Super-Resolution Reconstruction of Humidity Fields based on Wasserstein Generative Adversarial Network with Gradient Penalty

• Tao Li,
• Liang Wang,
• Lina Wang,
• Rui Han,

Vol. 18, No. 5, May 31, 2024
10.3837/tiis.2024.05.001, Download Paper (Free):

• super-resolution
• Generative Adversarial Network
• Relative humidity
• Deep Learning
• gradient penalty

Abstract

Humidity is an important parameter in meteorology and is closely related to weather, human health, and the environment. Due to the limitations of the number of observation stations and other factors, humidity data are often not as good as expected, so high-resolution humidity fields are of great interest and have been the object of desire in the research field and industry. This study presents a novel super-resolution algorithm for humidity fields based on the Wasserstein generative adversarial network(WGAN) framework, with the objective of enhancing the resolution of low-resolution humidity field information. WGAN is a more stable generative adversarial networks(GANs) with Wasserstein metric, and to make the training more stable and simple, the gradient cropping is replaced with gradient penalty, and the network feature representation is improved by sub-pixel convolution, residual block combined with convolutional block attention module(CBAM) and other techniques. We evaluate the proposed algorithm using ERA5 relative humidity data with an hourly resolution of 0.25°×0.25°. Experimental results demonstrate that our approach outperforms not only conventional interpolation techniques, but also the super-resolution generative adversarial network(SRGAN) algorithm.

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