Martin G. Schultz

Papers

Total Citations

535

H-Index

About

Martin G. Schultz is a leading figure in computational meteorology, whose work bridges the gap between traditional numerical weather prediction and modern artificial intelligence. His research focuses on applying deep learning methods to atmospheric science, particularly in weather forecasting and air quality modeling. Schultz’s most influential contribution is his landmark 2021 paper, “Can deep learning beat numerical weather prediction?” which has garnered over 535 citations and sparked a paradigm shift in the field. This work critically examined whether AI-driven approaches could outperform established physics-based models, catalyzing a wave of research into machine learning for meteorology. Beyond this, Schultz has made significant strides in developing hybrid models that combine physical constraints with neural networks, improving forecast accuracy and computational efficiency. His achievements include leading major European research projects on environmental informatics and serving as a key voice in debates about the future of weather prediction. For students and researchers, Schultz’s work offers a compelling vision of how AI can transform a traditional, data-intensive science—making him an essential figure to follow for anyone interested in the intersection of deep learning and Earth system science.