A deep learning filter for the intraseasonal variability of the tropics
A deep learning filter for the intraseasonal variability of the tropics
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DOI：
10.1175/aies-d-22-0079.1
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发表时间：
2023-07
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影响因子：
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通讯作者：
C. Stan;Rama Sesha Sridhar Mantripragada
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文献类型：
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作者：
C. Stan;Rama Sesha Sridhar Mantripragada
本文提出了卷积神经网络（CNN）模型的新应用，用于过滤热带气氛的季节内变异性，在此深度学习过滤器中，在监督的机器学习框架中顺序应用了两个卷积层每日异常。 （Zonal风应力和外向辐射），使用基于CNN的过滤器获得的过滤信号与常规重量的滤波器之间的一致性指数在95 - 99％之间。过滤器是其适用于时间序列的，其长度与要提取的信号周期相当。
This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95 – 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted.