Predicting what the weather will be like tomorrow is a difficult task. There are millions of variables and measurements for computer models to interpret, combine, and consider. Weather systems are complex because they occur on a global scale. Dr. Daescu has devoted his career to developing applied and computational mathematics that allows atmospheric scientists and climatologists to understand weather prediction issues, air quality forecasts, and climate change. Dr. Daescu's interest in applying mathematics to solve these challenging issues trace back to his doctoral studies at the University of Iowa. Dr. Daescu continued his research as a Postdoctoral Associate at the University of Minnesota before joining the research faculty in the Department of Mathematics here at PSU in 2003. Dr. Daescu's research has been published in journal articles, and he is the author of several book chapters; he has also presented his research at conferences around the world.
Atmospheric data, everything from patterns in the jet stream to the ocean's surface temperatures and land, to the amount of CO2 in the atmosphere, is collected by weather satellites in orbit, high tech weather balloons, and by thousands of observation sites on the ground. These technologies provide scientists with a deluge of information that they then plug into models to help them make short term predictions about weather patterns. Dr. Daescu's work allows models to optimize that deluge of data, improving the quality of weather and air pollution forecasts.
"Because atmospheric measurements are not homogenously distributed," Dr. Daescu said, "and because we're not able to capture all the complexity of weather systems, errors happen in modeling. Minimizing those errors is one of the most challenging problems in applied mathematics. Challenging because we're working with dynamic, physical processes. The computational methods I've developed have helped improve the predictive capabilities of weather models."
Dr. Daescu's research and innovations have led to collaborations, research partnerships, grants, and the implementation of his work in operational data assimilation and forecast systems with organizations such as NASA, the Naval Research Laboratory, the European Center for Medium-Range Weather Forecasts, the NSF, and Intel Inc. While Dr. Daescu's work focuses on weather prediction, climate change, and air quality prediction, he notes that there are many other applications such as guidance systems or even the stock market.
"The mathematical computations are very general," Dr. Daescu said. "But my focus is on atmospheric applications. That's the nature of the work. I think it's interesting, working with these very complex real-life systems. And working with this kind of mathematics and these kinds of models I get to see right away if the math is working or not, which helps me to improve the quality of the models continuously."
From the time of Benjamin Franklin to that of Al Roker, the technologies have changed immensely, and yet hardly a day goes by when someone doesn't complain about the meteorologist making a wrong call. But the truth is global systems like weather are incomprehensively complex, and even with modern tools such as satellite technology, high tech weather balloons, and weather monitoring stations, there is some level of uncertainty introduced into the models used to predict the weather. Dr. Dacian Daescu is at the forefront of mathematics that improves the way models predict the weather and forecast air quality. In the future, Dr. Daescu's research may be involved in saving lives by helping news organizations and national organization like the National Weather Service more accurately predict the paths of major storms, track atmospheric conditions that could spread fires or lead to floods, or monitor the quality of the air we breathe, not to mention help meteorologists win back some of the public's affection.