Accurate medium-range global weather forecasting with 3D neural networks
By Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Q. Tian. Nature, 2023
1,711 citations recorded by the source corpus.
Three-dimensional deep neural networks can be trained to forecast global weather patterns, including extreme weather, with accuracy greater than or equal to that of the best numerical weather prediction models. Weather forecasting is important for science and society. At present, the most accurate forecast system is the numerical weather prediction (NWP) method, which represents atmospheric states as discretized grids and numerically solves partial differential equations that describe the transition between those states^ 1 . However, this procedure is computationally expensive. Recently, artificial-intelligence-based methods^ 2 have shown potential in accelerating weather forecasting by orders of magnitude, but the forecast accuracy is still significantly lower than that of NWP methods. Here we introduce an artificial-intelligence-based method for accurate, medium-range global weather forecasting. We show that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, our program, Pangu-Weather, obtains stronger deterministic forecast results on reanalysis data in all tested variables when compared with the world’s best NWP system, the operational integrated forecasting system of the European Centre for Medium-Range Weather Forecasts (ECMWF)^ 3 . Our method also works well with extreme weather forecasts and ensemble forecasts. When initialized with reanalysis data, the accuracy of tracking tropical cyclones is also higher than that of ECMWF-HRES.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/6a78c73c92d5e5d9759b46eff892baf1d6e83754
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
In Global and Planetary Change
Citations in the corpus
- Foundation Models for Time Series Analysis: A Tutorial and Survey (2024, neutral): “It can also indicate the homogeneity between the pre-trained dataset and target dataset, especially for some real-world applications where a foundation model is built to fulfill domain-specific tasks [4, 13].”
- Neural general circulation models for weather and climate (2023, methodology): “Recent advances in machine learning have presented an alternative for weather forecasting 3,4,12,13 .”
- A foundation model for the Earth system (2025, positive): “The analysis of other large-scale global machine learning models 2,3,38 has been limited to comparisons of tracks, with recent comparisons indicating that performance lags behind that of the official operational forecasts 39 .”