هوش مصنوعی دیپ‌مایند گوگل دقیق‌تر از همیشه آب‌وهوا را پیش‌بینی می‌کند
هوش مصنوعی GraphCast در کمتر از یک دقیقه، آب‌وهوا 10 روز آینده را با دقتی فراتر از روش‌های مرسوم پیش‌بینی می‌کند.
مقاله منتشرشده در Science
براساس <anchor>مقاله منتشرشده در Science</anchor>، درحال‌حاضر مرکز اروپایی پیش‌بینی آب‌و‌هوای رنج متوسط یا ECMWF دارای سیستمی پیشرو و متعارف در پیش‌بینی آب‌وهوا است. اما در یک ارزیابی جامع، هوش مصنوعی « گراف‌کست» در 90 درصد از 1380 معیار، از جمله دما، فشار، رطوبت، سرعت و جهت باد در سطوح مختلف جوی، بهتر از سیستم ECMWF عمل کرده است. مدل‌های رایانه‌ای قبلی مدت زیادی بود که در پیش‌بینی‌ها نقش محوری داشتند، اما تغییرات اقلیمی باعث شده که پیش‌بینی‌ رویدادهای ویران‌کننده‌ آب‌وهوایی بسیار دشوارتر شود.
Learning skillful medium-range global weather forecasting | Science
Learning skillful medium-range global weather forecasting \| Science

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[Home](https://www.science.org/ "Home") [Science](/journal/science "Science") [Vol. 382, No. 6677](/toc/science/382/6677 "Vol. 382, No. 6677") Learning skillful medium-range global weather forecasting

[Back To Vol. 382, No. 6677](/toc/science/382/6677 "Back To Vol. 382, No. 6677")

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Research Article

WEATHER FORECASTING

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# Learning skillful medium-range global weather forecasting

[RemiLam](#con1) [https://orcid.org/0000-0003-4222-5358](https://orcid.org/0000-0003-4222-5358) [\[email protected\]](/cdn-cgi/l/email-protection#a3d1c6cecacfc2cee3c4ccccc4cfc68dc0ccce9cc0c09ec2cfd5c2d1ccd0c4e3c4ccccc4cfc68dc0ccce85c2ced398c0c09ecec2d7d7cbc9d4e3c4ccccc4cfc68dc0ccce85c2ced398c0c09ed0cbc2c8cad1e3c4ccccc4cfc68dc0ccce85c2ced398c0c09ed3c6d7c6d1c1c2d7d7c2c4cfcac2e3c4ccccc4cfc68dc0ccce), [AlvaroSanchez-Gonzalez](#con2) [https://orcid.org/0000-0001-5055-5790](https://orcid.org/0000-0001-5055-5790) [\[email protected\]](/cdn-cgi/l/email-protection#295b4c4440454844694e46464e454c074a4644164a4a1448455f485b465a4e694e46464e454c074a46440f484459124a4a1444485d5d41435e694e46464e454c074a46440f484459124a4a145a414842405b694e46464e454c074a46440f484459124a4a14594c5d4c5b4b485d5d484e454048694e46464e454c074a4644), \[...\], [MatthewWillson](#con3) [https://orcid.org/0000-0002-8730-1927](https://orcid.org/0000-0002-8730-1927) [\[email protected\]](/cdn-cgi/l/email-protection#493b2c2420252824092e26262e252c672a2624762a2a7428253f283b263a2e092e26262e252c672a26246f282439722a2a7424283d3d21233e092e26262e252c672a26246f282439722a2a743a212822203b092e26262e252c672a26246f282439722a2a74392c3d2c3b2b283d3d282e252028092e26262e252c672a2624), [PeterWirnsberger](#con4) [https://orcid.org/0000-0001-5961-5817](https://orcid.org/0000-0001-5961-5817), \[...\], [MeireFortunato](#con5) [https://orcid.org/0009-0002-7058-4657](https://orcid.org/0009-0002-7058-4657), [FerranAlet](#con6) [https://orcid.org/0009-0000-3059-0062](https://orcid.org/0009-0000-3059-0062), [SumanRavuri](#con7) [https://orcid.org/0000-0002-7481-7633](https://orcid.org/0000-0002-7481-7633), [TimoEwalds](#con8) [https://orcid.org/0000-0002-9693-7986](https://orcid.org/0000-0002-9693-7986), [ZachEaton-Rosen](#con9) [https://orcid.org/0009-0002-2102-6982](https://orcid.org/0009-0002-2102-6982), \[...\], [WeihuaHu](#con10) [https://orcid.org/0000-0003-2956-2616](https://orcid.org/0000-0003-2956-2616), [AlexanderMerose](#con11) [https://orcid.org/0000-0003-2944-5639](https://orcid.org/0000-0003-2944-5639), [StephanHoyer](#con12) [https://orcid.org/0000-0002-5207-0380](https://orcid.org/0000-0002-5207-0380), [GeorgeHolland](#con13), [OriolVinyals](#con14) [https://orcid.org/0000-0001-7848-7283](https://orcid.org/0000-0001-7848-7283), [JacklynnStott](#con15) [https://orcid.org/0009-0004-7859-3384](https://orcid.org/0009-0004-7859-3384), [AlexanderPritzel](#con16), [ShakirMohamed](#con17) [https://orcid.org/0000-0002-1184-5776](https://orcid.org/0000-0002-1184-5776) [\[email protected\]](/cdn-cgi/l/email-protection#5624333b3f3a373b16313939313a337835393b6935356b373a20372439253116313939313a337835393b70373b266d35356b3b3722223e3c2116313939313a337835393b70373b266d35356b253e373d3f2416313939313a337835393b70373b266d35356b26332233243437222237313a3f3716313939313a337835393b), and [PeterBattaglia](#con18) [https://orcid.org/0000-0003-3622-7111](https://orcid.org/0000-0003-3622-7111) [\[email protected\]](/cdn-cgi/l/email-protection#addfc8c0c4c1ccc0edcac2c2cac1c883cec2c092cece90ccc1dbccdfc2decaedcac2c2cac1c883cec2c08bccc0dd96cece90c0ccd9d9c5c7daedcac2c2cac1c883cec2c08bccc0dd96cece90dec5ccc6c4dfedcac2c2cac1c883cec2c08bccc0dd96cece90ddc8d9c8dfcfccd9d9cccac1c4ccedcac2c2cac1c883cec2c0)+15 authors+13 authors+8 authorsfewer [Authors Info & Affiliations](#tab-contributors)

