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

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.

Cloud Types Identification for Meteorological Satellite Image Using Multiple Sparse Representation Classifiers via Decision Fusion

“…It is of great significance to improve the accuracy of satellite cloud classification so as to enhance the effectiveness of meteorological monitoring and even establish a scientific climate model. Currently, manual interpretation methods are still widely used in satellite cloud imagery analysis, these methods not only affected by cognitive orientation and judging experience of interpreters but also face challenges to cope with massive cloud data [4], [5]. Therefore, computer aided cloud analysis has become a rapidly expanding field of research [6].…”

Detección digital de Sistemas Convectivos de Mesoescala a partir de imágenes meteorológicas multiespectrales

“…En latitudes tropicales, tanto ecuatoriales como medias, las nubes Cumulonimbus (Cb), son responsables de causar algunas condiciones climáticas extremas; estas nubes producen convección intensa que origina la formación de Sistemas Convectivos de Mesoescala (SCM) (Liu et al, 2014). Un SCM es un sistema de nubes Cb de origen convectivo que produce un área de precipitación contigua de aproximadamente 100 km en por lo menos una dirección (Houze, 2004).…”

Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images

“…Knowledge of convective systems' cloud processes and their evolution is important for understanding weather and climate, particularly over the tropics (Vila et al, 2008). In both equatorial and mid tropical latitudes, cumulonimbus (Cb) clouds are responsible for causing extreme weather conditions; these clouds intense convection causes the formation of Mesoscale Convective Systems (MCS) (Liu et al, 2014). A MCS is a convective system' Cb cloud that produces a contiguous precipitation area ≥ 100 km in at least one direction (Houze, 2004).…”

“…A MCS is a convective system' Cb cloud that produces a contiguous precipitation area ≥ 100 km in at least one direction (Houze, 2004). SCM 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 (Liu et al, 2014). A large number of MCS is located in Colombia, which according to data from Tropical Rainfall Measuring Mission (TRMM) during the period 1998-2002, contribute approximately 70% of annual precipitation (Sakamoto et al, 2011).…”

“…Observations of SCM are an important source of data for assimilation in weather forecasting and for monitoring climate trends (Liu et al, 2014). In this regard, images of the Geostationary Operational Environmental Satellites (GOES) have shown a high potential for detection and monitoring of SCM (Goyens et al, 2012).…”