这篇文章来自 nvidia.com。原始 url 是: https://blogs.nvidia.com/blog/2018/03/28/predicting-hail/
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Imagine tens of thousands of golf balls falling from the sky at more than 100 mph, and you’ll get an idea of the damage hail can wreak.
In just a few minutes, it can batter and ruin crops, dent cars and smash windshields, and even bash holes in houses and buildings — resulting in billions of dollars in losses.
“Because hail is so damaging, we want to be able to forecast it more reliably so people can take shelter and protect their property,” said David Gagne II, a postdoctoral fellow at the National Center for Atmospheric Research, in a talk at the GPU 技术会议this week.
Gagne and other scientists at NCAR are using GPU-accelerated 深入学习to more accurately forecast the likelihood of hail, where it will fall and how large it will be.
Current Hail Forecasts Fall Short
Hail happens when upward air currents from thunderstorms are strong enough to carry water droplets well above freezing level. These frozen droplets become hailstones, which grow as additional water freezes to them. When hailstones are too heavy for the updrafts, they fall to the ground.
Meteorologists and other scientists have multiple ways of predicting storms, but all of these have shortcomings that can result in missed storms and false alarms, Gagne said. Scientists have also experimented with machine learning-based forecasts.
“Machine learning can produce reliable severe weather forecasts but it struggles with learning spatial patterns,” Gagne said. These patterns show what areas will be affected by rain or hail.
AI Prediction Potential
By contrast, it’s relatively easy to integrate spatial patterns, time and physical understanding of conditions into deep learning models, according a 论文by Gagne and other scientists published in the American Meteorological Society journal.
AI also has the potential to reveal new knowledge in data such as Doppler radar maps, the multi-colored maps you see in weather forecasts on TV.
“I wonder if deep learning can look at these images and see what meteorologists see, or if it sees something different,” Gagne said.
Predicting Hail That Dings Your Car
He and the team use NVIDIA Tesla GPUs以及该 cuDNN-accelerated TensorFlow deep learning framework to train their models to predict hail more than 25 millimeters (about an inch) in diameter, or about the size of a quarter.
“That’s the size where it will ding your car or mess up your roof,” Gagne said.
In experiments so far, their models generally produced fewer false alarms and higher accuracy than other methods. Better hail predictions would give people the chance to move to protected areas, park their cars beyond the reach of storms and allow airports to reroute planes or cancel flights, Gagne said.
Gagne and other scientists are also experimenting with AI to predict the type of expected precipitation, severe winds and storm lifetimes.