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

Authors :
Dan Yu
Hoiio Kong
Jeremy Cheuk-Hin Leung
Pak Wai Chan
Clarence Fong
Yuchen Wang
Banglin Zhang
Source :
Applied Sciences, Vol 14, Iss 14, p 6289 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

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 sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intraseasonal signals (10–60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (R2 > 0.8). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
14
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.4b0cfebadad04cf1882b5a7f37d7ee08
Document Type :
article
Full Text :
https://doi.org/10.3390/app14146289