The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR).
Instead of zeroing in on the features of an individual face, scientists trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.
The promising results, published in the American Meteorological Society's Monthly Weather Review, highlight the importance of taking into account a storm's entire structure, something that's been challenging to do with existing hail-forecasting techniques.
"We know that the structure of a storm affects whether the storm can produce hail," said NCAR scientist David John Gagne, who led the research team. "A supercell is more likely to produce hail than a squall line, for example. But most hail forecasting methods just look at a small slice of the storm and can't distinguish the broader form and structure."
[...] Current computer models are limited in what they can look at because of the mathematical complexity it takes to represent the physical properties of an entire storm. Machine learning offers a possible solution because it bypasses the need for a model that actually solves all the complicated storm physics. Instead, the machine learning neural network is able to ingest large amounts of data, search for patterns, and teach itself which storm features are crucial to key off of to accurately predict hail.
For the new study, Gagne turned to a type of machine learning model designed to analyze visual images. He trained the model using images of simulated storms, along with information about temperature, pressure, wind speed, and direction as inputs and simulations of hail resulting from those conditions as outputs. The weather simulations were created using the NCAR-based Weather Research and Forecasting model (WRF).
The machine learning model then figured out which features of the storm are correlated with whether or not it hails and how big the hailstones are. After the model was trained and then demonstrated that it could make successful predictions, Gagne took a look to see which aspects of the storm the model's neural network thought were the most important. He used a technique that essentially ran the model backwards to pinpoint the combination of storm characteristics that would need to come together to give the highest probability of severe hail.
In general, the model confirmed those storm features that have previously been linked to hail, Gagne said. For example, storms that have lower-than-average pressure near the surface and higher-than-average pressure near the storm top (a combination that creates strong updrafts) are more likely to produce severe hail. So too are storms with winds blowing from the southeast near the surface and from the west at the top. Storms with a more circular shape are also most likely to produce hail.
[...] The next step for the newer machine learning model is to also begin testing it using storm observations and radar-estimated hail, with the goal of transitioning this model into operational use as well. Gagne is collaborating with researchers at the University of Oklahoma on this project.
"I think this new method has a lot of promise to help forecasters better predict a weather phenomenon capable of causing severe damage," Gagne said. "We are excited to continue testing and refining the model with observations of real storms."
(Score: 1, Funny) by Anonymous Coward on Tuesday August 20 2019, @03:46PM
heil von der Leyen [twitter.com]
(Score: 0) by Anonymous Coward on Tuesday August 20 2019, @03:55PM (2 children)
This has little to do with facial recognition. The only connection is that both use deep learning with convolutional neural networks. Wheelbarrows use wheels. So do automobiles. So does that mean automobiles use wheelbarrow technology?
(Score: 2) by JoeMerchant on Tuesday August 20 2019, @04:01PM
To Og the caveman who has never really seen a practical application for a wheel yet, yes, yes it does.
🌻🌻 [google.com]
(Score: 0) by Anonymous Coward on Tuesday August 20 2019, @04:10PM
I use self driving cars as an example to explain what I do (data science, machine learning, etc). I guess facial recognition would work just as well.
(Score: -1, Offtopic) by Anonymous Coward on Tuesday August 20 2019, @04:53PM (1 child)
WA state fails it:
How to steal a state budget: McCleary, et al. v. State of Washington - Supreme Court Case Number 84362-7: https://www.courts.wa.gov/appellate_trial_courts/supremecourt/?fa=supremecourt.mccleary_education [wa.gov]
How to steal a city: Montes v. City of Yakima - https://www.aclu-wa.org/cases/montes-v-city-yakima-0 [aclu-wa.org]
(Score: 2) by DannyB on Tuesday August 20 2019, @05:30PM
What does this have to do with the price of MAX 7219 chips in China?
To transfer files: right-click on file, pick Copy. Unplug mouse, plug mouse into other computer. Right-click, paste.