The proposed Physics-Informed CNN (PICNN), which integrates multi-scale feature extraction, spatial attention mechanisms, and a composite physics-informed loss function incorporating mean squared error, Laplacian spatial smoothness regularization, and spatial energy conservation constraints, achieves the best performance among all models.
Spatial downscaling of satellite imagery, the reconstruction of high-resolution outputs from coarser-resolution inputs, is a critical enabler of long-term land cover monitoring, yet the domain adaptation gap between natural-image super-resolution models and satellite sensor characteristics remains largely unaddressed....
Juan Valdés-Quintero, R. D. Vásquez-Salazar, J. C. Parra et al.· Italian National Conference...· 0 citations
Global climate models operate at coarse spatial resolutions that limit their ability to represent localized atmospheric phenomena, motivating deep learning–based super-resolution (SR) for meteorological fields. However, most existing SR models are designed for natural images and fail to fully capture the characteristic...
Won-ji Jo, Sung-Wook Park, Y. Park et al.· Journal of King Saud Univers...· 0 citations
Forecasts of radar composite reflectivity reveal that CorrDiff, as a generative model, is capable of capturing fine-grained meteorological details, yielding more physically realistic predictions than deterministic regression-based downscaling models.
Hong-Lu Sun, Hao Jing, Zhi-Xiang Dai et al.· Geoscientific Model Developm...· 0 citations
Accurate prediction of solar irradiance is fundamentally a problem of modeling radiative
transfer through a dynamic, multi-component chemical system—the atmosphere. Variability in
atmospheric composition and physical state, dictated by parameters like humidity, pressure, and
aerosol content, poses a significant chal...
Simon Onuwa Agbonifo· INTERNATIONAL JOURNAL OF CHE...· 0 citations
The research paper presents a detailed account of how deep learning, more specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures, has been pivotal in advancing weather prediction accuracy as well as reliability. Even though numerical weather prediction models based on traditiona...
Groundwater variability is recognized as a critical constraint on long-term water resource sustainability in South Korea under nonstationary climate forcing. This study developed a groundwater-level (GWL) projection framework based on CMIP6 simulations, validated against national monitoring data, screened for physical...
M. Waqas, Sang Min Kim· Water· 0 citations
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