NSF Career Award for UD Alum
Alum receives NSF Career Award for drought prediction research
9:41 a.m., March 17, 2011----University of Delaware alumnus Steven Quiring has been named a recipient of the prestigious Faculty Early Career Development Award from the National Science Foundation (NSF). The highly competitive award is bestowed on those scientists deemed most likely to become the academic leaders of the 21st century.
Reviresco June run
The five-year, $486,000 grant will support his research program on drought and the role of land-surface processes in the Great Plains, which stretch from Mexico to Canada across the center of the United States.
“A lot of things that go on in the atmosphere in terms of weather and climate are controlled by the interactions of the atmosphere with the land surface that's underneath it,” Quiring explained.
One example of this process is when water is cycled through the earth's atmospheric system, and in this case it's the water in the surface soil that's in question. Quiring wants to know what controls the fluxes of water from the surface to the atmosphere in order to understand precipitation processes, especially drought.
“The whole motivation behind this project is developing a better understanding of why and where droughts occur and the role of these land-atmosphere interactions that are modulated by soil moisture,” he said.
Quiring's NSF project focuses on creating a unified database of existing soil moisture observations made across the U.S. Great Plains. Making the soil moisture data uniform and available in one location will help experts develop better algorithms that can more accurately estimate soil moisture using satellites and global climate models.
The project is expected to help further refine seasonal climate forecasts for the United States as well as similar mid-latitude regions around the world. Not only will the project help the climate modeling community, it will benefit water resource managers, those in the agricultural industry, and anyone who needs to understand or predict seasonal climate.
Article by Elizabeth Boyle