Automated Historical and Real-Time Cyclone Discovery With Multimodal Remote Satellite Measurements

Existing cyclone detection and tracking solutions involve extensive manual analysis of modeled-data and field campaign data by teams of experts. We have developed a novel automated global cyclone detection and tracking system by assimilating and sharing information from multiple remote satellites. This unprecedented solution of combining multiple remote satellite measurements in an autonomous manner allows leveraging off the strengths of each individual satellite. Use of multiple satellite data sources also results in significantly improved temporal tracking accuracy for cyclones. Our solution involves an automated feature extraction and machine learning technique based on an ensemble classifier and Kalman filter for cyclone detection and tracking from multiple heterogeneous satellite data sources. Our feature-based methodology that focuses on automated cyclone discovery is fundamentally different from, and actually complements, the well-known Dvorak technique for cyclone intensity estimation (that often relies on manual detection of cyclonic regions) from field and remote data. Our solution currently employs the QuikSCAT wind measurement and the merged level 3 TRMM precipitation data for automated cyclone discovery. Assimilation of other types of remote measurements is ongoing and planned in the near future. Experimental results of our automated solution on historical cyclone datasets demonstrate the superior performance of our automated approach compared to previous work. Performance of our detection solution compares favorably against the list of cyclones occurring in North Atlantic Ocean for the 2005 calendar year reported by the National Hurricane Center (NHC) in our initial analysis. We have also demonstrated the robustness of our cyclone tracking methodology in other regions over the world by using multiple heterogeneous satellite data for detection and tracking of three arbitrary historical cyclones in other regions. Our cyclone detection and tracking methodology can be applied to (i) historical data to support Earth scientists in climate modeling, cyclonic-climate interactions, and obtain a better understanding of the cause and effects of cyclone (e.g. cyclo-genesis), and (ii) automatic cyclone discovery in near real-time using streaming satellite to support and improve the planning of global cyclone field campaigns. Additional satellite data from GOES and other orbiting satellites can be easily assimilated and integrated into our automated cyclone detection and tracking module to improve the temporal tracking accuracy of cyclones down to ½ hr and reduce the incidence of false alarms.