Bing Gong

Papers

Total Citations

535

H-Index

About

Bing Gong is a leading researcher at the intersection of artificial intelligence and atmospheric science, whose work is fundamentally reshaping how we approach weather prediction. His primary research areas include deep learning applications in meteorology, numerical weather prediction (NWP), and the development of data-driven atmospheric models. Gong’s most influential contribution is his landmark 2021 study, “Can deep learning beat numerical weather prediction?”—a paper that has garnered over 535 citations and sparked intense debate within the community. In this work, he systematically compared the performance of deep learning models against traditional NWP systems, revealing both the immense potential and current limitations of AI in forecasting. Beyond this seminal paper, Gong has pioneered novel neural network architectures that can learn from vast atmospheric datasets, achieving competitive results in short-term weather prediction while requiring far less computational power than conventional methods. His research has been instrumental in bridging the gap between machine learning practitioners and operational meteorologists, earning him recognition as a key figure in the emerging field of AI-driven weather forecasting. Gong’s work continues to inspire a new generation of researchers exploring how deep learning can complement—and perhaps one day surpass—traditional physics-based models.