Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees

Abstract

Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and quantitative way. This study develops a new model using WindSat data and a machine learning approach. Dynamic and hydrologic indices are quantified from WindSat wind and rainfall snapshot images over 352 developing and 973 non-developing tropical disturbances from 2005 to 2009. The degree of cyclonic circulation symmetry near the system center is quantified using circular variances, and the degree of strong wind aggregation (heavy rainfall) is defined using a spatial pattern analysis program tool called FRAGSTATS. In addition, the circulation strength and convection are defined based on the areal averages of wind speed and rainfall. An objective TC formation detection model is then developed by applying those indices to a machine-learning decision tree algorithm using calibration data from 2005 to 2007. Results suggest that the circulation symmetry and intensity are the most important parameters that characterize developing tropical disturbances. Despite inherent sampling issues associated with the polar orbiting satellite, a validation from 2008 to 2009 shows that the model produced a positive detection rate of approximately 95.3% and false alarm rate of 28.5%, which is comparable with the pre-existing objective methods based on cloud-pattern recognition. This study suggests that the quantitative microwave-sensed dynamic ocean surface wind pattern and intensity recognition model provides a new method of detecting TC formation.