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Cb patches in various life stages. The analyzed Cb patches are always located in the center of picture and their gravitational centers are marked with black criss-cross. The first column (a1, b1 ⋯ h1) is the classification results at 02:00 UTC of July 4, 2007; the second column (a2, b2 ⋯ h2) is the same as the first column but for 03:00 UTC; the third column (a3, b3 ⋯ h3) is the determination of life stages, respectively (a3: L1; b3: L2; c3: L3; d3: L4; e3: L5; f3: L6; g3: L7; h3: L8). L1, L2 ⋯ L8 means the same as that of Figure 2.
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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 i...
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... Weather radar has a significant role in generating early warnings to assist in disaster risk reduction (Rosell et al., 2020). When compared to satellites, which have a greater coverage area, weather radar still has limitations, particularly in the observation coverage area (Liu et al., 2014). Meanwhile, the Himawari-8 satellite, which is also a remote sensing tool, has good spatial and temporal resolution. ...
Extreme weather conditions caused by cumulonimbus (Cb) clouds are closely related to the world of aviation, which is the main mode of transportation in Indonesia. Thus, the delivery of information regarding Cb predictions needs to be optimized to support flight safety and minimize the impact that can be caused. The RDCA product from the Himawari satellite can be a solution for predicting cumulus clouds that have the potential to become Cb within the next 1 hour. How accurate is the prediction of the RDCA, is considered important to be carried out in its application in the Ambon Pattimura airport area. This study focuses on the spatial and statistical analysis of categorical scores from dichotomous verification using weather radar data and surface observations, which were also verified using several parameters. Based on analysis in July and December 2021, RDCA verification results using weather radar aligned with surface observation data show that RDCA has a high accuracy value in predicting Cb in the next 10-60 minutes. Meanwhile, the results of research with several parameters have a proficient level of accuracy, although in certain cases, there are still quite a lot of false alarms and misses, indicating that the RDCA point cannot predict perfectly. The results of this research have led to progress in the development of techniques or ways to obtain the accuracy of RDCA products. The results of the accuracy of the application of RDCA can be used as a basis for nowcasting considerations as well as practical use from an operational perspective in aviation. In addition to using surface data or observations as one of the verification considerations, this paper is a initial step in assessing the accuracy of RDCA products in the tropics, especially in Ambon.
... The present study provides a unique opportunity to study the vertical structure of an overshooting convective storm, which extended up to the tropopause (* 18 km), where generally shallow convective storms dominate during monsoon (Das et al. , 2017. This intense convection can lead to the development of tall clouds (e.g., cumulonimbus), which cause localized heavy rainfall, thunder, lightning, hail, and other severe weather phenomena resulting in serious hazards to air traffic (Liu et al. 2014). ...
This study examined a typical case of deep convective storm that formed over southwest India on October 12, 2011, using ground-based X-band radar measurements and Weather Research and Forecasting (WRF) model simulations. The radar observation showed isolated pockets of convective storm, which merged later to form a convective cluster. The observed storms were tall, extending well into the mixed-phase region. Few storms even extended up to the tropopause height. Three different WRF cloud microphysics schemes (WRF Double-Moment 6-Class, Morrison Double-Moment, and Milbrandt–Yau Double-Moment) were used to simulate the observed deep convective storm to examine the vertical structure of hydrometeors. All the cloud microphysics schemes were able to reproduce the convective storm event with a lag time of almost two and a half hours. The WRF Double-Moment 6-Class scheme better simulates the vertical structure of storm compared to the other two microphysics schemes. The WRF model reasonably simulated the observed patterns of convective storm when the WRF cloud microphysics scheme better simulate the graupel and snow. The differences in simulated storm structure obtained by different microphysics schemes compared to observation highlight the deficiency involved in the simulations in capturing the microphysics that is guiding the intensity of convective storms. The present study thus underscores the importance of microphysics in different parameterization schemes of WRF simulation over southwest India, which has an implication in the forecasting of convective storms.
... 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]. ...
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Meteorological satellite can monitor the weather conditions in large scales effectively; some solutions and researches have been raised for cloud types identification in satellite cloud image analysis. Extracting the features of satellite image and designing effective classifier play important roles in implementing cloud types identification system. Since different features describe the characteristics of the cloud image in different perspectives, the collaborative utilization different features help to improve the accuracy of cloud classification. This paper proposed a new method to identify cloud types for meteorological satellite image using multiple sparse representation classifiers via decision fusion (MSRCDF). Firstly, followed by different types of features extracting, multiple sparse representation based classifiers were trained respectively. Then, the strategy of decision fusion was introduced to fuse the outputs of multiple classifiers. In order to bring about a reasonable fusion rule, the fusion weights were determined by an adaptive iterative procedure, and the iterative procedure was constructed according to the performance of each sub-classifier. Finally, an adaptive weighted fusion was implemented to determine the cloud type according to the outputs of sub-classifiers and their corresponding weights. The experimental results on FY-2G satellite data demonstrate that the proposed method gains higher recognition accuracy than each separated sub-classifier, which suggests that the strategy of decision fusion can take advantage of each sub-classifier. Moreover, the proposed method achieves competitive results when compared with other state-of-the-art methods. The computation efficiency of the proposed method is also analyzed briefly.
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... 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). ...
... 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). ...
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The overview of papers dealing with the history of development and the current state of thunderstorm nowcasting technologies is presented. A role of information on thunderstorms obtained from different sources (satellites, radars, lightning detectors, ground-based microwave radiometers) and the significance of output data of numerical weather prediction models for thunderstorm nowcasting are shown. The features of thunderstorm nowcasting systems used in the national meteorological services are discussed.
