Meteorology Pioneer Borrows from Darwinism for Latest Forecast Innovation – Laboratory Equipment

In college, Paul Roebber reveled in the interdisciplinary aspects of meteorology. This was a sign to come, as Roebber, now a professor at the University of Wisconsin, Milwaukee, would go on to apply biological aspects in his research as he became one of the foremost experts in meteorology forecasting.

Ten years ago, Roebber designed weather forecast simulations that were organized like networks of neurons in the brain. The computer programs formed a system of interconnected processing units that could be activated or deactivated. This artificial neural network tool proved especially proficient at predicting scenarios with large data gaps and reams of variables. It significantly advanced snowfall prediction effortsso much so that the artificial neural network is now used by the National Weather Service.

For me, creativity comes from being open to broad interests, said Roebber in a release from the University of Wisconsin, Milwaukee.

Recently, that broad interest extended to Charles Darwins evolution theory based on the finches of the Galapagos Islandsspurring Roebbers next big weather innovation.

Metrology meets biology

Currently, weather forecasters use ensemble modeling, which predicts the weather based on the average of many weather models combined. But, ensemble modeling isnt always accurate as each model is so similar, they end up agreeing with each other, rather than the actual weather. Essentially, more data diversity is needed to distinguish relevant variables from irrelevant ones. However, its expensive to obtain and add new data.

The importance of a weather forecast goes beyond you bringing an umbrella to work, or planning to host a party outdoors. In fact, an estimated 40 percent of the U.S. economy is somehow dependent on weather prediction. Even a small improvement in the accuracy of forecasts could save millions of dollars annually for the industries that are affected mostnotably agribusiness and construction.

So, if the key to improving ensemble modeling is data diversityhow do you do it without first collecting new data?

Roebber found the answer in nature.

In 1835, Darwin observed what came to be known as natural selection in a population of finches inhabiting the Galapagos Islands. The birds divided into smaller groups, each residing in different locations around the islands. Over time, they adapted to their specific habitat, making each group distinct from the othersand all different from the original finches.

Applying this to weather prediction models, Roebber devised a mathematical method in which one computer program sorts 10,000 other ones, improving itself over time using strategies such as heredity, mutation andof coursenatural selection. The professor began by subdividing existing variables into conditional scenarios: the value of a variable would be set one way under one condition, but be set differently under another condition.

Then, his computer program picks out the variables that best accomplish the goal and recombines them. This means the offspring weather prediction models improve in accuracy because they block more of the unhelpful attributesjust as Darwin observed all those years ago.

One difference between this and biology is, I wanted to force the next generation [of models] to be better in some absolute sense, not just survive, Roebber said in a UWM press release.

He is already using the evolutionary methodology to forecast minimum and maximum temperatures for seven days out, and the technique is outperforming models used by the National Weather Service. In particular, Roebbers new model works well on long-range forecasts and extreme events, when an accurate forecast is most needed.

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Meteorology Pioneer Borrows from Darwinism for Latest Forecast Innovation - Laboratory Equipment