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Home > iSGTW 9 July 2008 > iSGTW Feature - Taking the LEAD on adaptive weather forecasting

Feature - Taking the LEAD on adaptive weather forecasting


A sample comparison of a LEAD forecast of radar reflectivity to the observed radar. Reflectivity is a measure of the radio energy scattered back to the radar by precipitation. Large raindrops and hail have high reflectivity whereas drizzle and snow have low. The graph displays decibel units, dBZ (factors of 10), of reflectivity. "WRF" refers to Weather Research and Forecasting, a numerical weather prediction system.

Click to see a comparison of the LEAD forecasts from June 7, 2007 to the observed radar and the 4-km NMM Weather Research and Forecasting member from the ensemble (a collection of numerical forecasts with different parameter values that are valid at the same time for the same area). The forecasts were run on  the University of Indiana's Big Red TeraGrid resource.

Image courtesy of LEAD.

Watch LEAD's six and a half minute video, below, to learn how cyberinfrastructure helps meteorologists integrate signals from the atmosphere into simulations that generate weather forecasts. 

Each year across the United States, floods, tornadoes, hail, strong winds, lightning and winter storms—what meteorologists call mesoscale weather events—cause many deaths, routinely disrupt transportation and commerce, and result in annual economic losses over $13 billion. As weather conditions change rapidly and dramatically, it becomes difficult to disseminate timely severe weather warnings or to reroute air traffic to avoid costly delays.

To address this, the Linked Environments for Atmospheric Discovery (LEAD) project has created an interactive, adaptable system for accessing and utilizing meteorological data, forecast models, and analysis and visualization tools. Using a simple Web-based interface, LEAD brings together all the resources needed to do weather forecasts that adapt quickly as conditions change. Researchers, educators, students and meteorologists run complex workflows in minutes and hours rather than weeks using LEAD’s interconnected cyber-environment.

LEAD supported the Hazardous Weather Testbed in making forecasts over the eastern two-thirds of the U.S. using the Bigben Cray at the Pittsburgh Supercomputing Center and BigRed at the University of Indiana, both TeraGrid resources. Meteorologists from government agencies, academia and the private sector have been evaluating these forecasts on an experimental basis, studying the process of how real people make forecasts using these additional inputs.

“Although these forecasts are experimental and not part of the operational forecasting system, they will form the basis of how severe weather forecasting will be done in the future,” says Keith Brewster, LEAD HWT meteorologist. “We need to understand how to present the result of say, ten high-resolution forecasts in a human-usable way.”

LEAD also has collaborated with the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere in a project where adaptive weather prediction meets adaptive radar sensing to form a system that can flexibly respond to the changing weather.

- Karen Green, RENCI, and Anne Heavey, iSGTW


Video courtesy of RENCI.  

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