Today's research and operational forecast models and data assimilation systems have difficulty ingesting and utilizing large volumes of satellite data, in part due to prohibitively large computational costs, time constraints and bandwidth issues. To address this problem, a NASA-funded project aimed at refining, testing and customizing an existing automated Intelligent Data Thinning (IDT) algorithm developed at the University of Alabama in Huntsville (UAH) in conjunction with commonly used data assimilation systems for numerical weather prediction models. The most significant measure of a successful data reduction algorithm is its ability to retain valuable information--that which has maximum impact on the model forecast--while simultaneously reducing the data volume. The IDT algorithm is specifically designed to retain information dense regions of a data set while removing redundant data. This recursive simplification algorithm is based on the computer graphics concept of data decimation and retains data within regions of high spatial frequency (large variances) while subsampling regions of low spatial frequency (low variances) to thin the data. The goal of this project is to test, refine and customize the IDT algorithm in support of real-time applications to dense data streams in operational communities. Comparisons of the IDT algorithm with conventional thinning approaches are presented using selected synthetic data sets and various assimilation methodologies. IDT is also examined in the context of real data with applications to MODerate resolution Imaging Spectroradiometer (MODIS) sea surface temperatures and Atmospheric InfraRed Sounder (AIRS) temperature profiles.