Advanced Analysis Tools Aim to Reduce Uncertainty in Climate Data

March 26, 2010
Diagram of data inputs of all natural processes to simulate Earth's complex climate system.

Pacific Northwest National Laboratory researchers have developed a new, advanced data-reduction method -- Stochastic Proper Orthogonal Decomposition -- that will greatly improve the capability to deal with uncertainty in the high-dimensional noisy data from random simulations.

Additionally, researchers have created a general approach for nonlinear bi-orthogonal decomposition of random fields to deal with uncertainty in climate model data as well as in measurements. Researchers have developed an efficient algorithm based on this approach to find the optimal positions for sensor placement to obtain . This will help further reduce uncertainty in the data.

These results are part of a project funded by ASCR to analyze petascale, noisy data from global climate models and available experimental data to predict global warming scenarios.

If successful, the larger research project will have a revolutionary impact on how scientists analyze petascale, noisy, incomplete data in complex systems and ultimately lead to better future prediction and decision-making.

Extracting scientific knowledge from massive petascale data sets has become increasingly difficult and necessary as com puter systems have grown larger and experimental devices more sophisti cated. Petascale computers are capable of performing one quadrillion—one million billion—operations per second.

Numerical models and experimental approaches have their own limitations. Data-driven simulations at the petascale level could lead to great advances in accurately predicting the performance of dynamic data-driven systems. But the main bottleneck to reducing uncertainty is the ability to attract useful information from the petascale out puts of large-scale unsteady simulations. In addition, the uncertainty associ ated with the simulation inputs may render the results of these highly expensive simulations erroneous, especially in long-term predictions. Therefore, advanced data analysis tools, such as Stochastic Proper Orthogonal Decomposition, have to be developed to deal with petscale level noisy, incomplete data sets.

Noise-induced transition in the gradient systems, where the vector field is the gradient of a potential function, has been studied for a long time and understood very well. However, the understanding of transition events in non-gradient systems, such as climate system, is much less satisfactory. To effectively detect and predict the transition pathways in non-gradient systems, researchers have employed the minimum action method (MAM) derived from the large deviation theory. Researchers have successfully used the MAM method to detect the minimum action paths for the two dimensional noise-driven Ginzburgh-Laudau system and the Kuramoto-Sivashinsky equation.

Researchers will be applying the developed framework to climate science, where effective petascale data-reduction techniques are critical to analyzing the huge amount of numerical and experimental data generated from petaflop computers and high-throughput instruments.

Explore further: Petascale climate modeling heats up

More information: Wan, X, X Zhou and W E. 2010. "Study of the noise-induced transition and the exploration of the phase space for the Kuramoto-Sivashinsky equation using the minimum action methodaction method," Nonlinearity 23, 475-493.

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not rated yet Mar 27, 2010
Here's a thought. Why don't we just go out and sort out all the BS in the world network of weather stations instead so that we don't have to wave magic wands like this over crap data in the future?

We could also start taking hourly data from weather stations again after abandoning the practice a few decades ago because of a crackbrained new policy aimed at saving mag tape. I ran into that bit of idiocy in South Africa when I was developing typical climate data weather years for the CSIR down there in the mid-1980's.
3 / 5 (1) Mar 29, 2010
I am inclined to believe three things

1) due to massive cutbacks we have reduced the number of field stations in unpopulated areas by over 50% - this may lead to a data scew

2) 60 years ago the fear was that the earth was getting colder

3) It doesn't actually matter if the climate people are right or wrong --- But the preventative measures we are starting to take are a good thing no matter what. Cleaner energy production is a good thing -- becoming more effiecent is a good thing -- recycling more is a good thing...

so F the models -- no one cares anymore -- but lets not stop funding a good thing -- if only education could get such money in the US

4) - i know and extra one - we as humans haven;t been collecting data long enough (100 yrs) to know a trend in a planet thats had trends over 10,000,000 years.

Lets face it science has confirmed one thing -- every few million years water engulfs the planet and then recedes and life on land , if it survived - starts up again

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