سابِق|تال

Eyed tropical cyclone intensity objective estimation model based on infrared satellite image and relevance vector machine

  • role： First author第一作者
  • Affiliation：

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China

  • Email：dlj16823@163.com
  • Introduction：戴李杰，1990年生，男，硕士研究生，研究方向为图像处理及应用、机器学习与模式识别。E-mail： dlj16823@163.com
Lijie DAI1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China

  • Email：zcj74922@zjnu.edu.cn
  • Introduction：张长江，1974年生，男，教授，研究方向为图像处理、机器学习、多尺度几何分析与应用。E-mail： zcj74922@zjnu.edu.cn
Changjiang ZHANG1*,  
  • Affiliation：

    College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, China

Licheng XUE1,  
  • Affiliation：

    Shanghai Central Meteorological Observatory, Shanghai Meteorological Bureau, Shanghai 200030, China

Leiming MA2,  
  • Affiliation：

    Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030, China

Xiaoqin LU3

ملخص

A Tropical Cyclone (TC) is an influential natural hazard in the world. Thus, the forecasting accuracy of intensity and track is important to reduce the impacts of this disaster. In recent years, a remote sensing satellite cloud image gradually becomes the main means of monitoring a TC. In this study, an eyed TC intensity objective estimation model based on geostationary infrared satellite cloud image and Relevance Vector Machine (RVM) is proposed. First, the infrared satellite cloud image is denoised through a Gaussian smoothing method. Second, a TC eye area is segmented using a Partial Differential Equation (PDE) based on a Geodesic Active Contour (GAC) model. Third, the brightness temperature gradient data of the eyewall are obtained, and the maximum of brightness and different mean brightness temperature gradients with various probabilities are calculated. Thus, the characterization factors that are closely related to the TC intensity are structured. Finally, the TC intensity objective estimation model based on the RVM is established (single and multiple characterization factors) to study the influence of different characteristic dimensions on a TC intensity estimation error. Experimental results show that the estimation model that is established using the mean brightness temperature gradient of the TC eyewall with a probability of 95% performs better than the other models in studying intensity estimation model that is established using a single characterization factor. The error of the intensity estimation model established using multiple characterization factors is lower than that of the single characterization factor. That is, the multiple characterization factors contain additional characteristic information that is related to the TC intensity. In the study of the intensity estimation model established using multiple characterization factors, the estimation model performs better in the two characterization factors (the maximum of brightness and mean brightness temperature gradients with a probability of 95%) than in the three characterization factors. Therefore, not all “more” is better. Hence, the dimension of the characterization factor should be reasonably selected. Three types of RVM kernel functions are used in the experiments. Results show that all RVM kernel functions are efficient in estimating the TC intensity. Cauchy kernel function is suitable for the estimation model established using multiple characterization factors. The RVM model proposed in this study has a favorable high-dimensional nonlinear processing capability. Therefore, this model can effectively estimate the TC intensity.

مفهوم

tropical cyclone;infrared satellite image;image segmentation;relevance vector machine;machine learning;intensity objective estimation

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