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

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 fro...

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Bibliographic Details
Published in:Remote sensing of environment Vol. 183; pp. 205 - 214
Main Authors: Park, Myung-Sook, Kim, Minsang, Lee, Myong-In, Im, Jungho, Park, Seonyoung
Format: Journal Article
Language:English
Published: Elsevier Inc 15.09.2016
Subjects:
algorithms
artificial intelligence
Circulation
convection
Cyclones
decision support systems
Decision trees
Disturbances
Dynamic pattern and intensity recognition
Dynamics
Formations
hurricanes
hydrology
Machine learning
methodology
Microwave sea surface wind
Ocean surface
rain
Rainfall
remote sensing
satellites
Tropical cyclone
wind speed
Dynamic pattern and intensity recognition
Microwave sea surface wind
Tropical cyclone
Machine learning
ISSN:0034-4257, 1879-0704
Online Access:Get full text
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Summary: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. •A tropical cyclone genesis detection model is developed using remote sensing.•Dynamical and hydrologic factors are quantified from WindSat.•An objective model is constructed using decision trees algorithm.•The model shows the circulation symmetry and intensity are the most important.•Validation shows that the model produces high hit rate and low false alarm rate.
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ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2016.06.006