Supercomputer Simulations Generate Surge of New Data Related to Stormy Weather Predictions

Published July 27, 2023

By Kimberly Mann Bruch

Convection, which transports heat, momentum and moisture from near the ground higher into the atmosphere, often produces the majestic clouds we see in the sky, which can also be viewed from satellites in space. Convection can also develop into severe thunderstorms or hurricanes, which can result in destructive flooding, high winds and lightning. To better predict the specifics of these storms, researchers at The Pennsylvania State University (Penn State) recently used Expanse at the San Diego Supercomputer Center (SDSC) at UC San Diego and Stampede2 supercomputer at the Texas Advanced Computing Center to closely examine the role of convection in global energy distribution.

“By incorporating geostationary satellite observations along with high-resolution global computer simulations, we were able to uncover new insights into the role of convection and convective systems,” said Da Fan, a doctoral student researcher at Penn State’s Department of Meteorology and Atmospheric Science and the Center for Advanced Data Assimilation and Predictability Techniques. “We used Expanse to estimate the importance of thunderstorms and weather systems in the energy distribution at various scales–from the local storm scale to the global scale.”

Fan said that the Expanse calculations validated that moist processes can energize the mesoscale of kinetic energy and that brightness temperature spectra showed dependence on convective systems at different scales.

“These results point the way toward a new approach to evaluate the predictability of convective systems, and future development of model dynamics and parameterization,” he said. “Our research makes a significant contribution to the field by advancing our understanding of the sources of energy at different scales.”

Fan worked with Penn State Associate Professor of Meteorology Steven J. Greybush, who is also associate director for the Center for Advanced Data Assimilation and Predictability Techniques, on this study. Their work was published in a Journal of Atmospheric Sciences article entitled Exploring the Role of Deep Moist Convection in the Wavenumber Spectra of Atmospheric Kinetic Energy and Brightness Temperature.

“Our research project required massive computational resources to process and analyze the vast amounts of simulation and observation data and plot figures,” Fan said. “SDSC’s Expanse supercomputer provided the high-performance computing capabilities necessary to carry out these complex analyses in a timely, efficient manner, and we are grateful for the support and resources provided by the Expanse team, which made this research project a success.”

He said that SDSC saved a lot of time for data processing and analysis and also provided the storage to save the huge amounts of data required for the study.

Fan and Greybush said that the findings have practical implications for validating the dynamic core of the Next Generation Global Prediction System (NGGPS) in the United States, which could ultimately increase the confidence towards its application in many research questions within the meteorological community.

“Overall, we believe that our research represents an important step forward in the field and has the potential to advance discovery and innovation in this domain,” Fan said. “Our findings could inform the accuracy of the NGGPS, which is critical for weather and climate forecasting–this, in turn, provides more accurate and timely weather and climate forecasts.”

He noted that the agreement between satellite observations and Expanse-generated simulations really surprised the team. “It gave us confidence to further explore the role of convection in global energy distribution and opened up several exciting avenues for future investigation,” Fan said.

One area that the researchers plan to further explore is the role of shallow convection in global energy distribution, which could help to shed more light on general predictability, which is the science of how accurately we can make weather predictions and what processes make prediction challenging. They also plan to apply their methods to validate other numerical weather prediction models by incorporating geostationary satellite observations. 

“I’d also like to note that our work was initially inspired by the scientific discovery of our Penn State colleague, Man-Yau ("Joseph") Chan,” Fan said.

Chan’s work has been focused on the application of geostationary satellite observations in improving the ability to predict mesoscale convective systems through data assimilation.

“The idea of applying Chan’s method to our work was proposed by my former advisor Fuqing  Zhang, who is recently deceased,” Fan said. “Without the help of SDSC’s supercomputer Expanse for our complex calculations and the original inspiration from these two colleagues, our work would not have been as successful.”

The time on Expanse was funded by the National Science Foundation ACCESS (allocation no. ATM090042).