Automatic Tracking and Characterization of Cumulonimbus Clouds from FY-2C Geostationary Meteorological Satellite Images

Yu Liu,Du-Gang Xi,Zhao-Liang Li,Chun-Xiang Shi

doi:10.1155/2014/478419

Abstract

This paper presents an automated method to track cumulonimbus (Cb) clouds based on cloud classification and characterizes Cb behavior from FengYun-2C (FY-2C). First, a seeded region growing (SRG) algorithm is used with artificial neural network (ANN) cloud classification as preprocessing to identify consistent homogeneous Cb patches from infrared images. Second, a cross-correlation-based approach is used to track Cb patches within an image sequence. Third, 7 pixel parameters and 19 cloud patch parameters of Cb are derived. To assess the performance of the proposed method, 8 cases exhibiting different life stages and the temporal evolution of a single case are analyzed. The results show that (1) the proposed method is capable of locating and tracking Cb until dissipation and can account for the eventual splitting or merging of clouds; (2) compared to traditional brightness temperature (TB) thresholds-based cloud tracking methods, the proposed method reduces the uncertainty stemming from TB thresholds by classifying clouds with multichannel data in an advanced manner; and (3) the configuration and developmental stages of Cb that the method identifies are close to reality, suggesting that the characterization of Cb can provide detailed insight into the study of the motion and development of thunderstorms.

Highlights

In the tropics and midlatitudes, cumulonimbus (Cb) clouds are associated with intense convection and severe weather such as wind gusts, heavy precipitation, lightning, and eventually hail, microbursts, and tornadoes
The results show that (1) the proposed method is capable of locating and tracking Cb until dissipation and can account for the eventual splitting or merging of clouds; (2) compared to traditional brightness temperature (TB) thresholds-based cloud tracking methods, the proposed method reduces the uncertainty stemming from temperature of Cb pixel (TB) thresholds by classifying clouds with multichannel data in an advanced manner; and (3) the configuration and developmental stages of Cb that the method identifies are close to reality, suggesting that the characterization of Cb can provide detailed insight into the study of the motion and development of thunderstorms
Cloud patch in the current image Other cloud patches in the current image Cloud patch in the previous time step image Other cloud patches in the previous time step image (h) evolution indicating consistent tracking

Summary

Introduction

In the tropics and midlatitudes, cumulonimbus (Cb) clouds are associated with intense convection and severe weather such as wind gusts, heavy precipitation, lightning, and eventually hail, microbursts, and tornadoes. Their presence may pose a serious risk to aviation and may impact crops and urban populations because rapidly changing weather on various spatial and temporal scales may occur within and near Cb clouds. Geostationary satellite images have been proven to be an important source of observations of dynamic weather events They are especially useful for convective cloud tracking, thanks to their high temporal resolution and large field of view compared to that of Doppler radars and atmospheric profilers [2, 3]. The determination of characteristics of Cb based on cloud tracking from geostationary imageries may improve existing precipitation estimation and nowcasting schemes

Methods

Results

Conclusion