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New statistical models could lead to better predictions of ocean patterns
Date:3/18/2014

ion to understand and better predict the uncertainty associated with elections," Wikle said. "So much like that, we developed more sophisticated statistical methods to combine various sources of datasatellite images, data from ocean buoys and ships, and scientific experienceto better understand the atmosphere over the ocean and the ocean itself. This led to models that help to better predict the state of the Mediterranean Sea, and the long-lead time prediction of El Nińo and La Nińa. Missouri, like most of the world, is affected by El Nińo and La Nińa (through droughts, floods and tornadoes) and the lowest levels of the food chain affect us all through its effect on Marine fisheries."

El Nińo is a band of warm ocean water temperatures that periodically develops off the western coast of South America and can cause climatic changes across the Pacific Ocean and the U.S. La Nińa is the counterpart that also affects atmospheric changes throughout the country. Wikle and his fellow researchers feel that, through better statistical methods and models currently in development, a greater understanding of these phenomena and their associated impacts will help forecasters better predict potentially catastrophic events, which will likely be increasingly important as our climate changes.


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Contact: Jeff Sossamon
sossamonj@missouri.edu
573-882-3346
University of Missouri-Columbia
Source:Eurekalert  

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