The key to improving weather forecasts may lie in the discovery of atmospheric ?hot spots?– regions in which small changes in conditions magnify quickly into large changes in the weather.

Researchers from the University of Maryland have shown that not all chaos on a weather map is equal, and they?ve developed a technique for identifying regions they call chaos hot spots. These hot spots shift location on a regular basis and cover about 20 percent of the global map at any given time.

?This work has tremendous potential for improving both the accuracy of existing forecasts and for increasing the length of time into the future that the weather can be predicted accurately,? says math professor James Yorke, principle investigator for the research project.

Weather is what scientists call a complex chaotic system whose central property is that a tiny change in one part of the system can become magnified over time into a major change elsewhere. This means that a small localized weather change not accounted for in computer forecasting models can cause the actual weather pattern to gradually diverge from the models until what occurs in the sky over our heads is very different from what the weatherman predicted a few days before.

Since 1992, the National Weather Service has provided ?ensemble forecasts,? in which a computer model generates a main forecast and several slightly adjusted forecasts that provide a range of possible outcomes for the weather. The forecast issued by local meteorologists represents a synthesis of these different models. The ensemble approach and other improvements that brought about accurate 3 and 5 day forecasts were developed by a co-leader of the Maryland team, Eugenia Kalnay, when she worked at the National Weather Service.

The Maryland researchers looked at global wind predictions from five of these ensemble forecasts at a particular level (the level at which atmospheric pressure is 500 millibars) in the atmosphere. Placing these five forecasts on the map, the researchers then looked at how each forecast deviated from the main forecast in wind strength and direction. By analyzing squares that are 688 miles by 688 miles on a global map, they identified regions in which these deviations in wind vectors tend to line up with one another. The wind vectors transform the regions in which they reside into chaos hot spots where good observations become most important for reducing forecasting errors. All other points on the map are less important for forecasting.

According to team member D. J. Patil, they are currently using wind vectors to identify hot spots, because these measurements are readily available for many points on global weather maps. However, he noted that findings about chaos hot spots also apply to other variables that affect weather patterns such as temperature, humidity and barometric pressure.

The project?s next step is to look for global hot spots based on running a hundred possible forecasts rather than just the five used in this work. The team then plans to move from a global perspective down to regional views of chaos hot spots that can translate into better regional and local forecasts. ?Going from a global to a more precise and therefore more data-rich regional view means the chaos hot spots will become more numerous and harder to pinpoint, and the weather impact of small atmospheric changes in these hot spots increases,? Patil says. The team will try to rank chaos hot spots based on which ones can best help keep ?good forecasts from going bad.? However, ?In some areas, your forecast doesn’t get any better no matter how many readings you take, so we want to be able to target those hot spots where frequent readings can provide information that really improves forecasts.?

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