• Subscribe

Trustworthy forecasts

Speed read
  • New institute to develop trustworthy AI for weather, climate, and coastal oceanography
  • Machine-learning models can find patterns that human forecasters may miss
  • Climate science may be gateway to machine-learning skills for minority students

Freezing rain is everyone’s least favorite winter weather forecast. No sledding, no snowmen—just slick sidewalks, icy highways, and downed power lines. Wouldn’t it be nice to know when you see that forecast for the dreaded ‘wintry mix’, on which side of freezing it’s going to fall?

<strong>When it comes to freezing rain</strong>, the difference between downed power lines and chilly rain is only 1 degree Fahrenheit, making it hard for weather models to predict. Courtesy NOAA. That’s one of the big questions for the AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), a coalition of academic, government, and industry researchers who are employing machine learning to tackle this and a host of other tricky weather and climate problems.

“Leading experts from AI, atmospheric and ocean science, risk communication, and education, will work synergistically to develop and test trustworthy AI methods that will transform our understanding and prediction of the environment,” says Amy McGovern, AI2ES principal investigator and a professor of computer science at the University of Oklahoma.

Saving lives, saving property

Good weather can mean a fun day at the lake or more time to spend outdoors with friends while social-distancing. But bad weather doesn’t just ruin your day—it can be downright dangerous.

<strong>Humans do a good job of predicting large tornadoes</strong>, but they have a harder time with smaller ones that still take lives. AI could help find patterns that humans miss. Courtesy Dr. Julie Demuth, National Center for Atmospheric Research. One of the superstars of violent weather is the tornado. Twisters kill around 80 people per year in the US and cause up to $20 billion in damage. McGovern says that humans already do pretty well forecasting large, violent tornadoes. It’s the smaller ones that they often miss—but those can still take lives.

“Violent tornadoes are usually forecasted well in advance. But if we can improve our prediction of the smaller ones, we can still save lives,” says McGovern. “There’s just so much data out there, that if we can use AI to find the patterns that humans are missing and improve the predictions, then we can improve resiliency, save lives, and save property.”

When it comes to the freezing rain mentioned above, the biggest forecasting difficulty lies in the one-degree Fahrenheit difference between regular rain and freezing rain. Additionally, if the temperature is hovering near 32 degrees, outcomes are very localized. It can be freezing and dangerous on one side of a city and safe on the other.

<strong>When temperatures drop</strong>, sea turtles in Corpus Christi Bay rise to the surface where they are run over by ships. Accurate forecasts could temporarily halt traffic and save turtles. Courtesy Jennifer Williams, Conrad Blucher Institute, TAMU-CC.“If I give you a temperature forecast for today and I said it would be 72° and it turned out to be 73°, you wouldn't be mad,” says McGovern. “But if I said 33° and raining versus 32° and raining, there's a big impact difference. Current [weather] models don't really see that one-degree error as something significant.”

And it’s not just humans whose lives could be saved. Fluctuating temperatures have a big impact on wildlife too. McGovern gives the example of sea turtles that live off the coast of Texas. When a strong cold front comes through, the turtles in Corpus Christi Bay come to the surface, where they are run over by ships. If temperature predictions were more accurate, shipping traffic could be temporarily halted and up to 80% of the turtles saved.

How AI can help

In order for humans to accept machine predictions, the AI models must be trustworthy. McGovern defines trustworthiness as AI that forecasters, emergency managers, and the general public will want to use. “The different needs of those end users will drive what it means to be trustworthy,” says McGovern. 

One of the first things McGovern and her colleagues are hoping to develop is explainable AI. Many current machine-learning models are ‘black boxes’—meaning that their creators do not understand how they work. But McGovern says that using models that are explicable will help improve trust in their outcomes.

<strong>Trusting machine predictions.</strong> (Top) Atmospheric river (rainfall) impacting California in February 2017. (Bottom) Regions most relevant for machine-learning method for predicting atmospheric rivers over North America 19 days later. Courtesy Elizabeth Barnes, Dept. of Atmospheric Science, Colorado State University. Another big challenge for the researchers of AI2ES is to create AI that reflects the physical reality of weather, climate, and oceanography.  

“Putting physics in [to a model] isn’t as simple as adding a little bit of math. If it was that simple, it would already be done,” says McGovern. “It tends to be that when you add physical constraints, you make things either not converge, or wildly diverge, or the model just doesn’t learn. So we have to figure out how to make it theoretically possible to put the constraints in while ensuring that we’re still getting the results we want.” 

The final piece is to develop robust AI that works under a variety of conditions. Meaning even if the data contains errors or omissions, the model retains a baseline of reliability and stability, and won’t predict something outrageous or impossible.

It won’t just be computer scientists and environmental scientists working towards these outcomes, but social scientists too. Risk communication researchers will interview end-users and the public in order to figure out what makes the AI trustworthy to them. “I think they are key to our success,” says McGovern.

Attracting the next generation

<strong>Making an impact.</strong> An AI certificate program at the community-college levels hopes to attract minority students to AI and geoscience careers. Courtesy Phillip Davis, Del Mar College. In addition to building trust, AI2ES also wants to build the next generation of climatologists and machine learning specialists. The Institute is working with Texas A&M—Corpus Christi and Del Mar College (both minority-serving institutions) to create an AI certificate program at the community college level, with the aim of improving diversity in both AI and geoscience careers.

“There’s a lot of research that shows minorities are interested in doing things that have an impact on the world,” says McGovern. “People really care about climate change right now. If we can show that using AI can improve something that relates to environmental science, we’re hoping we can hook more minority and under-represented students.”

Read more:

Join the conversation

Do you have story ideas or something to contribute? Let us know!

Copyright © 2023 Science Node ™  |  Privacy Notice  |  Sitemap

Disclaimer: While Science Node ™ does its best to provide complete and up-to-date information, it does not warrant that the information is error-free and disclaims all liability with respect to results from the use of the information.


We encourage you to republish this article online and in print, it’s free under our creative commons attribution license, but please follow some simple guidelines:
  1. You have to credit our authors.
  2. You have to credit ScienceNode.org — where possible include our logo with a link back to the original article.
  3. You can simply run the first few lines of the article and then add: “Read the full article on ScienceNode.org” containing a link back to the original article.
  4. The easiest way to get the article on your site is to embed the code below.