Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review

Y Sun, K Deng, K Ren, J Liu, C Deng, Y ** - ISPRS Journal of …, 2024 - Elsevier
Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

Foundation models for weather and climate data understanding: A comprehensive survey

S Chen, G Long, J Jiang, D Liu, C Zhang - arxiv preprint arxiv:2312.03014, 2023 - arxiv.org
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data

F Gerges, MC Boufadel, E Bou-Zeid, H Nassif… - … and Information Systems, 2024 - Springer
Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy
is important for renewable energy generation and is intrinsically fluctuating. Forecasting …

Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach

W **e, J Li, J Shi, X Zhang, AS Usmani, G Chen - Marine pollution bulletin, 2023 - Elsevier
Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure
damage and great casualties of offshore platforms. Real-time natural gas jet fire plume …

Downscaling daily wind speed with Bayesian deep learning for climate monitoring

F Gerges, MC Boufadel, E Bou-Zeid, H Nassif… - International Journal of …, 2024 - Springer
Wind dynamics are extremely complex and have critical impacts on the level of damage from
natural hazards, such as storms and wildfires. In the wake of climate change, wind dynamics …

Bayesian multi-head convolutional neural networks with Bahdanau attention for forecasting daily precipitation in climate change monitoring

F Gerges, MC Boufadel, E Bou-Zeid, A Darekar… - … Conference on Machine …, 2022 - Springer
Abstract General Circulation Models (GCMs) are established numerical models for
simulating multiple climate variables, decades into the future. GCMs produce such …

Spatial Downscaling of Satellite Sea Surface Wind with Soft-Sharing Multi-Task Learning

Y Yue, J Liu, Y Sun, K Ren, K Deng, K Deng - Remote Sensing, 2025 - mdpi.com
Sea surface wind (SSW) plays a pivotal role in numerous research endeavors pertaining to
meteorology and oceanography. SSW fields derived from remote sensing have been widely …

Transformers as a classifier for solar flare time series: a comparative study

JS Ferreira, ALS Gradvohl, AEA da Silva, GP Coelho… - 2024 - researchsquare.com
Solar flares are violent and sudden eruptions that occur in the solar atmosphere and release
energy in the form of radiation. They can affect technological systems on Earth and in its …

Monitoring Climate Change With Machine Learning and Uncertainty Quantification

F Gerges - 2022 - search.proquest.com
The impact of climate change on the environment has become increasingly visible today.
One important topic in climate change monitoring is to forecast future climate events, which …