Nav: Home

New IST research leverages big data to predict severe weather

June 21, 2017

Every year, severe weather endangers millions of people and causes billions of dollars in damage worldwide. But new research from Penn State's College of Information Sciences and Technology (IST) and AccuWeather has found a way to better predict some of these threats by harnessing the power of big data.

The research team, led by doctoral student Mohammad Mahdi Kamani and including IST professor James Wang, doctoral student Farshid Farhat, and AccuWeather forensic meteorologist Stephen Wistar, has developed a new approach for identifying bow echoes in radar images, a phenomenon associated with fierce and violent winds.

"It was inevitable for meteorology to combine big data, computer vision, and data mining algorithms to seek faster, more robust and accurate results," Kamani said. Their research paper, "Skeleton Matching with Applications in Severe Weather Detection," was published in the journal of Applied Soft Computing and was funded by the National Science Foundation (NSF).

"I think computer-based methods can provide a third eye to the meteorologists, helping them look at things they don't have the time or energy for," Wang said. In the case of bow echoes, this automatic detection would be vital to earlier recognition of severe weather, saving lives and resources.

Wistar, the meteorological authority on the project, explained, "In a line of thunderstorms, a bow echo is one part that moves faster than the other." As the name suggests, once the weather conditions have fully formed, it resembles the shape of a bow. "It can get really exaggerated," he said. "It's important because that's where you are likely to get serious damage, where trees will come down and roofs get blown off."

But currently, when the conditions are just beginning to form, it can be easy for forecasters to overlook. "Once it gets to the blatantly obvious point, (a bow echo) jumps out to a meteorologist," he said. "But on an active weather day? They may not notice it's just starting to bow."

To combat this, the research focused on automating the detection of bow echoes. By drawing on the vast historical data collected by the National Oceanic and Atmosphere Administration (NOAA), bow echoes can be automatically identified the instant they begin to form. Wang said, "That's our project's fundamental goal -- to provide assistance to the meteorologist so they can make decisions quicker and with better accuracy."

By continually monitoring radar imagery from NOAA, the algorithm is able to scan the entire United States and provide alerts whenever and wherever a bow echo is beginning. During times of active severe weather, when resources are likely to be spread thin, it's able to provide instant notifications of the development.

"But this is just the first step," Kamani commented. With the detection algorithm in place, they hope to one day forecast bow echoes before they even form. "The end goal is to have more time to alert people to evacuate or be ready for the straight line winds." With faster, more precise forecasts, the potential impacts can be significant.

"If you can get even a 10, 15 minute jump and get a warning out earlier pinned down to a certain location instead of entire counties, that's a huge benefit," Wistar said. "That could be a real jump for meteorologists if it's possible. It's really exciting to see this progress."

Envisioning the future of meteorology, the researchers see endless potential for the application of big data. "There's so much we can do," Wang said. "If we can predict severe thunderstorms better, we can save lives every year."
-end-


Penn State

Related Big Data Articles:

Discrimination, lack of diversity, & societal risks of data mining highlighted in big data
A special issue of Big Data presents a series of insightful articles that focus on Big Data and Social and Technical Trade-Offs.
'Charliecloud' simplifies Big Data supercomputing
At Los Alamos National Laboratory, home to more than 100 supercomputers since the dawn of the computing era, elegance and simplicity of programming are highly valued but not always achieved.
Advances in bayesian methods for big data
Big Data has imposed great challenges for machine learning. Bayesian methods provide a profound framework for characterizing the intrinsic uncertainty and performing probabilistic inference and decision-making.
Compiling big data in a human-centric way
When a group of researchers in the Undiagnosed Disease Network at Baylor College of Medicine realized they were spending days combing through databases searching for information regarding gene variants, they decided to do something about it.
Story of silver birch from genomic big data
Researchers at University of Helsinki, Finland and University at Buffalo, USA have analyzed the evolutionary history of silver birch using big data from the genomes of 150 birches.
Night lights, big data
Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Environmental Defense Fund (EDF) have developed an online tool that incorporates 21 years of night-time lights data to understand and compare changes in human activities in countries around the world.
Big data approach to predict protein structure
Nothing works without proteins in the body, they are the molecular all-rounders in our cells.
Is your big data messy? We're making an app for that
Vizier, software under development by a University at Buffalo-led research team, aims to proactively catch big data errors.
Big data for the universe
Astronomers at Lomonosov Moscow State University in cooperation with their French colleagues and with the help of citizen scientists have released 'The Reference Catalog of galaxy SEDs,' which contains value-added information about 800,000 galaxies.
Using Big Data to understand immune system responses
An enzyme found in many bacteria, including the bacterium that gives us strep throat, has given mankind a cheap and effective tool with which to edit our own genes.

Related Big Data Reading:

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Digital Manipulation
Technology has reshaped our lives in amazing ways. But at what cost? This hour, TED speakers reveal how what we see, read, believe — even how we vote — can be manipulated by the technology we use. Guests include journalist Carole Cadwalladr, consumer advocate Finn Myrstad, writer and marketing professor Scott Galloway, behavioral designer Nir Eyal, and computer graphics researcher Doug Roble.
Now Playing: Science for the People

#529 Do You Really Want to Find Out Who's Your Daddy?
At least some of you by now have probably spit into a tube and mailed it off to find out who your closest relatives are, where you might be from, and what terrible diseases might await you. But what exactly did you find out? And what did you give away? In this live panel at Awesome Con we bring in science writer Tina Saey to talk about all her DNA testing, and bioethicist Debra Mathews, to determine whether Tina should have done it at all. Related links: What FamilyTreeDNA sharing genetic data with police means for you Crime solvers embraced...