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• W3100331506 abstract "In this paper, the infrared cloud images from Fengyun series geostationary satellites and the best track data from the China Meteorological Administration (CMA-BST) in 2015–2017 are used to investigate the effects of two multi-factor models, generalized linear model (GLM) and long short-term memory (LSTM) model, for tropical cyclone (TC) intensity estimation based on the deviation angle variance (DAV) technique. For comparison, the typical single-factor Sigmoid function model (SFM) with the map minimum value of DAV is also used to produce TC intensity estimation. Sensitivity experiments regarding the DAV calculation radius and different training data groups are conducted, and the estimation precision and optimum calculation radius for DAV in the western North Pacific (WNP) are analyzed. The results show that the root-mean-square-error (RMSE) of the single-factor SFM is 8.79–13.91 m s−1 by using the individual years as test sets and the remaining two years as training sets with the optimum calculation radius of 550 km. However, after selecting and using the high-correlation multiple factors from the same test and training data, the RMSEs of GLM and LSTM models decrease to 5.93–8.68 and 4.99–7.00 m s−1 respectively, with their own optimum calculation radii of 350 and 400 km. All the sensitivity experiments indicate that the SFM results are significantly influenced by the DAV calculation radius and characteristics of the training set data, while the results of multi-factor models appear more stable. Furthermore, the multi-factor models reduce the optimum radius within the process of DAV calculation and improve the precision of TC intensity estimation in the WNP, which can be chosen as an effective approach for TC intensity estimation in marine areas." @default.
• W3100331506 created "2020-11-23" @default.
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• W3100331506 date "2020-10-01" @default.
• W3100331506 modified "2023-09-27" @default.
• W3100331506 title "Multi-Factor Intensity Estimation for Tropical Cyclones in the Western North Pacific Based on the Deviation Angle Variance Technique" @default.
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• W3100331506 doi "https://doi.org/10.1007/s13351-020-9216-5" @default.
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