Profiting from accurate weather predictions

Gregg Suhler, of Dynamic Predictables, has been analyzing data and predicting the weather for his clients for eight years.

Gregg Suhler, of Dynamic Predictables, has been analyzing data and predicting the weather for his clients for eight years.

Like all farmers trying to decide on a planting strategy, Fred Helms wanted to know back in December what the weather was likely to be in the spring and summer.

Rather than depending on the Farmer’s Almanac or traditional prediction methods, Helms took the advice of researchers at a Columbia company that’s in the business of making accurate weather forecasts, Dynamic Predictables.

“We got the seasonal outlook back in December,” said Helms, who grows corn and soybeans on his Illinois farm. “Back then we were still in a dryer period, but they predicted we were heading into a wetter time.

“We used their information, combined with what we were seeing, and made a decision on March 3 to buy another tractor to help us handle what we expected would be a big crop,” Helms said. “They were right, and we could mostly pay for the tractor in one season because of the work it allowed us to do. They help us maximize our income and make the season work for us.”

It has been said predicting the weather is one profession in which someone can be wrong on a regular basis and still succeed. The folks at Dynamic Predictables, where weather patterns must be predicted as far ahead as five years, would beg to differ.

Helms began using Dynamic Predictables when he first heard Al Peterlin, a meteorologist and partner with the firm, providing weather forecasts on the radio. “Everything he said turned out to be right, so I contacted him to see if they do work for individual farmers,” Helms said.

That was four years ago, and Helms has been farming with Dynamic Predictables’ guidance ever since.

Since 1998, Dynamic Predictables has consistently predicted the weather with accuracy that provides value to farmers and other businesses affected by weather patterns, such as energy suppliers and utilities.

Using a proprietary combination of climate history, mathematics and physics, the firm tells their customers what to expect, on a month-by-month basis, for years in advance.

“We say on the Web site that we can predict as far out as three years but, really, we can go further than that,” said Gregg Suhler, a Dynamic Predictables co-founder and managing partner. “Our predictions made five years ago are markedly better, not just relatively better, than what the National Weather Service’s climate prediction center can do a month at a time.”

This map of predicted precipitation and temperatures is some of the first data that Gregg Suhler gives to his clients.

This map of predicted precipitation and temperatures is some of the first data that Gregg Suhler gives to his clients.

Dynamic Predictables’ origins lie with the University of Missouri, where Suhler worked from 1988 to 2000, primarily with the Food and Agriculture Policy Research Institute.

“When I came to FAPRI, one of the things that I had some interest in was the linkage of climate and agriculture,” Suhler said. At that time, he started analyzing the historical record of various crop yields around the world and noticed a significant level of structure.

“Any time there is structure, you’ve got a potential for predictability,” he said.

Suhler said he then started looking all over the world for people who could help him crack the problem of climate predictability. He eventually discovered Doug O’Brien, a geophysicist who was a visiting professor at one of the national universities in Brazil. Together, they created the processes and eventually founded the company in 1998.

“Dynamic Predictables is a consequence of what was a climate prediction effort that had been within the university for a little less than five years,” Suhler said. “As often happens, budget crunch time came along and so the activity got externalized.”

In the early stages, Suhler and O’Brien spent much of their time researching and developing their processes. They say what they are doing is completely new and unique.

“Dynamic Predictables’ main activity is predicting temperature and precipitation at monthly time step for years in advance,” Suhler said. “The primary users have been in agriculture and energy. We have a few customers, but they are very happy.”

Suhler said a downside to being so cutting edge is that it can be difficult to convince potential customers that Dynamic Predictables can deliver long-range weather predictions with a high level of accuracy. As a consequence, most of the company’s customers are smaller operations, with fewer levels of decision-making.

“To be sure, most of our success is with individual farmers,” Suhler said. “If you’re doing something really, really new, and really different, an individual with a very streamlined decision making process can more readily make judgments.”

“Our biggest challenge is not our competitors,” Al Peterlin, marketing director and a partner at Dynamic Predictables, said. “It is the people who claim it is impossible to do what we do.”

Another business challenge Dynamic Predictables must face as it reaches for the next level of growth is the hesitation of their customers to talk about what the company does for them.

“In some cases, what we’re finding is that when people in a very competitive environment find something that works for them, they don’t want their competitors to know about it,” Suhler said. “That’s tough on a word-of-mouth business model. I think we’re looking at maybe trying to become a bit better known in some other ways and convert that into business.” v

For more information about Dynamic Predictables, visit www.dynapred.com.

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