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Video Amy McGovern is one of those rare people who moved to Oklahoma for the weather. Which isn’t to say she personally enjoys the tornadoes that regularly tear through the state or the routine pummeling with golf-ball-size hail. “I’m on roof number three in 15 years,” she laughs. But that severe weather is her reason for coming: A computer scientist who formerly worked in robotics, she was recruited by the University of Oklahoma’s school of meteorology. And last fall, with $20 million from the National Science Foundation, she launched one of the country’s foremost institutes applying artificial intelligence to weather and climate. As new techniques in machine learning become ubiquitous and yield startling applications, such as recognizing faces or mimicking human writing, her center is part of a new push to see if they can read the clouds. SPACEX LAUNCH LEAVES BEHIND COLORFUL CLOUD EFFECT OVER FLORIDA SKIES Dr. McGovern’s institute, which also includes six other universities and a variety of private partners, is part of that effort. In addition to developing artificial-intelligence methods to improve prediction of extreme weather and coastal oceanography, they are working to ensure the tools they develop are viewed as trustworthy by the human forecasters who will ultimately use them. “We’re testing the whole cycle,” she says. “We will actually save lives and save property.” AI is already making existing prediction methods more efficient and contributing to increases in the speed and accuracy of forecasting, and it shows promise for tracking the paths of severe
weather like tornadoes and hail with greater precision. The technology isn’t going to replace traditional weather forecasting but rather augment and strengthen existing methods. Boosting efficiency There is enormous opportunity in more accurately forecasting and better preparing for severe weather. According to the National Oceanic and Atmospheric Administration, in 2020 there were a record-setting 22 weather and climate disasters that each did more than $1 billion in damage. Modelers estimate the recent freeze in Texas destroyed infrastructure and disrupted supply chains worth $90 billion. And for all the improvements in forecasting over the years, there is still a lot we don’t know. According to Dr. McGovern, the government is fairly comprehensive in giving advanced warnings of tornadoes, anticipating 80% of them, but prone to false positives, with 80% of warnings turning out to be mistaken. Since the beginning of modern weather forecasting in the 1950s, meteorologists have primarily relied on “numerical weather prediction” — mathematical models that simulate the world and atmosphere in accordance with the physics of water, wind, earth and sunlight, and the infinite ways they interact. In the pursuit of an ever more detailed rendering, today’s models incorporate about 100 million pieces of data each day, a level of complexity comparable to simulations of the human brain or the birth of the universe. GET FOX BUSINESS ON THE GO BY CLICKING HERE For decades, this has produced steady improvements in forecast accuracy. But in recent years, the proliferation of Earth-observation satellites as well as new sensors, like the air-pressure monitors in billions of mobile phones, have outstripped scientists’ ability to integrate them into weather models. And even crunching a fraction of these data
has demanded exponential increases in computing power to make timely predictions. The latest artificial-intelligence techniques work in a fundamentally different way from older techniques by training neural networks on this deluge of data rather than on the laws of physics. Instead of using brute-force computation to forecast weather based on present conditions, these networks review data on weather from the past and develop their own understanding of how conditions evolve. Rudimentary AI techniques have been applied to weather and climate for decades — the National Oceanic and Atmospheric Administration sponsored its first conference on AI way back in 1986 — but recent advances in deep learning and greater access to computers capable of running them have enabled a swift uptick in research. AI techniques aren’t being used to generate forecasts on their own, at least not yet. That is partly because traditional methods are quite good: Two weeks ahead of the winter storm that clobbered Texas in mid-February, the Fort Worth office of the National Weather Service began advising that unusually cold weather was on the way, and by a week out many models were estimating its intensity within a few degrees. Ted Ryan, a meteorologist there, says they sometimes run forecasts through a sophisticated machine-learning algorithm to see if it delivers substantially different results from their own, but don’t routinely integrate it into their forecast and messaging operations. “It’s somewhere between a curiosity and a novelty.” Another challenge for AI is that it is best at predicting patterns that are common among the data it has trained on — but weather matters most when it is outside the ordinary, such as the Texas storm, which tied for the coldest temperature since 1899. AS TEXAS FREEZE GAS BILLS COME DUE, CUE UP THE
Suitable for Women/Men/Girl/Boy, Fashion 3D digital print drawstring hoodies, long sleeve with big pocket front. It’s a good gift for birthday/Christmas and so on, The real color of the item may be slightly different from the pictures shown on website caused by many factors such as brightness of your monitor and light brightness, The print on the item might be slightly different from pictures for different batch productions, There may be 1-2 cm deviation in different sizes, locations, and stretch of fabrics. Size chart is for reference only, there may be a little difference with what you get.
- Material Type: 35% Cotton – 65% Polyester
- Soft material feels great on your skin and very light
- Features pronounced sleeve cuffs, prominent waistband hem and kangaroo pocket fringes
- Taped neck and shoulders for comfort and style
- Print: Dye-sublimation printing, colors won’t fade or peel
- Wash Care: Recommendation Wash it by hand in below 30-degree water, hang to dry in shade, prohibit bleaching, Low Iron if Necessary
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