Facial recognition software could be used to detect hail storms – and their severity.
That’s according to scientists at the US National Center for Atmospheric Research, who’ve tested the software’s effectiveness on meteorological data.
Specifically, they found that a deep learning model called a convolutional neural network can spot the early signs as they happen – better than current methods.
The promising results, published in the American Meteorological Society’s Monthly Weather Review, could be a game-changer for providing accurate weather warnings.
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AI: The promising results, published in the American Meteorological Society’s Monthly Weather Review, could be a game-changer for providing accurate weather warning
‘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.’
The research team also believe this can have beneficial effects on the economy.
‘Hail – particularly large hail – can have significant impacts on agriculture and property,’ said Nick Anderson, an NSF program officer.
‘Using these deep learning tools in unique ways will provide additional insight into the conditions that favor large hail, improving model predictions. This is a creative, and very useful, merger of scientific disciplines.’
Whether or not a storm produces hail hinges on myriad meteorological factors. The air needs to be humid close to the land surface, but dry higher up.
The freezing level within the cloud needs to be relatively low to the ground. Strong updrafts that keep the hail aloft long enough to grow larger are essential. Changes in wind direction and speed at different heights within the storm also seem to play a role
But even when all these criteria are met, the size of the hailstones produced can vary remarkably, depending on the path the hailstones travel through the storm and the conditions along that path. That’s where storm structure comes into play.
‘The shape of the storm is really important,’ Mr Gagne said.
‘In the past we have tended to focus on single points in a storm or vertical profiles, but the horizontal structure is also really important.’
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.
Weathered: 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, Mr Gagne turned to a type of machine learning model designed to analyse 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.
He then 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, Mr Gagne said.
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.
HOW DOES FACIAL RECOGNITION TECHNOLOGY WORK?
Facial recognition software works by matching real time images to a previous photograph of a person.
Each face has approximately 80 unique nodal points across the eyes, nose, cheeks and mouth which distinguish one person from another.
A digital video camera measures the distance between various points on the human face, such as the width of the nose, depth of the eye sockets, distance between the eyes and shape of the jawline.
A different smart surveillance system (pictured) can scan 2 billion faces within seconds has been revealed in China. The system connects to millions of CCTV cameras and uses artificial intelligence to pick out targets. The military is working on applying a similar version of this with AI to track people across the country
This produces a unique numerical code that can then be linked with a matching code gleaned from a previous photograph.
A facial recognition system used by officials in China connects to millions of CCTV cameras and uses artificial intelligence to pick out targets.
Experts believe that facial recognition technology will soon overtake fingerprint technology as the most effective way to identify people.