Science

14 Nov 2023

Vol 382, Issue 6677

pp. 1416-1421

[DOI: 10.1126/science.adi2336](https://doi.org/10.1126/science.adi2336)

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- Contents

- [Editor’s summary](#editor-abstract)
- [Abstract](#abstract)
- [Supplementary Materials](#supplementary-materials)
- [References and Notes](#bibliography)

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## 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_](#R42)– [_75_](#R75))

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## References and Notes

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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_](#R40)).

**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](https://github.com/deepmind/graphcast) ( [_41_](#R41)). 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](https://www.ncei.noaa.gov/products/international-best-track-archive) and ( [_29_](#R29), [_30_](#R30)), as required.

**License information:** Copyright © 2023 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. [https://www.science.org/about/science-licenses-journal-article-reuse](https://www.science.org/about/science-licenses-journal-article-reuse)

### Authors

#### Affiliations

RemiLam[\*](#cor1), [†](#afn1) [https://orcid.org/0000-0003-4222-5358](https://orcid.org/0000-0003-4222-5358) [\[email protected\]](/cdn-cgi/l/email-protection#c8baada5a1a4a9a588afa7a7afa4ade6aba7a5f7ababf5a9a4bea9baa7bbaf88afa7a7afa4ade6aba7a5eea9a5b8f3ababf5a5a9bcbca0a2bf88afa7a7afa4ade6aba7a5eea9a5b8f3ababf5bba0a9a3a1ba88afa7a7afa4ade6aba7a5eea9a5b8f3ababf5b8adbcadbaaaa9bcbca9afa4a1a988afa7a7afa4ade6aba7a5)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Lam/Remi)

AlvaroSanchez-Gonzalez[\*](#cor1), [†](#afn1) [https://orcid.org/0000-0001-5055-5790](https://orcid.org/0000-0001-5055-5790) [\[email protected\]](/cdn-cgi/l/email-protection#1a687f7773767b775a7d75757d767f34797577257979277b766c7b6875697d5a7d75757d767f347975773c7b776a21797927777b6e6e72706d5a7d75757d767f347975773c7b776a2179792769727b7173685a7d75757d767f347975773c7b776a217979276a7f6e7f68787b6e6e7b7d76737b5a7d75757d767f34797577)

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.

[View all articles by this author](/authored-by/Sanchez-Gonzalez/Alvaro)

MatthewWillson[\*](#cor1), [†](#afn1) [https://orcid.org/0000-0002-8730-1927](https://orcid.org/0000-0002-8730-1927) [\[email protected\]](/cdn-cgi/l/email-protection#483a2d2521242925082f27272f242d662b2725772b2b7529243e293a273b2f082f27272f242d662b27256e292538732b2b7525293c3c20223f082f27272f242d662b27256e292538732b2b753b202923213a082f27272f242d662b27256e292538732b2b75382d3c2d3a2a293c3c292f242129082f27272f242d662b2725)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Willson/Matthew)

PeterWirnsberger[†](#afn1) [https://orcid.org/0000-0001-5961-5817](https://orcid.org/0000-0001-5961-5817)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Wirnsberger/Peter)

