Objective estimation of tropical cyclone innercore surface wind structure using infrared satellite images

Changjiang Zhang; Lijie Dai; Leiming Ma; Jinfang Qian; Bo Yang

doi:10.1117/1.JRS.11.046030

27 December 2017 Objective estimation of tropical cyclone innercore surface wind structure using infrared satellite images

Changjiang Zhang, Lijie Dai, Leiming Ma, Jinfang Qian, Bo Yang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046030 (December 2017). https://doi.org/10.1117/1.JRS.11.046030

Abstract

An objective technique is presented for estimating tropical cyclone (TC) innercore two-dimensional (2-D) surface wind field structure using infrared satellite imagery and machine learning. For a TC with eye, the eye contour is first segmented by a geodesic active contour model, based on which the eye circumference is obtained as the TC eye size. A mathematical model is then established between the eye size and the radius of maximum wind obtained from the past official TC report to derive the 2-D surface wind field within the TC eye. Meanwhile, the composite information about the latitude of TC center, surface maximum wind speed, TC age, and critical wind radii of 34- and 50-kt winds can be combined to build another mathematical model for deriving the innercore wind structure. After that, least squares support vector machine (LSSVM), radial basis function neural network (RBFNN), and linear regression are introduced, respectively, in the two mathematical models, which are then tested with sensitivity experiments on real TC cases. Verification shows that the innercore 2-D surface wind field structure estimated by LSSVM is better than that of RBFNN and linear regression.

Citation Download Citation

Changjiang Zhang, Lijie Dai, Leiming Ma, Jinfang Qian, and Bo Yang "Objective estimation of tropical cyclone innercore surface wind structure using infrared satellite images," Journal of Applied Remote Sensing 11(4), 046030 (27 December 2017). https://doi.org/10.1117/1.JRS.11.046030

Received: 10 April 2017; Accepted: 1 December 2017; Published: 27 December 2017

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