Dynamic, Adaptive Workflows for Mesoscale Meteorology

Abstract

The Linked Environments for Atmospheric Discovery (LEAD) [122] is a National Science Foundation funded¹ project to change the paradigm for mesoscale weather prediction from one of static, fixed-schedule computational forecasts to one that is adaptive and driven by weather events. It is a collaboration of eight institutions,² led by Kelvin Droegemeier of the University of Oklahoma, with the goal of enabling far more accurate and timely predictions of tornadoes and hurricanes than previously considered possible. The traditional approach to weather prediction is a four-phase activity. In the first phase, data from sensors are collected. The sensors include ground instruments such as humidity and temperature detectors, and lightning strike detectors and atmospheric measurements taken from balloons, commercial aircraft, radars, and satellites. The second phase is data assimilation, in which the gathered data are merged together into a set of consistent initial and boundary conditions for a large simulation. The third phase is the weather prediction, which applies numerical equations to measured conditions in order to project future weather conditions. The final phase is the generation of visual images of the processed data products that are analyzed to make predictions. Each phase of activity is performed by one or more application components.

LEAD is funded by the National Science Foundation under the following Cooperative Agreements: ATM-0331594 (Oklahoma), ATM-0331591 (Colorado State), ATM-0331574 (Millersville), ATM-0331480 (Indiana), ATM0331579 (Alabama in Huntsville), ATM03-31586 (Howard), ATM-0331587 (UCAR), and ATM-0331578 (Illinois at Urbana-Champaign).

University of Oklahoma, Indiana University, University of Illinois at Urbana-Champaign, University Corporation for Atmospheric Research (UCAR), University of Alabama in Huntsville, University of North Carolina, Howard University, and Colorado State University.