High-Resolution Daily XCH4 Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data

Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environme...

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Vydáno v:Atmosphere Ročník 16; číslo 7; s. 806
Hlavní autoři: Awad, Mohamad M., Homayouni, Saeid
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2025
Témata:
Accuracy
Acidification
Artificial neural networks
Atmospheric methane
Carbon
Climate change
CNN
Datasets
Disasters
Emissions
Environmental degradation
Fossil fuels
global
Global temperatures
Global warming
Ground stations
Interpolation
Methane
monitoring
Natural disasters
Neural networks
Ocean acidification
prediction
Prediction models
Radial basis function
Regional analysis
Remote sensing
Repeaters
Root-mean-square errors
Satellites
Temperature rise
Trends
XCH4
ISSN:2073-4433, 2073-4433
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Shrnutí:Atmospheric methane (CH4) concentrations have increased to 2.5 times their pre-industrial levels, with a marked acceleration in recent decades. CH4 is responsible for approximately 30% of the global temperature rise since the Industrial Revolution. This growing concentration contributes to environmental degradation, including ocean acidification, accelerated climate change, and a rise in natural disasters. The column-averaged dry-air mole fraction of methane (XCH4) is a crucial indicator for assessing atmospheric CH4 levels. In this study, the Sentinel-5P TROPOMI instrument was employed to monitor, map, and estimate CH4 concentrations on both regional and global scales. However, TROPOMI data exhibits limitations such as spatial gaps and relatively coarse resolution, particularly at regional scales or over small areas. To mitigate these limitations, a novel Convolutional Neural Network Autoencoder (CNN-AE) model was developed. Validation was performed using the Total Carbon Column Observing Network (TCCON), providing a benchmark for evaluating the accuracy of various interpolation and prediction models. The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. This was followed by the Random Forest (RF) regressor (MAE: 29.07 ppb; RMSE: 36.89 ppb), GridData Nearest Neighbor Interpolator (NNI) (MAE: 30.06 ppb; RMSE: 32.14 ppb), and the Radial Basis Function (RBF) Interpolator (MAE: 80.23 ppb; RMSE: 90.54 ppb). On a global scale, the CNN-AE again outperformed other methods, yielding the lowest MAE and RMSE (19.78 and 24.7 ppb, respectively), followed by RF (21.46 and 27.23 ppb), GridData NNI (25.3 and 32.62 ppb), and RBF (43.08 and 54.93 ppb).
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ISSN:2073-4433
2073-4433
DOI:10.3390/atmos16070806