2021/11/14 by Lasse Espeholt, Espeholt, Lasse, Shreya Agrawal +20
Computer Science · Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Hydrological Forecasting Using AI #Hydrology and Watershed Management Studies #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks #cs.LG #physics.ao-ph
paper · pdf · doi:10.48550/arxiv.2111.07470
34 pages
arxiv created 2021/11/14 · openalex publication_date 2021/11/14 · arxiv updated 2021/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The problem of forecasting weather has been scientifically studied for centuries due to its high impact on human lives, transportation, food production and energy management, among others. Current operational forecasting models are based on physics and use supercomputers to simulate the atmosphere to make forecasts hours and days in advance. Better physics-based forecasts require improvements in the models themselves, which can be a substantial scientific challenge, as well as improvements in the underlying resolution, which can be computationally prohibitive. An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient. For neural models, however, each additional hour of lead time poses a substantial challenge as it requires capturing ever larger spatial contexts and increases the uncertainty of the prediction. In this work, we present a neural network that is capable of large-scale precipitation forecasting up to twelve hours ahead and, starting from the same atmospheric state, the model achieves greater skill than the state-of-the-art physics-based models HRRR and HREF that currently operate in the Continental United States. Interpretability analyses reinforce the observation that the model learns to emulate advanced physics principles. These results represent a substantial step towards establishing a new paradigm of efficient forecasting with neural networks.