Title of Presentation: Global Cyclone Detection and Tracking using Multiple Remote Satellite Data
Primary (Corresponding) Author: Ashit Talukder
Organization of Primary Author: Jet Propulsion Lab
Co-Authors: Shen-Shyang Ho, Timothy Liu, Wendy Tang, Andrew Bingham, Eric Rigor
Abstract: Current techniques for cyclone detection and tracking employ NCEP (National Centers for Environmental Prediction) models from in-situ measurements. This solution does not provide 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.
In this NASA Applied Information Systems Research (AISR) funded project, 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 mining and data discovery from heterogeneous satellite data streams. We consider two remote sensor measurements in our current implementation, namely: the QuikSCAT wind satellite data, and the merged precipitation data using 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 automated approach compared to previous work. Results of our cyclone detection and tracking technology using our knowledge sharing approach is discussed and is compared with the list of cyclones reported by the National Hurricane Center for a specific year. The performance quality of our automated cyclone detection solution is found to closely match the manually created database of cyclones from the National Hurricane Center in our initial analysis.