MeireFortunato[†](#afn1) [https://orcid.org/0009-0002-7058-4657](https://orcid.org/0009-0002-7058-4657)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Fortunato/Meire)

FerranAlet[†](#afn1) [https://orcid.org/0009-0000-3059-0062](https://orcid.org/0009-0000-3059-0062)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Alet/Ferran)

SumanRavuri[†](#afn1) [https://orcid.org/0000-0002-7481-7633](https://orcid.org/0000-0002-7481-7633)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Ravuri/Suman)

TimoEwalds [https://orcid.org/0000-0002-9693-7986](https://orcid.org/0000-0002-9693-7986)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Ewalds/Timo)

ZachEaton-Rosen [https://orcid.org/0009-0002-2102-6982](https://orcid.org/0009-0002-2102-6982)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Eaton-Rosen/Zach)

WeihuaHu [https://orcid.org/0000-0003-2956-2616](https://orcid.org/0000-0003-2956-2616)

Google DeepMind, London, UK.

Roles: Investigation, Methodology, and Software.

[View all articles by this author](/authored-by/Hu/Weihua)

AlexanderMerose [https://orcid.org/0000-0003-2944-5639](https://orcid.org/0000-0003-2944-5639)

Google Research, Mountain View, CA, USA.

Roles: Data curation and Software.

[View all articles by this author](/authored-by/Merose/Alexander)

StephanHoyer [https://orcid.org/0000-0002-5207-0380](https://orcid.org/0000-0002-5207-0380)

Google Research, Mountain View, CA, USA.

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

[View all articles by this author](/authored-by/Hoyer/Stephan)

GeorgeHolland

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Holland/George)

OriolVinyals [https://orcid.org/0000-0001-7848-7283](https://orcid.org/0000-0001-7848-7283)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Vinyals/Oriol)

JacklynnStott [https://orcid.org/0009-0004-7859-3384](https://orcid.org/0009-0004-7859-3384)

Google DeepMind, London, UK.

Roles: Funding acquisition and Project administration.

[View all articles by this author](/authored-by/Stott/Jacklynn)

AlexanderPritzel

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Pritzel/Alexander)

ShakirMohamed[\*](#cor1) [https://orcid.org/0000-0002-1184-5776](https://orcid.org/0000-0002-1184-5776) [\[email protected\]](/cdn-cgi/l/email-protection#96e4f3fbfffaf7fbd6f1f9f9f1faf3b8f5f9fba9f5f5abf7fae0f7e4f9e5f1d6f1f9f9f1faf3b8f5f9fbb0f7fbe6adf5f5abfbf7e2e2fefce1d6f1f9f9f1faf3b8f5f9fbb0f7fbe6adf5f5abe5fef7fdffe4d6f1f9f9f1faf3b8f5f9fbb0f7fbe6adf5f5abe6f3e2f3e4f4f7e2e2f7f1fafff7d6f1f9f9f1faf3b8f5f9fb)

Google DeepMind, London, UK.

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

[View all articles by this author](/authored-by/Mohamed/Shakir)

PeterBattaglia[\*](#cor1) [https://orcid.org/0000-0003-3622-7111](https://orcid.org/0000-0003-3622-7111) [\[email protected\]](/cdn-cgi/l/email-protection#d1a3b4bcb8bdb0bc91b6bebeb6bdb4ffb2bebceeb2b2ecb0bda7b0a3bea2b691b6bebeb6bdb4ffb2bebcf7b0bca1eab2b2ecbcb0a5a5b9bba691b6bebeb6bdb4ffb2bebcf7b0bca1eab2b2eca2b9b0bab8a391b6bebeb6bdb4ffb2bebcf7b0bca1eab2b2eca1b4a5b4a3b3b0a5a5b0b6bdb8b091b6bebeb6bdb4ffb2bebc)

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.

[View all articles by this author](/authored-by/Battaglia/Peter)

#### Notes

\*

Corresponding author. Email: [\[email protected\]](/cdn-cgi/l/email-protection#9be9fef6f2f7faf6dbfcf4f4fcf7feb5f8f4f6) (R.L.); [\[email protected\]](/cdn-cgi/l/email-protection#4425283225362b372304232b2b2328216a272b29) (A.S.-G.); [\[email protected\]](/cdn-cgi/l/email-protection#90fdf1e4e4f8fae7d0f7fffff7fcf5bef3fffd) (M.W.); [\[email protected\]](/cdn-cgi/l/email-protection#c2b1aaa3a9abb082a5adada5aea7eca1adaf) (S.M.); [\[email protected\]](/cdn-cgi/l/email-protection#b6c6d3c2d3c4d4d7c2c2d7d1dadfd7f6d1d9d9d1dad398d5d9db) (P.B.)

†

These authors contributed equally to this work.

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