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1,248 claims from 785 papers are on the record. 46 have been checked so far; the other 1,202 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 21–40 of 46

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Temperature forecasting by deep learning methods

   Gong, Langguth, Ji et al. · Geoscientific model development · 2022

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   1. Unchecked“Including the 850 hPa temperature as an additional predictor enhances the forecast quality, and the model also benefits from a larger spatial domain.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation

   Hu, Chen, Wang and Li · Journal of Advances in Modeling Earth Systems · 2023

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   1. Unchecked“Comparisons on WeatherBench dataset show the learned distribution perturbation method using our SwinVRNN model achieves superior forecast accuracy and reasonable ensemble spread due to joint optimization of the two targets.”
   2. Unchecked“More notably, SwinVRNN surpasses operational IFS on surface variables of 2-m temperature and 6-hourly total precipitation at all lead times up to five days.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   The operational medium-range deterministic weather forecasting can be extended beyond a 10-day lead time

   Chen, Han, Ling et al. · Communications Earth & Environment · 2025

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   1. Unchecked“Comparative evaluations against the Integrated Forecasting System Ensemble show that FengWu-Ensemble achieves superior performance across multiple meteorological variables and evaluation metrics.”
   2. Unchecked“These enhancements allow FengWu to outperform deterministic forecasts produced by European Centre for Medium-Range Weather Forecasts High-Resolution Model, Pangu-Weather, and GraphCast.”
   3. Unchecked“A new deep learning-based global medium-range weather forecasting model outperforms existing machine learning models and the European Centre for Medium-Range Weather Forecasts model, producing accurate global weather forecasts beyond ten days.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Machine Learning Methods in Weather and Climate Applications: A Survey

   Chen, Han, Wang, Zhao, Yang and Yang · Applied Sciences · 2023

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   1. Unchecked“Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances.”

5. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

   Chen, Zhong, Zhang et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“The performance evaluation, based on latitude-weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather…

6. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific

   Liu, Hsu, Peng et al. · npj Climate and Atmospheric Science · 2024

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   1. Unchecked“A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu.”
   2. Unchecked“For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors.”

7. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   A data-to-forecast machine learning system for global weather

   Sun, Zhong, Xu et al. · Nature Communications · 2025

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   1. Unchecked“FuXi Weather generates reliable 10-day forecasts at 0.25° resolution using fewer observations than conventional NWP systems.”
   2. Unchecked“FuXi Weather outperforms the European Centre for Medium-Range Weather Forecasts high-resolution forecasts beyond day one in observation-sparse regions such as central Africa, highlighting its potential to improve forecasts where observational infrastructure…

8. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events

   Pasche, Wider, Zhang, Zscheischler and Engelke · Artificial Intelligence for the Earth Systems · 2024

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   1. Unchecked“We find that ML weather prediction models locally achieve similar accuracy to HRES on the record-shattering Pacific Northwest heatwave but underperform when aggregated over space and time.”

9. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

   Olivetti and Messori · Geoscientific model development · 2024

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   1. Unchecked“However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time.”
   2. Unchecked“Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times.”

10. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    A Practical Probabilistic Benchmark for AI Weather Models

    Brenowitz, Cohen, Pathak et al. · Geophysical Research Letters · 2025

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    1. Unchecked“The results reveal that two leading AI weather models, i.e. GraphCast and Pangu, are tied on the probabilistic CRPS metric even though the former outperforms the latter in deterministic scoring.”

11. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Physics-based models outperform AI weather forecasts of record-breaking extremes

    Zhang, Fischer, Zscheischler and Engelke · Science Advances · 2026

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    1. Unchecked“Here, we show that for record-breaking weather extremes, the physics-based numerical model High RESolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts still consistently outperforms state-of-the-art AI models GraphCast, GraphC…
    2. Unchecked“We demonstrate that forecast errors in AI models are consistently larger for record-breaking heat, cold, and wind than in HRES across nearly all lead times.”
    3. Unchecked“We further find that the examined AI models tend to underestimate both the frequency and intensity of record-breaking events, and they underpredict hot records and overestimate cold records with growing errors for larger record exceedance.”

12. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators

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

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    1. Unchecked“We report that a data-driven deep learning Earth system emulator, FourCastNet, can predict global weather and generate medium-range forecasts five orders-of-magnitude faster than NWP while approaching state-of-the-art accuracy.”
    2. Unchecked“FourCast-Net is optimized and scales efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed precision (11.9%of peak at that scale).”
    3. Unchecked“The time-to-solution for training FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in inference.”

13. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    AI for atmosphere–ocean sciences: advancements, challenges and ways forward

    Luo, Xia, Pan et al. · National Science Review · 2026

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    1. Unchecked“The most promising path forward is identified as the development of hybrid physics–AI modeling, which integrates the data-driven power of AI with the foundational constraints of physical laws to ensure generalizability and causal consistency.”
    2. Unchecked“A new framework for AI-based model intercomparison is essential for rigorous benchmark performance.”

14. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench

    Rasp and Thuerey · Journal of Advances in Modeling Earth Systems · 2021

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    1. Unchecked“The resulting forecasts outperform previous submissions to WeatherBench and are comparable in skill to a physical baseline at similar resolution.”

15. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Data-driven forecasts of extreme weather in East Asia: feasibility of operational use

    Oh, Bae, Son et al. · Weather and Climate Extremes · 2026

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    1. Unchecked“Overall forecast skills increase when MLWP models are initialized with ERA5 reanalysis, highlighting the importance of initial conditions even in MLWP.”

16. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Exploring the Origin of the Two-Week Predictability Limit: A Revisit of Lorenz’s Predictability Studies in the 1960s

    Shen, Pielke, Zeng and Zeng · Atmosphere · 2024

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    1. Unchecked“The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis.”

17. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    A Four‐Dimensional Variational Informed Generative Adversarial Network for Data Assimilation

    Wang, Duan, Ni et al. · Journal of Advances in Modeling Earth Systems · 2025

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    1. Unchecked“Moreover, our method demonstrates effective performance when starting from background fields of varying qualities, consistently achieving stable results.”

18. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    An extension of WeatherBench 2 to binary hydroclimatic forecasts

    Zhao, Li, Tu and Chen · Geoscientific model development · 2025

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    1. Unchecked“For wet extremes, the GraphCast tends to outperform the IFS HRES when using the total precipitation of ERA5 reanalysis data as the ground truth.”
    2. Unchecked“For warm extremes, Pangu-Weather, GraphCast and FuXi tend to be more skillful than the IFS HRES within 3 d lead time but become less skillful as lead time increases.”

19. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers

    Bouallègue, Weyn, Clare, Dramsch, Dueben and Chantry · arXiv (Cornell University) · 2023

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    1. Unchecked“Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark.”

20. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

    Ensemble Methods for Neural Network‐Based Weather Forecasts

    Scher and Messori · Journal of Advances in Modeling Earth Systems · 2020

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    1. Unchecked“The ensemble mean forecasts obtained from these four approaches all beat the unperturbed neural network forecasts, with the retraining method yielding the highest improvement.”
    2. Unchecked“However, the skill of the neural network forecasts is systematically lower than that of state-of-the-art numerical weather prediction models.”