Current techniques for cyclone detection and tracking employ NCEP (National Centers for Environmental Prediction) models from in-situ measurements. This solution does not provide true global coverage, unlike remote satellite observations. However it is impractical to use a single Earth orbiting satellite to detect and track events such as cyclones in a continuous manner due to limited spatial and temporal coverage. One solution to alleviate such persistent problems is to utilize heterogeneous sensor data from multiple orbiting satellites. However, this solution requires overcoming other new challenges such as varying spatial and temporal resolution between satellite sensor data, the need to establish correspondence between features from different satellite sensors, and the lack of definitive indicators for cyclone events in some sensor data. We describe an automated cyclone discovery and tracking approach using heterogeneous near real-time sensor data from multiple satellites. This approach addresses the unique challenges associated with knowledge discovery and mining from heterogeneous satellite data streams. We consider two remote sensor measurements in our current implementation, namely: QuikSCAT wind satellite measurements, and merged precipitation data from TRMM and other satellites. More satellites will be incorporated in the near future and our solution is sufficiently powerful that it generalizes to multiple sensor measurement modalities. Our approach consists of three main components: (i) feature extraction from each sensor measurement, (ii) an ensemble classifier for cyclone discovery, and (iii) knowledge sharing between the different remote sensor measurements based on a linear Kalman filter for predictive cyclone tracking. Experimental results on historical hurricane datasets demonstrate the superior performance of our approach compared to previous work.