A National Science Foundation Engineering Research Center, the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), was established recently to develop innovative observing systems for high-resolution sensing of the lower atmosphere. The development of low-cost, high-density, collaborative networks of Doppler radars with polarimetric capabilities is one of the first goals. Such networks are to probe the lower atmosphere that is often missed by the existing WSR-88D Doppler radar network, so as to significantly improve the detection of hazardous weather events such as tornadoes, and to provide more complete data for the initialization of numerical weather prediction models.

In close relation to the second goal, we describe in this paper the impact on assimilated data sets, and subsequent forecasts of an idealized convective storm, the radar beam geometry, scan strategy, and the location of convective storm relative to the radar network. A recently developed ensemble Kalman filter assimilation system is used for the assimilation, which represents a state-of-the-science technique of four-dimensional data assimilation.

The current work is also part of an NSF Large Information Technology Research project called the Linked Environments for Atmospheric Discovery (LEAD). Within this context, we will adapt our ensemble Kalman filter assimilation system to handle real time streaming data, and to produce real time assimilated data that in turn feed into the optimal control system for steering dynamically the adaptive observing systems of CASA.