University of Oklahoma researchers use TACC's Ranger supercomputer to simulate unpredictable tornadoe
On May 3, 1999, violent thunderstorms swept through Oklahoma and Kansas, spawning 66 tornados and claiming 48 lives. Most severe among them was the F5-intensity tornado that swept through Moore, Oklahoma, which carried wind speeds of more than 300 miles per hour — the most powerful winds ever recorded on earth. Thirty-six lives were claimed by this tornado alone, which caused nearly $2 billion in property damage.
Tornadoes are violent, rotating columns of air, generated by intense thunderstorms. Hundreds to thousands of tornadoes can strike in the United States each year, incurring billions of dollars in damages, dozens of deaths, and thousands of injuries annually.
One of several tornadoes observed by the VORTEX-99 team on May 3, 1999, in central Oklahoma. Note the tube-like condensation funnel, attached to the rotating cloud base, surrounded by a translucent dust cloud. [Courtesy of NSSL]
Yet, despite their power and preponderance, many mysteries remain regarding how tornadoes form. "Various theories exist that try to explain the cause of rotation in tornadoes," according to Ming Xue, professor of meteorology at the University of Oklahoma, and director of the Center of Analysis and Prediction of Storms (CAPS). "But the true causes are still not well understood."
One important principle behind the tornado's rotation is the conservation of angular momentum — the same principle that makes ice-skaters spin faster when they embrace their stretched arms. "Tornadoes form inside intense thunderstorms when there is strong vertical motion that causes a concentration of air towards a convergence center," Xue said.
"When you reduce the radius of rotation, you increase the rotation rate." And as the funnel narrows to a few dozen feet, the vortex grows into one of the most powerful natural forces on earth. Xue is on the forefront of tornado simulation and prediction research. Using the Ranger supercomputer at the Texas Advanced Computing Center (TACC), Xue (working with graduate students, Daniel Dawson and Ming Hu) has been able to simulate the May 3, 1999 tornado, as well as a May 8, 2003 F4 tornado that also passed through Moore, with unprecedented fidelity, obtaining simulations that match well with the observed storm in its characteristics, path and timing.
Xue's detailed analysis of the model output is providing insights into how and why tornadoes form, and how microphysical processes within the cloud affect tornado formation.
Unlike hurricanes, which last for several days and can be anticipated far out at sea, tornadoes are localized, short-lived and almost unpredictable, making them notoriously difficult to observe and foresee. "It's very hard to predict any weather system beyond its life cycle, and to predict tornadoes, we have to first forecast their parent storms," Xue said. "We're still trying to predict these thunderstorms with significant lead-time."
The difficulty in simulating tornadoes lies primarily in the difference between the size of the tornado itself and the much larger thunderstorms that spawn them as they travel over land. This difference in scale — from tens of meters (the size of the funnel) to hundreds of kilometers (the path of the storm as it travels and generates tornadoes) — makes for an extremely challenging multi-scale computational problem, where each scale has to be accurately handled in the numerical model. This doesn't even include the even larger-scale environmental flows that affect the development and evolution of thunderstorms themselves.
A still from an animation showing the predicted surface reflectivity field on the 50-m grid valid at 2213:45 UTC (a), and observed reflectivity at the 1.45° elevation of OKC radar at 2216 UTC (b).
Add the complex microphysics within the cloud (the processes that generate rain, wind and hail), and the large volumes of Doppler radar observations that have to be assimilated to allow for forward prediction, and you have a problem that can only be solved by the world's most powerful supercomputers.
The use of the Doppler radar data is important for prediction of all weather phenomena that involve precipitation. These forecasts "require sophisticated methods and software to effectively incorporate the data into numerical prediction models, a processing called data assimilation. Only a few groups in the world do this kind of work," Xue said, "and our center, CAPS, pioneered this area of research."
