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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Topic: Meteorological Phenomena and Simulations ×Clear all

79 claims from 46 papers, showing 1–20 of 46

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Accurate medium-range global weather forecasting with 3D neural networks

   Bi, Xie, Zhang, Chen, Gu and Tian · Nature · 2023

   The paper introduces Pangu-Weather, an AI weather forecasting method using 3D neural networks, and reports it matches or beats the leading physics-based system in medium-range global forecasts.

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   1. UncheckedPangu-Weather, an AI model trained on 39 years of global data, beat ECMWF's operational forecast on every tested variable using reanalysis data.“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 Fo…”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Learning skillful medium-range global weather forecasting

   Lam, Sánchez‐González, Willson et al. · Science · 2023

   The paper introduces GraphCast, a machine-learning weather model trained on reanalysis data, and reports that it beats the leading operational deterministic forecasting system on most verification targets.

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   1. UncheckedGraphCast, a machine-learning model, forecasts hundreds of weather variables 10 days ahead on a 0.25° global grid in under a minute.“It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute.”
   2. UncheckedGraphCast, a machine-learning model, is reported to beat the best operational deterministic forecasts on 90% of 1380 targets and to aid severe-event prediction.“GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

   Weyn, Durran and Caruana · Journal of Advances in Modeling Earth Systems · 2020

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   1. Unchecked“For short- to medium-range forecasting, our model significantly outperforms persistence, climatology, and a coarse-resolution dynamical numerical weather prediction (NWP) model.”
   2. Unchecked“On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top-of-atmosphere solar forcing.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning

   Scher · Geophysical Research Letters · 2018

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   1. Unchecked“Additionally, after being initialized with an arbitrary model state, the network can through repeatedly feeding back its predictions into its inputs create a climate run, which has similar climate statistics to the climate of the general circulation model.”
   2. Unchecked“This network climate run shows no long‐term drift, even though no conservation properties were explicitly designed into the network.”
   3. Unchecked“It is shown that it is possible to emulate the dynamics of a simple general circulation model with a deep neural network.”

5. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Neural Networks for Postprocessing Ensemble Weather Forecasts

   Rasp and Lerch · Monthly Weather Review · 2018

   The authors propose a neural network to correct systematic errors in ensemble weather forecasts, tested on 2-metre temperature at German surface stations, and say it can also show which variables matter.

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   1. UncheckedIn a German case study of 2-metre temperature forecasts, a neural network post-processing method beat benchmark methods and cost less to compute.“In a case study of 2-meter temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark post-processing methods while being computationally more affordable.”
   2. UncheckedIn a German 2-metre temperature case study, the neural network's better forecasts rely on extra predictor variables and station-specific embeddings.“Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings.”

6. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

   Pathak, Subramanian, Harrington et al. · arXiv (Cornell University) · 2022

   FourCastNet is a global, data-driven weather model at 0.25° resolution that gives short to medium-range forecasts and runs far faster than the traditional IFS model.

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   1. UncheckedFourCastNet, a data-driven model, is reported to match the IFS at short lead times for large-scale variables and to beat it for fine-scale ones such as precipitation.“FourCastNet matches the forecasting accuracy of the ECMWF Integrated Forecasting System (IFS), a state-of-the-art Numerical Weather Prediction (NWP) model, at short lead times for large-scale variables, while outperforming IFS for variables with complex fine-scale structure, including precipitation.”
   2. UncheckedFourCastNet, a data-driven weather model, produces a week-long global forecast in under 2 seconds, orders of magnitude faster than the IFS.“FourCastNet generates a week-long forecast in less than 2 seconds, orders of magnitude faster than IFS.”

7. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Probabilistic weather forecasting with machine learning

   Price, Sánchez‐González, Alet et al. · Nature · 2024

   The paper introduces GenCast, a machine-learning probabilistic weather model that it reports has greater skill and speed than ENS, the European Centre for Medium-Range Weather Forecasts' ensemble forecast.

