Development of a python-based visualisation infrastructure for the DAV set of modules for pre-genesis tracking, estimation and prediction of tropical cyclone genesis, intensity, and structure.
Abstract
SUMMARYApproved for Public ReleaseTropical cyclones (TCs) spend the majority of their lives over the vast tropical oceans where traditional in situ observations are sparse. In these regions, satellite-based remote-sensing instruments are central to determining the current structure and intensity of TCs. However, most of the observations provided by these instruments do not directly relate to the basic-state variables used to characterize TC structure and intensity or to initialize them in numerical weather prediction models. The deviation angle variance (DAV) technique was developed to mitigate this need. By objectively analysing the structure of cloud clusters relative to that of an ideal intense axisymmetric tropical cyclone, potential cloud clusters can be tracked, the likelihood of genesis, current and short-term future intensity, and surface wind structure can be determined. These basic measurements are vital both to provide consistent datasets for fundamental research and for operations.In this project we propose to develop an integrated python-based visualisation infrastructure package for global functionality of the DAV-based tropical cyclone genesis, intensityand wind field structure modules. At the same time, we will develop and integrate the real-time infrastructure that will enable DAVto be integrated with SATCON, the CIRA wind radii consensus suite and the new NRL GeoIPS system. The focus will be on developing individual modules to achieve each task to ensure the final product is easily integrated into other systems. To calibrate and validatethe algorithms we will process 5 years of global satellite imagery in all tropical cyclone basins and develop their real-time capability. Finally, we will undertake real-time testing of the system in year 3 during the northern hemisphere tropical cyclone season.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 11, 2023
- Source ID
- N000142312628
Entities
People
- Elizabeth Ritchie Tyo
Organizations
- Monash University
- Office of Naval Research
- United States Navy