Objective, Automatic Tracking of Pre-Genesis Tropical Disturbances within the Deviation Angle Variance Method

The deviation angle variance (DAV) method is an objective tool for estimating the intensity of tropical cyclones (TCs) using geostationary infrared (IR) brightness temperature data. At early stages in TC development, the DAV signal can also be a robust predictor of tropical cyclogenesis. However, one of the problems with using the DAV method at these early stages is that the operator has to subjectively track potentially developing cloud systems, sometimes before they are clearly identifiable. Here we present a method that automatically tracks cloud clusters using only the raw IR imagery and the resulting DAV maps. The method uses the DAV map and IR satellite image to identify potentially developing cloud clusters. Low DAV values are associated with highly symmetric IR regions where a disturbance might be forming. These regions are identified by the method and then checked for cloud presence in the IR image. Regions with DAV values below a pre-determined threshold (from our previous DAV-based work on cyclogenesis) and cloud presence are included in a table of detects, which is used to keep track of the identified disturbances in subsequent satellite images. The method includes measures to keep track of disturbances that vary considerably during their evolution. Furthermore, a number of thresholds (chosen from typical parameters of TCs) are used to distinguish between developing disturbances and cloud clusters that might be identified by the tracking system as potentially developing cloud clusters but later dissipate without becoming TCs.

In this poster we will show results of the performance of our objective method obtained by cross-checking the tracking results with detailed results obtained manually by expert operators on a limited data set spanning a 12-day period during the 2010 typhoon season in the western North Pacific. In addition, the results of a longer-term comparison between the automated system and the JTWC best-track and invest databases from 2009-2012 will be presented.