Midwestern Climate and Weather Prediction
This science team brings data-driven machine learning approaches to short-term climate prediction and risk assessment in the Midwest.
This science team brings data-driven machine learning approaches to short-term climate prediction and risk assessment in the Midwest.
Our ultimate goal is to improve subseasonal to seasonal climate prediction in order to address the serious financial, health and property risks posed by weather and climate extremes. In the near-term, we will adapt and apply machine learning prediction
methods to analyze specific flood and heat wave events affecting the midwest. In the long-term, we will achieve:
rsriver@illinois.edu
University of Illinois at Urbana-Champaign: Associate Professor, Atmospheric Sciences
verahur@math.uiuc.edu
University of Illinois at Urbana-Champaign: Professor, Mathematics
zhizhenz@illinois.edu
University of Illinois at Urbana-Champaign: Assistant Professor, Electrical and Computer Engineering
libo@illinois.edu
University of Illinois at Urbana-Champaign: Professor, Statistics
Email: libo@illinois.edu
wuebbles@illinois.edu
University of Illinois at Urbana-Champaign: Professor, Atmospheric Sciences
Email: wuebbles@illinois.edu
PI Ryan Sriver, head of the Climate Dynamics and Variability Group at the University of Illinois at Urbana-Champaign, has used the Blue Waters supercomputer at NCSA to model the effect of changing climate on the frequency and power of hurricanes. As an expert in the intersection between climate and weather, Sriver will will lead this science team in project activities including observational data analysis, statistical modeling and development of risk-based metrics.