New Argonne algorithm increases accuracy of air-pollution predictions
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ARGONNE, Ill. (May 23, 2008) — When air-quality monitors and environmental
regulators inspect the pollution levels of certain cities, the difference of
one or two parts per million in the concentration of pollutants like ozone
and carbon monoxide can mean the difference between achieving a target and
having to implement additional costly provisions to get failing areas back
on track.
| Kotamarthi's research was funded by the Office of Biological
and Environmental Research in the U.S. Department of Energy's Office
of Science. For more than 50 years the Biological and Environmental
Research (BER) program has been advancing environmental and biomedical
knowledge that promotes national security through improved energy production,
development and use; international scientific leadership that underpins
our nation's technological advances; and research that improves the quality
of life for all Americans. BER supports these vital national missions
through competitive and peer-reviewed research at national laboratories,
universities, and private institutions. In addition, BER develops and
delivers the knowledge needed to support the President's National Energy
Plan. |
Because of the high stakes involved in meeting air-quality targets, scientists,
city officials and regulators all desire an effective and accurate way not
only to measure air quality but also to predict where pollution "hot spots" will
occur and plan for additional control strategies.
To assist in that effort, environmental scientist Rao Kotamarthi of the U.S.
Department of Energy's Argonne National Laboratory, in collaboration with
Alexis Zubrow, now at the University
of North Carolina, and Li Chen, now at
Bristol University,
U.K., developed a computer algorithm that quickly
and accurately assimilates observational data into climate models to generate
more reliable forecasts.
"By incorporating observation data into our models, we can refine our
predictions," Kotamarthi said. "Meteorologists have been doing it
for a while, but people in the chemical trace gas and aerosol modeling community
have just started doing it."
When scientists include measurement data in their models, the uncertainties
in those measurements compound the uncertainties already present in the model.
Compensating for these new uncertainties requires a mathematically rigorous
analysis, so Kotamarthi and his colleagues decided to launch many simulations
with slightly different initial conditions. This ensemble-based approach creates
a better method to correct for uncertainty, he said.
"We need to generate better forecasts of ozone, carbon monoxide and other
trace gases for air-quality applications," Kotamarthi said. "And
the way to do that is by assimilating the data taken today into the forecast
for tomorrow. But the data come with certain types of uncertainty that most
models are unable to accommodate."
The ensemble methods will give policy-makers another tool to guide their decisions,
Kotamarthi added. "There's very little merit in trying to decide a policy
based on a single emissions scenario," he said. "We need to combine
different measurements with a suite of new mathematical techniques in order
to help reduce the uncertainty in our forecasts."
Although Kotamarthi's model looks expressly at carbon-monoxide emissions,
he claimed that researchers could use similar algorithms to examine the atmospheric
concentrations of carbon dioxide and other greenhouse gases and aerosols. Kotamarthi
and Argonne environmental scientist Paul Hovland have initiated a NASA-funded
project to develop data assimilation methods for worldwide chemical transport
models that can incorporate satellite measurements of several atmospheric gases.
Data assimilation may also boost researchers' ability to project likely climate
scenarios for the "near-term decadal scale"—approximately 10 to
20 years—which would help public officials assess the consequences of their
decisions that concern climate change.
The results
of the study were published in the Journal
of Geophysical Research.
Kotamarthi's research was funded by the Office of Biological
and Environmental Research in the U.S. Department of Energy's Office
of Science. For more than 50 years the Biological and Environmental Research
(BER) program has been advancing environmental and biomedical knowledge that
promotes national security through improved energy production, development
and use; international scientific leadership that underpins our nation's
technological advances; and research that improves the quality of life for
all Americans. BER supports these vital national missions through competitive
and peer-reviewed research at national laboratories, universities, and private
institutions. In addition, BER develops and delivers the knowledge needed
to support the President's National Energy Plan.
Argonne National Laboratory seeks solutions to pressing national problems in science and technology.
The nation's first national laboratory, Argonne conducts leading-edge basic
and applied scientific research in virtually every scientific discipline. Argonne
researchers work closely with researchers from hundreds of companies, universities,
and federal, state and municipal agencies to help them solve their specific
problems, advance America 's scientific leadership and prepare the nation for
a better future. With employees from more than 60 nations, Argonne is managed
by UChicago
Argonne, LLC for
the U.S.
Department of Energy's Office
of Science.
By Jared Sagoff.
For more information, please contact Steve McGregor (630/252-5580 or media@anl.gov) at Argonne.
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