Scarlet Stadtler
Papers
1
Total Citations
535
H-Index
1
About
Scarlet Stadtler is a leading figure at the intersection of artificial intelligence and atmospheric science, best known for her provocative and influential work on whether deep learning can outperform traditional numerical weather prediction (NWP). Her landmark 2021 paper, “Can deep learning beat numerical weather prediction?”—which has garnered over 535 citations—sparked a paradigm shift in the field, challenging decades of reliance on physics-based models and opening the door to data-driven forecasting approaches. Stadtler’s research explores the limits of machine learning in capturing complex atmospheric dynamics, and she has been instrumental in designing hybrid models that blend the interpretability of NWP with the pattern-recognition power of neural networks. Her work has not only generated intense debate but also inspired a new generation of interdisciplinary researchers to rethink weather prediction from the ground up. Beyond her highly cited paper, Stadtler has contributed to benchmark datasets and evaluation frameworks that are now standard in the AI-for-weather community. Her bold, rigorous approach continues to shape the future of operational meteorology, making her a pivotal voice in one of the most exciting frontiers in climate science.
Research Focus
Key Achievements
Top Papers
- 1Can deep learning beat numerical weather prediction?535 citations · 2021