Tropical cyclone intensity objective determination method based on satellite cloud chart and RVM

Abstract

The invention provides a tropical cyclone intensity objective determination method based on a satellite cloud chart and an RVM. The method is used for constructing a tropical cyclone (TC) intensity objective determination model based on the satellite cloud chart and the RVM (relevance vector machine). The method mainly comprises the following two aspects: 1) carrying out fusion on infrared and water vapor channel cloud charts by utilizing a Laplacian pyramid algorithm, constructing a deviation angle-gradient co-occurrence matrix with the TC center as a reference point, constructing characteristic factors closely related to TC intensity by utilizing a plurality of statistical parameters in the co-occurrence matrix and information of TC kernel scale and center latitude and the like, and establishing the TC intensity objective determination model by utilizing the RVM; and 2) based on the fused satellite cloud chart, and with each point as the reference point in sequence, constructing a deviation angle-gradient co-occurrence matrix and calculating a minimum value, a median value and a mean value of a co-occurrence matrix statistical parameter array. The method constructs the characteristic factors closely related to TC intensity by utilizing the plurality of statistical parameters of the co-occurrence matrix parameter array and information of TC kernel scale and center latitude and the like, and establishes the TC intensity objective determination model by utilizing the RVM.

Description

technical field [0001] The invention belongs to the field of image processing technology and meteorological prediction. Specifically, it involves an objective method for determining the intensity of tropical cyclones based on satellite cloud images and Relevance Vector Machine (RVM) for the purpose of improving the accuracy of determining the intensity of tropical cyclones. Background technique [0002] Tropical Cyclone (TC) is a common disaster among many natural disasters that harm China. Its activities are accompanied by strong winds, heavy rains and storm surges, and even cause natural geological disasters such as landslides and mud-rock flows. Accurate forecasting of TC's intensity and path is crucial to preventing and mitigating its impact. [0003] In recent years, the development of various observation methods and numerical forecasting techniques at home and abroad has promoted the continuous improvement of the level of TC path forecasting, but the forecasting abili...

AI Technical Summary

Problems solved by technology

However, there is still room for improvement in three aspects of this type of method. First, the analysis area of ​​existing methods is fixed as the kernel area with a fixed radius around the center of the TC, but the scale of the kernel area of ​​different TCs varies. The size of the kernel will inevitably affect the accuracy and scope of application of the fixed strength

Second, the strength determination technology uses linear regression for modeling, and generally speaking, there is a complex nonlinear relationship between TC strength and its influencing factors. Simple linear regression modeling cannot well construct the relationship between TC strength and its influencing factors. Nonlinear Relationship Between Impact Factors

Third, among the existing studies, there are few simultaneous effects of central latitude on TC intensity

Relevant studies have shown that the latitude position of the TC center has a great influence on the intensity of the TC, and only considering the brightness temperature data will lead to a certain error in determining the intensity of the TC

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