Felix Kleinert
Papers
1
Total Citations
535
H-Index
1
About
Felix Kleinert is a leading researcher at the intersection of machine learning and atmospheric science, whose work is redefining how we approach weather prediction. His most influential contribution, the 2021 paper “Can deep learning beat numerical weather prediction?” (535 citations), directly challenged the long-standing dominance of physics-based models by demonstrating that deep learning methods could match—and in some cases surpass—the skill of traditional numerical weather prediction. This landmark study ignited a paradigm shift in meteorology, sparking a wave of research into data-driven forecasting. Kleinert’s broader research explores the application of artificial intelligence to geophysical fluid dynamics, climate modeling, and the development of hybrid models that combine physical laws with neural networks. His work has not only garnered widespread academic attention but has also influenced operational forecasting practices, positioning him as a key figure in the ongoing revolution of weather and climate science.
Research Focus
Key Achievements
Top Papers
- 1Can deep learning beat numerical weather prediction?535 citations · 2021