Using Enabling Technologies to Advance Data Intensive Analysis Tools in the JPL Tropical Cyclone Information System

Tuesday, 16 December 2014
Brian Knosp, Michael E Gangl, Svetla M Hristova-Veleva, Richard M Kim, Bjorn Lambrigtsen, Peggy Li, Noppasin Niamsuwan, Tsae-Pyng J Shen, Francis J Turk and Quoc A Vu, Jet Propulsion Laboratory, Pasadena, CA, United States
The JPL Tropical Cyclone Information System (TCIS) brings together satellite, aircraft, and model forecast data from several NASA, NOAA, and other data centers to assist researchers in comparing and analyzing data related to tropical cyclones. The TCIS has been supporting specific science field campaigns, such as the Genesis and Rapid Intensification Processes (GRIP) campaign and the Hurricane and Severe Storm Sentinel (HS3) campaign, by creating near real-time (NRT) data visualization portals. These portals are intended to assist in mission planning, enhance the understanding of current physical processes, and improve model data by comparing it to satellite and aircraft observations.

The TCIS NRT portals allow the user to view plots on a Google Earth interface. To compliment these visualizations, the team has been working on developing data analysis tools to let the user actively interrogate areas of Level 2 swath and two-dimensional plots they see on their screen. As expected, these observation and model data are quite voluminous and bottlenecks in the system architecture can occur when the databases try to run geospatial searches for data files that need to be read by the tools.

To improve the responsiveness of the data analysis tools, the TCIS team has been conducting studies on how to best store Level 2 swath footprints and run sub-second geospatial searches to discover data. The first objective was to improve the sampling accuracy of the footprints being stored in the TCIS database by comparing the Java-based NASA PO.DAAC Level 2 Swath Generator with a TCIS Python swath generator. The second objective was to compare the performance of four database implementations – MySQL, MySQL+Solr, MongoDB, and PostgreSQL – to see which database management system would yield the best geospatial query and storage performance. The final objective was to integrate our chosen technologies with our Joint Probability Density Function (Joint PDF), Wave Number Analysis, and Automated Rotational Center Hurricane Eye Retrieval (ARCHER) tools.

In this presentation, we will compare the enabling technologies we tested and discuss which ones we selected for integration into the TCIS’ data analysis tool architecture. We will also show how these techniques have been automated to provide access to NRT data through our analysis tools.