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@article{Smith:2021:10.1029/2020GL091912,
author = {Smith, M and Toumi, R},
doi = {10.1029/2020GL091912},
journal = {Geophysical Research Letters},
pages = {1--9},
title = {Using video recognition to identify tropical cyclone positions},
url = {http://dx.doi.org/10.1029/2020GL091912},
volume = {48},
year = {2021}
}
TY - JOUR
AB - 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 infrared (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 vapor 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.
AU - Smith,M
AU - Toumi,R
DO - 10.1029/2020GL091912
EP - 9
PY - 2021///
SN - 0094-8276
SP - 1
TI - Using video recognition to identify tropical cyclone positions
T2 - Geophysical Research Letters
UR - http://dx.doi.org/10.1029/2020GL091912
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000641974600056&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091912
VL - 48
ER -
Climate Science
Earth and Life Sciences
Energy and Low-Carbon Futures
Resources and Pollution
Economics and Finance
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