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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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Keyword: numerical weather prediction ×Clear all

13 claims from 7 papers

1. 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.”

2. 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.”

3. 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…

4. 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…

5. 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.”

6. 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.”

7. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

   Willard, Harrington, Subramanian, Mahesh, O'Brien and Collins · arXiv (Cornell University) · 2024

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   1. Unchecked“Specifically, we train a minimally modified SwinV2 transformer on ERA5 data, and find that it attains superior forecast skill when compared against IFS.”