Will help climate scientists make data-driven discoveries
This story was written by Megan Hanks and first appeared on news.umbc.edu
Jianwu Wang, assistant professor of information systems, is the most recent UMBC faculty member to receive a prestigious CAREER Award from the National Science Foundation (NSF). Wang’s NSF grant totals more than $500,000 over five years. It will support his work to develop more efficient and reproducible causality analytics for use in climate science.
Understanding cause and effect is fundamental to research in many disciplines. There are some unique challenges in using climate data to discover cause-effect relationships. Wang explains that Earth changes so rapidly that climate scientists studying it must continuously capture a huge volume of data. Each point in time yields distinct information about the planet’s environment, and there is no way to retest the climate to confirm causal relationships.
Rapidly available climate data can be challenging for researchers to keep up with, explains Wang. “Computation and data techniques have become the third and fourth paradigm for science in many disciplines,” he says. “The novel computational and data science techniques we will study through this award could help climate or Earth scientists to quickly find interesting patterns from data and use data to conduct hypothesis testing.”
Using big data to study Earth
Wang is exploring new ways of applying artificial intelligence and data science to studying Earth and its climate system. He has collaborated with faculty across UMBC, including in the physics department and the Joint Center for Earth Systems Technology, where he is an affiliated faculty member. These connections have helped him better understand the needs of climate scientists.
Recent advances in artificial intelligence and data science give hope to studying Earth from a data-driven perspective. Yet climate scientists are challenged by the need to process terabytes of data collected about Earth, Wang has learned. The data’s volume and complexity can make it very difficult to examine, which could mean missed opportunities for insights.
Wang hopes that his work will help climate scientists better understand the data they have already collected, and find better ways to test their causality-related hypotheses. The end goal of the research is to develop a climate causality analytics platform that is scalable and reproducible.
Research opportunities for students
As he plans next steps, Wang is particularly looking forward to providing students with research opportunities. He will connect with graduate and undergraduate students through UMBC’s Center for Women in Technology, McNair Scholars program, and Louis Stokes Alliances for Minority Participation (LSAMP) program. He will also connect with local high school students through UMBC’s Shriver Center.
The range of perspectives these students bring will help Wang address the challenges posed by climate data, ultimately making it more useful and accessible. “We expect our research will help climate scientists efficiently discover causal relationships from complex climate datasets and easily share their findings with others,” he explains.
“These findings could help us better understand how Earth’s climate system works,” Wang says. “Eventually, they could help us better predict and adapt to many specific climate-related events, such as extreme heatwaves, droughts, and floods.”
Banner image: The California Current System. Photo by: NASA/Goddard/Suomin-NPP/VIIRS, used under CC 2.0.
Posted: April 29, 2020, 1:31 PM
