Linked Environments for Atmospheric Discovery (LEAD) is a 5-year NSF Large Information Technology Research grant that has created an integrated web service architecture to support mesoscale meteorological data acquisition, analysis, assimilation, simulation modeling, prediction, mining and visualization. A unique component of LEAD is the operation of meteorological resources, and associated cyberinfrastructure, as dynamically adaptive, on-demand systems that can a) change configuration rapidly and automatically in response to weather; b) respond to decision-driven inputs from users; c) initiate other processes automatically; d) steer remote observing technologies, such as Doppler radars, to optimize data collection for the problem at hand; and e) provide the fault tolerance necessary to achieve required levels of performance.

The overarching objectives of LEAD are to:

• Lower the entry barrier for using, and make broadly available, several sophisticated meteorological tools and data services for graduate and undergraduate research and education;

• Improve our understanding of and ability to detect, analyze and predict mesoscale atmospheric phenomena by interacting with weather in a dynamically adaptive manner.

The LEAD web services framework – not unlike familiar resources such as Amazon.com, Travelocity.com – provides users with an almost endless set of capabilities, ranging from simply accessing real time or historical data and perhaps visualizing it, to running highly complex and linked data ingest, assimilation and forecast processes in real time and in a manner that adjusts dynamically to inputs as well as outputs.

As part of the IIPS special session on LEAD, we describe in this overview paper the latest capabilities of and application experiences with LEAD, the latter from the NOAA Hazardous Weather Test Bed Spring 2007 Experiment as well as Weather Challenge 2007. We also describe plans to deploy LEAD as a community facility as well as its extensibility for problems such as Earth system prediction and regional climate modeling.