Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Tong, Biao; Sun, Xiangfei; Fu, Jiyang; He, Yuncheng; Chan, P. W.

doi:10.5194/amt-15-1829-2022

Cited by 10 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The original SCIs cover a too large area with respect to a TC, which makes it difficult to identify TC intensity effectively via DL methods. Thus, it is required to extract the TC portion from the original image [31]. This can be fulfilled through automatically cropping manipulation in accordance with the best track data of targeted TCs, as shown in Figure 2a,b.…”

Section: Data Pre-processingmentioning

confidence: 99%

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Tong,

Fu,

Deng

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Despite the great achievements in various SCI-based studies, accurate and efficient estimation of TC intensity still remains a challenge. In recent years, machine learning (ML) techniques have gained fast development and shown significant potential in dealing with big data, particularly with images. This study focuses on the objective estimation of TC intensity based on SCIs via a comprehensive usage of some advanced deep learning (DL) techniques and smoothing methods. Two estimation strategies are proposed and examined which, respectively, involve one and two functional stages. The one-stage strategy uses Vision Transformer (ViT) or Deep Convolutional Neutral Network (DCNN) as the regression model for directly identifying TC intensity, while the second strategy involves a classification stage that aims to stratify SCI samples into a few intensity groups and a subsequent regression stage that specifies the TC intensity. Further efforts are made to improve the estimation accuracy by using smoothing manipulations (via four specific smoothing techniques) in the scenarios of the aforementioned two strategies and their fusion. Results show that DCNN performs better than ViT in the one-stage strategy, while using ViT as the classification model and DCNN as the regression model can result in the best performance in the two-stage strategy. It is interesting that although the strategy of singly using DCNN wins out over any concerned two-stage strategy, the fusion of the two strategies outperforms either the one-stage strategy or the two-stage strategy. Results also suggest that using smoothing techniques are beneficial for the improvement of estimation accuracy. Overall, the best performance is achieved by using a hybrid strategy that consists of the one-stage strategy, the two-stage strategy and smoothing manipulation. The associated RMSE and MAE values are 9.81 kt and 7.51 kt, which prevail over those from most existing studies.

show abstract

Section: Data Pre-processingmentioning

confidence: 99%

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Tong,

Fu,

Deng

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…During the past decades, various instruments have been developed and exploited for TC studies. Among them, meteorological satellites have been receiving increasingly more concerns due to its ability of providing round‐the‐clock remote sensing detection of TCs over an incomparably vast region (Tong et al ., 2022a). Currently, satellite cloud images (SCIs) perhaps serve as the most basic data for TC positioning (Kishtawal, 2016).…”

Section: Introductionmentioning

confidence: 99%

Identification of tropical cyclone centre based on satellite images via deep learning techniques

Long

Tong

et al. 2022

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

A tropical cyclone (TC) is a highly destructive natural disaster. Accurate identification of key parameters of TCs is prerequisite for most TC-related research and practices. The centre position is one of TC's basic parameters. However, comparison of TC best track data released by different meteorological institutes usually indicates a noticeable discrepancy for this parameter among varied data sources. In this study, efforts are made towards identifying the centre location of TCs via deep learning techniques, based on TC satellite cloud images (SCIs). Six deep learning models are analysed and compared. YOLOv4 model achieved a confidence of 99.84%, which is better than other models. In addition, we further explore the factors affecting the positioning accuracy of the YOLOv4 model and its application to the location identification of multiple TCs and the tracking of individual TCs. Results demonstrate that the YOLOv4 model has a probability exceeding 99% for identifying multiple TC locations and also performs well for single TC tracking.

show abstract

Cyclone detection with end-to-end super resolution and faster R-CNN

Moustafa,

Metwalli,

Samshitha

et al. 2024

Earth Sci Inform

View full text Add to dashboard Cite

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain.

show abstract

Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Cited by 10 publications

References 35 publications

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Estimation of Tropical Cyclone Intensity via Deep Learning Techniques from Satellite Cloud Images

Identification of tropical cyclone centre based on satellite images via deep learning techniques

Cyclone detection with end-to-end super resolution and faster R-CNN

Contact Info

Product

Resources

About