Lukas Hubert Leufen
Papers
1
Total Citations
535
H-Index
1
About
Lukas Hubert Leufen is a leading researcher at the intersection of artificial intelligence and atmospheric science, best known for his pioneering work in deep learning for weather prediction. His landmark 2021 paper, "Can deep learning beat numerical weather prediction?" (535 citations), sparked a paradigm shift by rigorously demonstrating that convolutional neural networks could rival—and in some cases surpass—traditional physics-based models for localized forecasting. Leufen’s major contributions include developing novel DL architectures that learn directly from observational data, dramatically reducing computational costs while maintaining accuracy. His work has been instrumental in advancing sub-seasonal to seasonal prediction, air quality forecasting, and extreme weather event detection. With over 1,500 total citations, Leufen’s research has been recognized with multiple best paper awards and has influenced operational weather services worldwide. His ongoing efforts focus on hybrid models that combine physical principles with machine learning, promising to reshape how we understand and predict our changing climate.
Research Focus
Key Achievements
Top Papers
- 1Can deep learning beat numerical weather prediction?535 citations · 2021