A 1D Convolutional Neural Network (1D-CNN) Temporal Filter for Atmospheric Variability: Reducing the Sensitivity of Filtering Accuracy to Missing Data Points

The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sen...

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Autors principals:	Dan Yu, Hoiio Kong, Jeremy Cheuk-Hin Leung, Pak Wai Chan, Clarence Fong, Yuchen Wang, Banglin Zhang
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2024-07-01
Col·lecció:	Applied Sciences
Matèries:	climate variability temporal filter 1D convolution neural network quasi-biweekly-to-intraseasonal oscillation machine learning Hong Kong
Accés en línia:	https://www.mdpi.com/2076-3417/14/14/6289
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