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   1. UncheckedGenCast, a machine-learning model, produces an ensemble of 15-day global forecasts at 0.25° resolution for over 80 variables in 8 minutes.“GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min.”
   2. UncheckedThe paper says GenCast, a machine-learning model, beat the leading ensemble forecast, ENS, on 97.2% of 1,320 evaluated targets and on several specific forecast types.“It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production.”

8. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

   Weyn, Durran and Caruana · Journal of Advances in Modeling Earth Systems · 2019

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   1. Unchecked“At forecast lead times up to 3 days, CNNs trained to predict only 500‐hPa geopotential height easily outperform persistence, climatology, and the dynamics‐based barotropic vorticity model, but do not beat an operational full‐physics weather prediction model.”
   2. Unchecked“Our best performing CNN does a good job of capturing the climatology and annual variability of 500‐hPa heights and is capable of forecasting realistic atmospheric states at lead times of 14 days.”

9. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models

   Weyn, Durran, Caruana and Cresswell‐Clay · Journal of Advances in Modeling Earth Systems · 2021

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   1. Unchecked“Averaged globally and over a two-year test set, the ensemble mean RMSE retains skill relative to climatology beyond two-weeks, with anomaly correlation coefficients remaining above 0.6 through six days.”
   2. Unchecked“The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead…

10. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning

    Chattopadhyay, Nabizadeh and Hassanzadeh · Journal of Advances in Modeling Earth Systems · 2020

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    1. Unchecked“CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression.”
    2. Unchecked“Using both temperature and Z500, accuracies (recalls) with CapsNets increase to $\sim 80\%$ $(88\%)$, showing the promises of multi-modal data-driven frameworks for accurate/fast extreme weather predictions, which can augment NWP efforts in providing early w…

11. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Deep learning for post-processing ensemble weather forecasts

    Grönquist, Yao, Ben‐Nun et al. · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2021

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    1. Unchecked“Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%.”
    2. Unchecked“Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies.”
    3. Unchecked“We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble.”

12. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

    Scher and Messori · Geoscientific model development · 2019

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    1. Unchecked“Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.”

13. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    GraphCast: Learning skillful medium-range global weather forecasting

    Lam, Sánchez‐González, Willson et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.”
    2. Unchecked“We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclones, atmospheric rivers, and extreme t…

14. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    ClimaX: A foundation model for weather and climate

    Nguyen, Brandstetter, Kapoor, Gupta and Grover · arXiv (Cornell University) · 2023

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    1. Unchecked“The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining.”

15. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead

    Chen, Han, Gong et al. · arXiv (Cornell University) · 2023

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    1. Unchecked“In addition, the inference cost of each iteration is merely 600ms on NVIDIA Tesla A100 hardware.”
    2. Unchecked“The results suggest that FengWu can significantly improve the forecast skill and extend the skillful global medium-range weather forecast out to 10.75 days lead (with ACC of z500 > 0.6) for the first time.”

16. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Forecasting Global Weather with Graph Neural Networks

    Keisler · arXiv (Cornell University) · 2022

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    1. Unchecked“Test performance on metrics such as Z500 (geopotential height) and T850 (temperature) improves upon previous data-driven approaches and is comparable to operational, full-resolution, physical models from GFS and ECMWF, at least when evaluated on 1-degree sca…

17. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast

    Bi, Xie, Zhang, Chen, Gu and Tian · arXiv (Cornell University) · 2022

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    1. Unchecked“There are two key strategies to improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer (3DEST) architecture that formulates the height (pressure level) information into cubic data, and (ii) applying a hierarchical temporal aggregation…

18. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Machine Learning Methods for Weather Forecasting: A Survey

    Zhang, Liu, Zhang and Li · Atmosphere · 2025

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    1. Unchecked“Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field.”
    2. Unchecked“However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events.”

19. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    End-to-end data-driven weather prediction

    Allén, Markou, Tebbutt et al. · Nature · 2025

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    1. Unchecked“The global forecasts outperform an operational NWP baseline for several variables and lead times.”

20. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

    Charlton-Perez, Dacre, Driscoll et al. · npj Climate and Atmospheric Science · 2024

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    1. Unchecked“The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector an…
    2. Unchecked“All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradien…