Even after decades of meteorological advances, weather forecasting remains infamously difficult. Today’s most trusted models are rarely accurate beyond a two-week window due to a constant cascade of atmospheric and climatic variables. These unpredictable meteorological interactions are the foundations of chaos theory. The interdisciplinary field studies how even the world’s most predictable systems can still produce completely unpredictable results, and was first developed by MIT mathematician Edward Lorenz during the 1970s.

“He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with,” Judah Cohen, a climatologist also at MIT, explained.

According to Cohen, many climate researchers continue to view Lorenz’s two-week hurdle as insurmountable. And that assumption has largely proven true for nearly 50 years. Even today’s dynamical simulations and artificial intelligence models begin to break down when attempting to predict conditions two-to-six weeks in the future—a window known as subseasonal forecasting. Nicknamed the “predictability desert,” this range runs into slow-moving, highly variable forces like this year’s super El Niño event. Once those factors enter the equation, forecasting becomes more of a roll of the dice l than anything concrete. However, Cohen has had his suspicions about chaos theory’s limitations.

“I never really believed that completely,” he said. “In theory, the atmosphere can create an infinite number of patterns, but it likes to fall into these favorite patterns over and over again.”

To try and break this forecasting barrier, Cohen and his colleague Lester Mackey have spent years developing an AI system that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing. With postprocessing, the AI improves by comparing the inaccuracies with existing historical data, and then adjusts from there.

Cohen’s team recently submitted three versions of their model featuring the postprocessing system the European Center for Medium-Range Weather Forecasts (ECMWF). Widely known as the world’s best forecasting hub, the ECMWF also hosts seasonal competitions that pit the latest prediction models against one another. Most of these systems have relied on historic climate averages to reach their conclusions, but this was the first time a group submitted a program with postprocessing capabilities.

Cohen and his colleagues reported their results in a study published today in the journal Nature. Their AI postprocessing integration blew the existing forecasting models out of the water. For the two-to-six-week subseasonal window, researchers doubled overall prediction accuracy. Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.

“It also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” said Cohen. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.” 

Many researchers have long presumed subseasonal forecasting to be a dead end, but the latest leap may clear the way to even bigger advances in the field.

“People have questioned if it’s even worth investing in models at all for this time range,” said Cohen. “[But] we consistently beat climatology averages with our models.”

The post New weather model may finally offer a reliable six-week forecast appeared first on Popular Science.

As residents on the northern Gulf Coast brace for quickly strengthening Hurricane Isaias (a Category 3 storm as of Friday afternoon), many are likely eyeing their roofs and walls with nervous anticipation. The Congressional Budget Office estimates hurricane force winds and storm-related flooding collectively cost households in the United States some $34 billion every year. And while rounded corners, sloped walls and reinforced construction can help mitigate some wind damage, these additions are often expensive or impractical for developers looking to cut costs. Engineers at Florida A&M University and Florida State University think they’ve found a resourceful alternative, and it involves embracing the ocean’s own wavy aesthetic.  

For  a study recently published  in the journal Engineering Structures, the team built 3D-printed models of several low-rise buildings with wave shaped walls and roofs and tested them in a wind tunnel. The designs with the deepest waves, (in this case 10 percent of the building’s overall height) cut peak wind pressure near roof and wall corner by up to 60 percent. Those corners and edges are where damaging forces typically concentrate. Surprisingly simple designs like these could become increasingly attractive to develop in the coming years, as more and more communities brace themselves to endure stronger and more frequently occurring storms. 

“These nonconventional building shapes reduce the damage from worst-case severe weather scenarios,” Pedro Fernández-Cabán, a study co-author and Florida State University civil engineer, said in a statement. “It’s another tool for engineers and designers to protect against wind damage.” 

Bigger waves means better protection 

To test their idea, Fernández-Cabán and his colleagues started by 3D printing various models of low-rise buildings. Some of the buildings had wavy walls and roofs, while others were built with a more conventional baseline model with flat, straight surfaces. They made three wavy versions, with wave amplitudes of 5 percent, 7.5 percent, and 10 percent. The amplitude  measures the wave’s size in proportion to the building’s overall height in feet. So, for a 20-foot-tall building, a 10 percent amplitude would mean waves that extend about 2 feet.​​​​​​​​​​​​​​​​

Once the models were printed, the team then put them into a wind tunnel at the university. They measured wind pressure data on their surfaces as wind pelted the modelers from various directions. All of the wave-roofed models experienced reduced wind load compared to the flat version. The deepest wave model performed the best, reducing peak wind pressure near roof and wall corners by between 40 and 60 percent compared to the flat version. At least in theory, that means a building with that design could see significantly less damage when exposed to hurricane-force winds.

The paper includes a useful diagram (above) that breaks down why this wavy design works. When wind flows over a flat surface like a roof, it separates at the leading edge and from a single large, swirling vortex. That creates a zone of low pressure, while higher pressure underneath the building pushes out against the surface. That leads to a suction like effect that, in extreme scenarios, can peel roofs off of a building. Over a wavy surface, the wind skips along like a stone across a lake, forming a series of smaller swirls instead of one big one. That disruption to the wind can prevent high-suction picks from forming. The result is a building that with less exposure to the strongest, points many damaging wind forces than a flat one. 

“Changing a building’s shape can significantly reduce the intensity of wind forces it has to withstand,” Fernández-Cabán said. 

The researchers caution that this study focused specifically on wind pressure on the model’s surfaces. They’re now running additional experiments to better understand how wind flows around the wavy structures and plan to use computer modeling to fine-tune the wave patterns and test other shapes. If that work bears out similar to  the early results, it could make wavy walls and rooms all the more attractive to architects and engineers looking for simple solutions to protect buildings in storm prone areas.

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