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DOI | 10.1029/2020GL091912 |
Using Video Recognition to Identify Tropical Cyclone Positions | |
Mohan Smith; Ralf Toumi | |
2021-03-15 | |
发表期刊 | Geophysical Research Letters
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出版年 | 2021 |
英文摘要 | 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. |
领域 | 气候变化 |
URL | 查看原文 |
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文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/319833 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Mohan Smith,Ralf Toumi. Using Video Recognition to Identify Tropical Cyclone Positions[J]. Geophysical Research Letters,2021. |
APA | Mohan Smith,&Ralf Toumi.(2021).Using Video Recognition to Identify Tropical Cyclone Positions.Geophysical Research Letters. |
MLA | Mohan Smith,et al."Using Video Recognition to Identify Tropical Cyclone Positions".Geophysical Research Letters (2021). |
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