A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images

Wang, Pingping; Yuan, Yue; Wang, Di

doi:10.1109/jstars.2020.2995158

Cited by 27 publications

(11 citation statements)

References 38 publications

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“…Some methods are designed using artificial fixing. In the case of the experience, the foreclose will cause some errors and fixing accuracy is not high [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Objective Detection of a Tropical Cyclone’s Center Using Satellite Image Sequences in the Northwest Pacific

Liu

Zhang

2022

Atmosphere

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A tropical cyclone (TC) is one of the most destructive natural disasters that can cause heavy loss of life and property. Determining a TC’s center is crucial to TC forecasting. It is difficult to locate the center of a TC during its formation stage and dissipation period. To address this problem, a novel objective algorithm called cloud motion wind (CMW) was proposed for detecting a TC’s center using infrared (IR) image sequences from a geostationary meteorology satellite. First, the optical flow model with weighted median filtering was utilized to build a cloud motion wind field. Second, the density matrix method was used to calculate the center of the TC. FY-2E (Fengyun-2E geostationary meteorological satellites) IR images of three TCs in the Northwest Pacific, Halong, Rammasun and Haiyan were analyzed using the proposed algorithm. The present algorithm estimated the track with an averaged track error of around 41 km. Experimental results compared with the observed track that was given by the China Meteorological Administration (CMA) show that the proposed method provided accurate estimates of the cyclone center. The present algorithm has the potential to be employed to assist forecasters to detect the track of imminent TC.

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“…Some methods are designed using artificial fixing. In the case of the experience, the foreclose will cause some errors and fixing accuracy is not high [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Objective Detection of a Tropical Cyclone’s Center Using Satellite Image Sequences in the Northwest Pacific

Liu

Zhang

2022

Atmosphere

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“…The center location for a tropical cyclone is important for the trajectory forecast of the tropical cyclone [ 7 – 9 ]. The center position of the tropical cyclone is estimated by finding the spiral origin [ 10 ] or by fitting the elliptic center [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Tropical Cyclone Center Location Method Based on Satellite Image

You

Qian

et al. 2022

Computational Intelligence and Neuroscience

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Accurately detecting and locating the center of the tropical cyclone is critical for the trajectory forecasting. This study proposed an automatic method for centers’ location of the tropical cyclones based on the visible or the infrared satellite images. The morphological structure of the tropical cyclone is modeled using the circular pattern. The tropical cyclone center is located based on regional pixels instead of skeleton points. All pixels in a segmented cloud cluster vote for a 2-dimensional accumulator. The center of the cloud cluster is computed by the mean voting distances, which are calculated by fitting quadratic functions in every column of the two-dimensional (2D) accumulator. Then, a linear function is fitted according to the functional relationship between the mean voting distance and voting angle. The fitted coefficients of the linear function are the center coordinates of the tropical cyclone. The proposed method for centers location of the tropical cyclones is tested using visible and infrared satellite images. The results of center location are compared with the best track provided in JMA datasets.

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“…Matsuoka et al (2018) trained CNNs on simulated outgoing long-wave radiation to detect TCs. P. Wang et al (2020) utilized CNNs with satellite imagery to detect TCs. However, template matching (circle fitting and image segmentation) was needed to identify the exact TC center.…”

Section: Introductionmentioning

confidence: 99%

Using Video Recognition to Identify Tropical Cyclone Positions

Smith

Toumi

2021

Geophysical Research Letters

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Tropical cyclone (TC) center fixing is a challenge for improving forecasting and establishing TC climatologies. We propose a novel objective solution through the use of video recognition algorithms. The videos of tropical cyclones in the Western North Pacific are of sequential, hourly, geostationary satellite infra-red (IR) images. A variety of convolutional neural network architectures are tested. The best performing network implements convolutional layers; a convolutional long short-term memory layer; and fully connected layers. Cloud features rotating around a center are effectively captured in this video-based technique. Networks trained with long-wave IR channels outperform a water vapour channel based network. The average position across the two IR networks has a 19.3 km median error across all intensities. This equates to a 42% lower error over a baseline technique. This video based method combined with the high geostationary satellite sampling rate can provide rapid and accurate automated updates of TC centers.

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A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images

Cited by 27 publications

References 38 publications

Objective Detection of a Tropical Cyclone’s Center Using Satellite Image Sequences in the Northwest Pacific

Objective Detection of a Tropical Cyclone’s Center Using Satellite Image Sequences in the Northwest Pacific

A Tropical Cyclone Center Location Method Based on Satellite Image

Using Video Recognition to Identify Tropical Cyclone Positions

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