SAN FRANCISCO -- The chance of more accurate weather forecasts might improve this afternoon (Feb. 22), when two Cornell University graduate students describe their new method of statistical forecast analysis that could lift current forecasting techniques out of a very light fog.

Based on a relatively new mathematical concept known as wavelets, the students' methods could help forecasters develop a new prediction paradigm.

"This is not a forecast itself," said William M. Briggs, Cornell doctoral student in meteorological statistics. "It allows the meteorologist to gauge how good the forecast was after the fact. We use a mathematical concept known as wavelets to develop new error diagnostic procedures." For example, the new method gauges field forecasts of temperature over a wide-area grid, then determines how close the forecast was to what actually happened. Using this information, meteorologists soon will be able to develop a better database and clearly see where the weaknesses are in their own forecasts.

Briggs and Richard A. Levine, Cornell doctoral student in statistics, will present their paper, "Wavelets and Image Comparison: New Approaches to Field Forecast Verification," at the American Meteorological Society's 13th Conference on Probability and Statistics in the Atmospheric Sciences in San Francisco on Feb. 22.

Their method, which could later help hone weather forecasts, was developed in the environmental statistics program directed by George Casella, Cornell Liberty Hyde Bailey Professor of Biometrics, and David Ruppert, Cornell professor of operations research. Briggs studies under Daniel Wilks, Cornell professor of atmospheric sciences.

"Briggs and Levine are applying new statistical methodology to an extremely difficult prediction problem," Casella said. "They face a lot of complications. While trying to model physical processes and manage data sets, they're mixing a lot of complex characteristics. That makes it difficult." EDITORS: William Briggs and Richard Levine can be reached at Cathedral Hill Hotel, (415) 776-8200, Feb. 20 to 24. Prior to and after the meeting, Briggs may be reached at (607) 255- 0180; Levine at (607) 255-9802.

But, what are wavelets?

"Wavelets are mathematical tools that help remove the noise from the data," Levine said. "Wavelets remove the errors that may exist. In data, there is real versus muddled data; information is muddled by extraneous stuff. But, somewhere in that information is an obvious trend that can't be seen because there is so much junk. By running data through wavelets, it gives a clearer picture of what's going on." As an analogy, suppose an old, scratchy sound recording were put onto a digital format -- scratches and all. The digitized music then could be electronically modified through a wavelet-oriented computer program and the extraneous scratches could be easily removed, leaving a more crisp sound intact.

With all the variables of weather to consider, forecasters have a tough time sifting through the noise, Levine said. This program arms the meteorologist with that much more information. For example, this method separates the insignificant data from the the truly useful information, so that meteorologists can better understand how wide-area forecasts of rain or temperature performed.

"This technique removes some of the subjectivity that now exists in the analysis of the forecast. Wavelets act like a variable microscope, allowing us to study forecast performance at different scales," Briggs said. "This method can show the meteorologist that we're doing well forecasting here, but perhaps not so well there. We think incorporating wavelets can give important insights on the forecast process."