events combined, including tornadoes, hurricanes, and floods.”

Leveraging the past to predict the future

The new technology will use advanced computer models and atmospheric observations, including NASA satellite sensors. It will also incorporate a powerful technique that Delle Monache and his colleagues have developed over the past several years and successfully applied to fine-scale prediction of certain weather phenomena. The technique is being used to forecast the atmospheric conditions most conducive to generating solar and wind power, for example.

The technique, known as an analog ensemble, draws on a multitude of past computer model predictions and observations to create a database of situations that are comparable to the current one. It analyzes how well those past predictions performed. The information is then applied to the current situation. This results in more detailed and accurate predictions.

If all goes as planned, the research team will begin testing the new prediction technology next year. Air quality officials say they are looking forward to getting more detailed forecasts, including the first-ever estimates of probability within NOAA-issued air quality predictions.

“NCAR's new forecast system promises to expand the forecasting capabilities in Delaware and the continental United States,” said Ali Mirzakhalili, director of the Delaware Division of Air Quality. “This will improve the decision making process for protecting the public health.”


Writer/contact
David Hosansky

Collaborators
NCAR
NASA
NOAA
University of Colorado Boulder
University of Maryland

Funder
NASA

 

 

 

 

 

 

 

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