Data Mining has enormous potential as a processing tool for meteorological data, because it provides an automated solution for extracting information from massive amounts of data. However, designing a useful data mining system for solving science problems is both complex and challenging. The key issue that needs to be addressed in the design of a mining framework is flexibility. The mining framework should be able to adapt to (1) handle variability of data sets typically found in meteorology, and (2) allow easy addition of new operations for extracting information depending on the problem. The ADaM (Algorithm Development and Mining) system, developed at the Information Technology and Systems Center at the University of Alabama in Huntsville, has been developed with these adaptive capabilities. This system provides the scientist with knowledge discovery, content-based searching and data mining capabilities for data values, as well as for metadata. It contains over 120 different operations which can be performed on the input data stream. The overall architecture of this system, design aspects that make this system flexible, and features that make ADaM ideal for mining meteorological data will be discussed in this paper. This paper will also describe some case studies to illustrate how scientists can construct mining plans.