The ultimate goal of this process is combine advanced sensors and numerical models to predict hazardous weather, so the National Weather Service can provide warnings well in advance of their formation, a concept called ‘Warn on Forecast.' But before we can predict future cases, it's necessary to simulate past cases and produce a more complete picture of the atmosphere than raw observations can provide. Said Xue: "When we have good simulations, we can analyze the data and obtain a much better understanding of the processes involved in the weather phenomena."
On Ranger, Xue and Dawson have produced 3-D simulations of the 1999 storm that have a higher resolution than any previous investigations: 12.5 meters throughout the entire thunderstorm, comprising 500 million grid points. The simulations, which used over 1 million computing hours in 2008, showed the formation of a condensation funnel that looks very much like an actual tornado and illustrated tornado paths only a few kilometers from the observed paths.
[A paper describing an earlier set of simulations at 100 meter resolution was recently published by Xue and his graduate student, Nathan Snook, in Geophysical Research Letters (December 2008).]
Based on his research and most recent simulations, Xue has formed a new hypothesis about why tornadoes form. "Our theory says that the cloud microphysics affect a thunderstorm's cold pool, and the cold pool affects how the mid-level updraft and rotation are positioned relative to the low-level rotation,"
Xue explained. "We believe this relative position is a key factor affecting whether a thunderstorm can produce a tornado or not. It explains why some thunderstorms produce tornadoes, and others, though they may look very similar, don't." Xue's group at CAPS has been able to simulate tornadoes with unprecedented realism, especially using their new methods for assimilating high-resolution Doppler radar data and for synthesizing this information into 3-D visualizations of evolving storms.
However, to turn these methods into a predictive tool that can say where, when and how a twister will hit in real-time is still a distant dream. "The full simulations can take up to a week to complete, but the actual tornado forecast has to be done in a matter of minutes," Xue clarified. "To actually use the tools for real-time prediction, computers need to be much faster."
Furthermore, assessing the reliability of a prediction requires ‘ensemble' methods, where a simulation runs dozens of times to quantify the probability that a tornado will form and what track it will most likely take. Ensemble methods increase the computational workload by another order of magnitude.
Real-time, high-fidelity predictions will need to wait for the next-generation of HPC systems that can compute at the multi-petascale level and beyond (several times faster than today's fastest supercomputers).
But the field is developing quickly. Five years ago, a high-fidelity simulation capturing both the parent thunderstorm and an embedded tornado was impossible to produce. Five years hence, multiple simulations will be performed relatively quickly using tens of thousands of computer processors in tandem, so that many factors affecting the formation of tornadoes can be studied together. By using next-generation supercomputers, Xue believes it will be possible to resolve supercell thunderstorms in real-time with a resolution of about 250 meters — not tornado-resolving capacity, but enough to give a good indication of where, when and how a tornado may occur.
"High performance computers are essential for continued advances in weather prediction," said Jack Kain, a research meteorologist at the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory (NSSL). "This is especially true for high impact, small-scale phenomena like tornadoes. Xue's pioneering tornado simulations help us to better understand how tornadoes form and they provide fundamental insights about the computational resources and modeling framework that will be necessary if we are to achieve our eventual goal of real-time numerical prediction of tornadoes."
In the meantime, Xue is working on related research projects that will enhance real-time predictions for tornadoes and other weather systems in different ways. This year, a National Science Foundation (NSF)-supported field experiment (VORTEX-II) will deploy mobile radar and other observing systems to gather more precise data on tornadoes and tornadic thunderstorm valuable for simulation studies; Xue will provide real-time numerical weather prediction in support of that field experiment. Xue also leads the Analysis and Prediction Thrust of the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), a group that develops and tests adaptive sensors for best observing the atmosphere; his computer models will be used to tell the radars when and where to probe the thunderstorms.
Driving all this research are the powerful HPC systems at TACC. "Having access to large supercomputers like Ranger allows us to do simulations that were not possible before and to analyze the huge volume of data much faster," Xue said. "They make such advanced research possible."
March 4, 2009
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