Abstract
Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. Instead of using optical images, wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) is utilized as the dataset for model training and testing. The wind field vectors are reconstructed and fed to the deep-learning model, which is built based on a faster-region with convolutional neural network (faster-RCNN). The model consists of three modules: a series of convolutional and pooling layers as the feature extractor, a region proposal network that searches for the potential areas of cyclone/anticyclone within the dataset, and the classifier that classifies the proposed region as cyclone or anticyclone through a fully-connected neural network. Compared with existing methods of cyclone detection, the test results indicate that this model based on deep learning is able to reduce the number of false alarms, and at the same time, maintain high accuracy in cyclone detection. An application of this method is presented in the article. By processing temporally continuous data of wind field, the model is able to track the path of Hurricane Irma in September, 2017. The advantages and limitations of the model are also discussed in the article.
Highlights
Cyclone detection is a classical yet still actively developing topic, since it provides the theoretical foundation for the responses of extreme weather condition, as well as studies of the Earth’s climate system
A performance experiment is conducted based on the global mean wind fields (MWF)-ASCAT data collected from 25 February 2020 to 5 March 2020
In order to verify the accuracy of the model, the results are evaluated by the standard performance measurements: true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR)
Summary
Cyclone detection is a classical yet still actively developing topic, since it provides the theoretical foundation for the responses of extreme weather condition, as well as studies of the Earth’s climate system. Rudeva and Gulev analyzed sea-level pressure (SLP) data over the northern hemisphere, based on which they detected cyclones by locating the minimum points of SLP [5]. Based on this method, Simmonds et al proposed an automatic cyclone detection algorithm by comparing the Laplacian operator of SLP at each grid point to those at neighboring grid points [6]. Hanley and Caballero developed an identification and tracking method that recognized multicenter cyclones by examining the gradient of SLP and finding minimum points [7]. The minimum SLP method can effectively detect an ideal cyclone, the accuracy of the detection result is limited due to the lack of in situ data and ambiguities in satellite presentation
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