A fundamental function of any aviation weather system is to provide accurate and timely weather information tailored to the specific air traffic situations for which a system is designed. Weather location and intensity are of prime importance to such systems. Knowledge of the weather provides "nowcasting" functionality in the terminal and en route air spaces. It also is used as input into aviation weather forecasting applications for purposes such as storm tracking, storm growth and decay trends, and convective initiation. Weather radar products are the primary source of the weather location and intensity information used by the aviation weather systems. In the United States, the primary radar sources are the Terminal Doppler Weather Radar (TDWR) and the Weather Surveillance Radar 1988 Doppler (WSR-88D, known as NEXRAD). Additional weather radar products from the Canadian network are used by some of the aviation weather systems. Product quality from all these radars directly impacts the quality of the down stream products created by the aviation weather systems and their utility to air traffic controllers. Four FAA weather systems use some combination of products from the aforementioned radars. They are the Corridor Integrated Weather System (CIWS), the Integrated Terminal Weather System (ITWS), the Weather and Radar Processor (WARP), and the Medium Intensity Airport Weather System (MIAWS). This paper focuses on the improvement of weather radar data quality specific to CIWS. The other mentioned FAA aviation weather systems also benefit either directly or indirectly from the improvements noted in this paper. For CIWS, the legacy data quality practices involve two steps. Step one is the creation of weather radar products of highest possible fidelity. The second step involves creating a mosaic from these products. The mosaic creation process takes advantage of inter-radar product comparisons to interject a further level of improved data quality. The new CIWS data quality plan will use a mounting evidence data quality classifier technique currently being developed. The technique applies a multi-tiered approach to weather radar data quality. Its premise is that no single data quality improvement technique is as effective as a collaboration of many. The evidence will be expanded to include data and products from the radars along with data from additional sensing platforms. The mosaic creation process will correspondingly expand to take advantage of the additional evidence. Section 2 covers data quality of products from the single radar perspective. Section 3 focuses on the use of satellite data as the first additional sensing platform to augment removal of problematic radar contamination. Section 4 describes the data quality procedures associated with creation of mosaics from the single radar products augmented with new satellite masking information. Last, Section 5 discusses future plans for the mounting evidence data quality improvement technique.