Education and Outreach Within the Modeling Environment for Atmospheric Discovery (MEAD) Project Daniel J. Bramer University Of Illinois at Urbana-Champaign Scott Lathrop Robert Wilhelmson National Center for Supercomputing Applications Steve Gordon Ohio Supercomputing Center Susan Ragan Maryland Virtual High School Bob Panoff Garrett Love Shodor Education Foundation

What is MEAD?? The goal of the MEAD expedition is the development and adaptation of Grid and TeraGrid-enabled cyberinfrastructure for mesoscale storm and hurricane research and education. Portal Grid and Web infrastructure will enable launching of hundreds of individual Weather Research and Forecasting (WRF), Regional Ocean Modeling System (ROMS), or coupled WRF/ROMS simulations in either ensemble or parameter mode. Discovery and use metadata coupled to the resulting terabytes of data will then be made available to enable further exploration. Thus, a user of the MEAD workflow will be able to configure and integrate model simulations, manage resulting model and derived data, and analyze, mine, and visualize large model data suites in a research (not predictive) context. Finally, very large domain research fault- tolerant simulations will be enabled through decomposition techniques that can be utilized efficiently on the new TeraGrid architecture. The resulting environment serves both as an example for other research efforts and a cyberinfrastructure proving ground.

MEAD Lite  Develop / adapt Grid and TeraGrid-enabled cyberinfrastructure (CI) for mesoscale storm and hurricane research and education.  You can:  Configure, integrate, and launch 100’s of model simulations  manage resulting model and derived data  analyze, mine, and visualize large model data  And more…

Why Education??  Many do not know understand the benefits of ensemble model generation.  MEAD can do this efficiently, but no one will use it unless they understand how it can help them.

MEAD Education Topics  MEAD Education Group Goal: Create models and examples to teach the value of the following topics  Parameter Space (different kinds of storms form in different atmospheric environments)  Uncertainty (small changes in model initial conditions and physical process representations lead to different results)  Prediction (determining probabilities bases on a collection of simulation results)

The Education Group  Scott Lathrop, NCSA-EOT  Susan Ragan, MVHS  Hurricane Model in STELLA  Steve Gordon, OSC  Hurricane Floyd Models  Bob Panoff and Garrett Love, Shodor  Connection Models  Daniel Bramer, UIUC  Balloon Model  Robert Wilhelmson, NCSA

Hurricane Model in STELLA  What?  Help students learn about hurricane path and strength by giving them a simplified model to play with.  Why?  Many factors play a role in how a hurricane develops as well as where it goes.

Hurricane Model in STELLA  Hurricane Model  Investigate the role of sea surface temperature and location of landfall  Note the uncertainty of hurricane paths  Same run may produce different results the second time.  Operate a real model

Hurricane Inland Flood Model  What?  Help students use real data (Floyd, ‘99) to forecast flooding conditions and estimate flood damage.  Why?  Inland floding is often a larger problem than wind damage and is also more difficult to predict (storm path, time exposure, response of complex watersheds).

Hurricane Inland Flood Model  Inland Flood Model  Forecast streamflow with respect to weather and relate to gage height  Use real data  Experience with statistical techniques  Show forecasting difficulty for complex environmental events

Connection Models  What?  Connection Models can help introduce students to computational science topics by starting with simple hands-on experiments.  Why?  It can be difficult to introduce students to the world of computational science.

Connection Models  Foam-disc Shooter  Relatable to hurricanes  See how predictability and uncertainty vary with distance  Fun!  Hook to get to more computational topics

Balloon Model  What?  Help students see the usefulness of running multiple simulations by allowing them to release balloons into the atmosphere and view the results.  Why?  The variance in flight paths of balloons is a good allegory to the variance weather system movement.

Balloon Model  Balloon Model  Path variance is similar to hurricane path forecasts  Compare balloon paths to ‘errors’ in the model.  Relate uncertainty in balloon travel to uncertainty in forecasting weather systems.  Predict next balloon’s location.

How does this help??  Many different ways to teach students about importance of uncertainty, predictability, and parameter space.  Leads to discussion of needing to run a model more than once to help get a feel for the potential possibilities.

Take Note  MEAD website  See Also  Wilhelmson, et. al MEAD (A Modeling Environment for Atmospheric Discovery)  IIPS Paper 6.2