Hava Durumu Tahmin Yöntemleri
Yapay Zekâ ile Çok Kısa Sürede Doğru Hava Tahminleri Yapmak ...
Geleneksel yöntemlerde meteorolojik veriler matematiksel denklemler kullanılarak, süper bilgisayarlar aracılığı ile hava durumu tahminlerine ...
araştırmada
Sonuçları Science dergisinde yayımlanan <anchor>araştırmada</anchor>, Google tarafından geliştirilen GraphCast isimli hava tahmini uygulamasının 10 günlük hava tahminlerini güvenilir hava durumu tahmin modellerinden daha hızlı ve daha doğru yaptığı belirlendi.
Learning skillful medium-range global weather forecasting | Science
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Research Article

WEATHER FORECASTING

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# Learning skillful medium-range global weather forecasting

Science

14 Nov 2023

Vol 382, Issue 6677

pp. 1416-1421

## Editor’s summary

The numerical models used to predict weather are large, complex, and computationally demanding and do not learn from past weather patterns. Lam et al. introduced a machine learning–based method that has been trained directly from reanalysis data of past atmospheric conditions. In this way, the authors were able to quickly predict hundreds of weather variables globally up to 10 days in advance and at high resolution. Their predictions were more accurate than those of traditional weather models in 90% of tested cases and displayed better severe event prediction for tropical cyclones, atmospheric rivers, and extreme temperatures. —H. Jesse Smith

## Abstract

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. 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. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems.

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## Supplementary Materials

### This PDF file includes:

Materials and Methods

Supplementary Text

Figs. S1 to S53

Tables S1 to S4

References ( 42– 75)

## References and Notes

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Science

Volume 382 | Issue 6677

22 December 2023

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Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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Received: 18 April 2023

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#### Acknowledgments

In alphabetical order, we thank K. Allen, C. Blundell, M. Botvinick, Z. B. Bouallegue, M. Brenner, R. Carver, M. Chantry, M. Deisenroth, P. Deuben, M. Garnelo, R. Keisler, D. Kochkov, C. Mattern, P. Mirowski, P. Norgaard, I. Price, C. Qin, S. Racanière, S. Rasp, Y. Rubanova, K. Shah, J. Smith, D. Worrall, and countless others at Alphabet and ECMWF for advice and feedback on our work. We also thank ECMWF for providing invaluable datasets to the research community. The style of the opening paragraph was inspired by ( 40).

Funding: All research in this study was funded by Google DeepMind and Alphabet. There was no external funding.

Author contributions: Conceptualization: R.L., A.S.-G., M.W., S.M., and P.B. Data curation: R.L., A.S.-G., M.W., A.M., and P.B. Formal analysis: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., W.H., A.P., S.M., and P.B. Investigation: R.L., A.S. -G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., W.H., A.P., S.M., and P.B. Methodology: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., W.H., A.P., S.M., and P.B. Project administration: R.L., A.S.-G., M.W., G.H., O.V., J.S., S.M., and P.B. Software: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., W.H., A.P., and P.B. Supervision: R.L., S.M., and P.B. Validation: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., W.H., A.P., S.M., and P.B. Visualization: R.L., A.S.-G., M.W., F.A., and P.B. Writing – original draft: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., S.H., A.P., S.M., and P.B. Writing – review & editing: R.L., A.S.-G., M.W., P.W., M.F., F.A., S.R., T.E., Z.E.-R., S.H., A.P., S.M., and P.B.

Competing interests: This work was done in the course of employment at Google DeepMind, with no other competing financial interests. A.S.-G., R.L., P.B., M.W., P.W., M.F., and A.P. have filed a provisional patent application relating to machine learning for learned medium-range global weather forecasting (US Provisional App. no. US63/435,163).

Data and materials availability: GraphCast’s code and trained weights are publicly available on GitHub at https://github.com/deepmind/graphcast ( 41). This work used publicly available data from the ECMWF. We used the ECMWF archive (expired real-time) products for ERA5, HRES, and TIGGE products, whose use is governed by the Creative Commons Attribution 4.0 International (CC BY 4.0). We used IBTrACS Version 4 from https://www.ncei.noaa.gov/products/international-best-track-archive and ( 29, 30), as required.

### Authors

#### Affiliations

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Investigation, Methodology, and Software.

Google Research, Mountain View, CA, USA.

Roles: Data curation and Software.

Google Research, Mountain View, CA, USA.

Roles: Writing - original draft and Writing - review & editing.

GeorgeHolland

Google DeepMind, London, UK.

Roles: Conceptualization, Funding acquisition, Project administration, Supervision, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Project administration, Supervision, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Funding acquisition and Project administration.

AlexanderPritzel

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing - original draft, and Writing - review & editing.

Google DeepMind, London, UK.

Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.

#### Notes

*

†

These authors contributed equally to this work.

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Learning skillful medium-range global weather forecasting.Science382,1416-1421(2023).DOI: 10.1126/science.adi2336

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#### Cited by

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The Climate of Innovation: AI’s Growing Influence in Weather Prediction Patents and Its Future Prospects, Sustainability, 15, 24, (16681), (2023). https://doi.org/10.3390/su152416681
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   - Zhengding Hu,
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Crossref

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6. - T. Selz,
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8. - Constantin Bône,
   - Guillaume Gastineau,
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   - Patrick Gallinari,
   - Carlos Mejia,

9. - Wen Zhou,
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   - Zhijin Li,

10. - Athul Rasheeda Satheesh,
    - Peter Knippertz,
    - Andreas H. Fink,
    - Eva‐Maria Walz,
    - Tilmann Gneiting,

Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon, Quarterly Journal of the Royal Meteorological Society, 149, 757, (3721-3737), (2023). https://doi.org/10.1002/qj.4581
Crossref

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