红外云图的台风内核风速建模的RBFNN和PDE方法

钱金芳; 张长江; 杨波; 马雷鸣

红外与激光工程, 2015, 44 (2): 0438, 网络出版: 2016-01-26

红外云图的台风内核风速建模的RBFNN和PDE方法

Typhoon inner core wind speed modeling method by RBFNN and PDE based on infrared cloud image

论文大纲

钱金芳 ^1,*张长江 ¹杨波 ¹马雷鸣 ²

作者单位

¹ 浙江师范大学数理与信息工程学院，浙江金华 321004

² 上海市浦东新区气象局，上海 200135

建模偏微分方程径向基函数神经网络台风云图 model partial differential equations radial basis function neural network typhoon cloud image

摘要

：目前反演台风内核风场时多采用线性回归方法进行建模，针对基于线性回归法的台风内核风速拟合效果较差的缺点，提出一种基于径向基函数神经网络(RBFNN)和偏微分方程(PDE)结合的红外卫星云图有眼台风内核风速和云图灰度建模方法。首先采用基于测地活动轮廓模型的PDE提取有眼台风的眼壁，获得台风眼壁空间位置和亮度数据；然后结合台风年鉴给出的台风近中心最大风速数据基于RBFNN进行有眼台风内核风速和云图灰度建模。实验结果表明：该算法改善了台风内核风速拟合效果，算法性能优于传统的线性回归法。

Abstract

At present, linear regression model is often used to estimate typhoon inner core wind field. But the fitting effect of typhoon inner core wind speed based on linear regression was bad. Based on infrared satellite cloud image, radial basis function neural network(RBFNN) and partial differential equation(PDE) were used to build a model between typhoon inner core speed and cloud image′s gray value. Firstly, typhoon′s eye wall was extracted by using PDE which based on geodesic active contour model from the infrared satellite cloud image and the eye wall′s space position and brightness are obtained. Then the maximum wind speed near typhoon center which was recorded by typhoon yearbook was used to build a model between typhoon inner core′s speed and cloud image′s gray value by RBFNN. The experimental results show that the proposed algorithm improves the fitting effect of typhoon inner core′s wind speed, and the overall performance of the proposed algorithm is better than tradition method of linear regression.

参考文献

[1] Dvorak V F. Tropical cyclone intensity analysis and forecasting from satellite imagery[J]. Monthly Weather Review, 1975, 103(5): 420-430.

[2] 王瑾，江吉喜. 热带气旋强度的卫星探测客观估计方法研究[J]. 应用气象学报, 2005, 16(13): 283-292.

Wang Jin, Jiang Jixi. An objective technique for estimating tropical cyclone intensity from geostationary meteorological satellite observation[J]. Journal of Applied Meteorological Science, 2005, 16(3): 283-292. (in Chinese)

[3] Kossin J P, Knaff J A, Berger H I, et al. Estimating hurricane wind structure in the absence of aircraft reconnaissance[J]. Weather and Forecasting, 2007, 22: 89-101.

[4] Lu Xiaoqin, Lei Xiaotu, Yu Hui, et al. An objective TC intensity estimation method based on satellite data[J]. Journal of Applied Meteorological Science, 2014, 25(1): 52-58. (in Chinese)

[5] Gholamreza F, Abdollah H. Objective tropical cyclone intensity estimation using analogs of spatial features in satellite data[J]. Weather and Forecasting, 2013, 28(6):1446-1459.

[6] Miguel F P, Elizabeth A R, Tyo J S. Estimating tropical cyclone intensity from infrared image data[J]. Weather and Forecasting, 2011, 26: 690-698.

[7] 陈希, 李妍, 毛科峰, 等. 基于非线性最优目标函数的红外云图台风中心自动定位[J]. 热带气象学报，2013, 29(1): 155-160.

Cheng Xi, Li Yan, Mao Kefeng, et al. Infrared cloud image typhoon center automatic position based on nonlinear optimal objective function[J]. Journal of Tropical Meteorology, 2013, 29(1): 155-160. (in Chinese)

[8] Kass M, Witriw A, Terzopoulos D. Snakes: active contour models[C]//In the first International Conference on Computer Vision, 1998, 1: 321-369.

[9] Osher S, Sethian J. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations[J]. Journal of Computational Physics, 1988, 79: 12-49.

[10] Caselles V, Kimmel R, Sapiro G. Geodesic active contour[J]. The International Journal of Computer Vision, 1997, 22(1): 61-79.

钱金芳, 张长江, 杨波, 马雷鸣. 红外云图的台风内核风速建模的RBFNN和PDE方法[J]. 红外与激光工程, 2015, 44(2): 0438. Qian Jinfang, Zhang Changjiang, Yang Bo, Ma Leiming. Typhoon inner core wind speed modeling method by RBFNN and PDE based on infrared cloud image[J]. Infrared and Laser Engineering, 2015, 44(2): 0438.

红外云图的台风内核风速建模的RBFNN和PDE方法

关于本站 Cookie 的使用提示

全站搜索

红外云图的台风内核风速建模的RBFNN和PDE方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索