{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:37:03Z","timestamp":1778060223774,"version":"3.51.4"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"8044","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nature"],"published-print":{"date-parts":[[2025,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)<jats:sup>1<\/jats:sup>, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations<jats:sup>2,3<\/jats:sup>. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather\u00a0Forecasts<jats:sup>4<\/jats:sup>. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25\u00b0 latitude\u2013longitude resolution, for more than 80 surface and atmospheric variables, in 8\u2009min. 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. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.<\/jats:p>","DOI":"10.1038\/s41586-024-08252-9","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T16:02:33Z","timestamp":1733328153000},"page":"84-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":259,"title":["Probabilistic weather forecasting with machine learning"],"prefix":"10.1038","volume":"637","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-2703","authenticated-orcid":false,"given":"Ilan","family":"Price","sequence":"first","affiliation":[]},{"given":"Alvaro","family":"Sanchez-Gonzalez","sequence":"additional","affiliation":[]},{"given":"Ferran","family":"Alet","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1556-9932","authenticated-orcid":false,"given":"Tom R.","family":"Andersson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6510-0831","authenticated-orcid":false,"given":"Andrew","family":"El-Kadi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6945-8325","authenticated-orcid":false,"given":"Dominic","family":"Masters","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9693-7986","authenticated-orcid":false,"given":"Timo","family":"Ewalds","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7859-3384","authenticated-orcid":false,"given":"Jacklynn","family":"Stott","sequence":"additional","affiliation":[]},{"given":"Shakir","family":"Mohamed","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3622-7111","authenticated-orcid":false,"given":"Peter","family":"Battaglia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4222-5358","authenticated-orcid":false,"given":"Remi","family":"Lam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8730-1927","authenticated-orcid":false,"given":"Matthew","family":"Willson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"8252_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/nature14956","volume":"525","author":"P Bauer","year":"2015","unstructured":"Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47\u201355 (2015).","journal-title":"Nature"},{"key":"8252_CR2","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1126\/science.adi2336","volume":"382","author":"R Lam","year":"2023","unstructured":"Lam, R. et al. Learning skillful medium-range global weather forecasting. Science 382, 1416\u20131421 (2023).","journal-title":"Science"},{"key":"8252_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/s41586-023-06185-3","volume":"619","author":"K Bi","year":"2023","unstructured":"Bi, K. et al. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 533\u2013538 (2023).","journal-title":"Nature"},{"key":"8252_CR4","unstructured":"ECMWF. IFS Documentation CY46R1. Part V: Ensemble Prediction System (ECMWF, 2019)."},{"key":"8252_CR5","doi-asserted-by":"crossref","unstructured":"Lorenz, E. N. The Essence of Chaos (Univ. Washington Press, 1993).","DOI":"10.4324\/9780203214589"},{"key":"8252_CR6","doi-asserted-by":"crossref","unstructured":"Palmer, T. & Hagedorn, R. Predictability of Weather and Climate (Cambridge Univ. Press, 2006).","DOI":"10.1017\/CBO9780511617652"},{"key":"8252_CR7","doi-asserted-by":"crossref","unstructured":"Kalnay, E. Atmospheric Modeling, Data Assimilation and Predictability (Cambridge Univ. Press, 2003).","DOI":"10.1017\/CBO9780511802270"},{"key":"8252_CR8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1002\/qj.3383","volume":"145","author":"T Palmer","year":"2019","unstructured":"Palmer, T. The ECMWF ensemble prediction system: looking back (more than) 25\u2009years and projecting forward 25\u2009years. Q. J. R. Meteorol. Soc. 145, 12\u201324 (2019).","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"8252_CR9","doi-asserted-by":"publisher","first-page":"E680","DOI":"10.1175\/BAMS-D-21-0273.1","volume":"104","author":"N Roberts","year":"2023","unstructured":"Roberts, N. et al. Improver: the new probabilistic postprocessing system at the Met office. Bull. Am. Meteorol. Soc. 104, E680\u2013E697 (2023).","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"8252_CR10","unstructured":"Yamaguchi, H. et al. Introduction to JMA's New Global Ensemble Prediction System. Technical Review No. 20 (RSMC Tokyo, Typhoon Center, 2018); https:\/\/www.jma.go.jp\/jma\/jma-eng\/jma-center\/rsmc-hp-pub-eg\/techrev\/text20-2.pdf."},{"key":"8252_CR11","unstructured":"Zhu, Y., Toth, Z., Wobus, R., Wei, M. & Cui, B. May 2006 Upgrade of the GEFS and First Implementation of NAEFS Systems (NAEFS, 2012)."},{"key":"8252_CR12","unstructured":"ECMWF. Plans for High-Resolution Forecast (HRES) and Ensemble Forecast (ENS) Control Run (ECMWF, 2024)."},{"key":"8252_CR13","unstructured":"Pathak, J. et al. Fourcastnet: a global data-driven high-resolution weather model using adaptive Fourier neural operators. Preprint at arxiv.org\/abs\/2202.11214 (2022)."},{"key":"8252_CR14","unstructured":"Keisler, R. Forecasting global weather with graph neural networks. Preprint at arxiv.org\/abs\/2202.07575 (2022)."},{"key":"8252_CR15","doi-asserted-by":"publisher","unstructured":"Kurth, T. et al. FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. In Proc. Platform Adv. Sci. Comp. Conf. 1\u201311 (ACM, New York, NY, USA, 2023); https:\/\/doi.org\/10.1145\/3592979.3593412.","DOI":"10.1145\/3592979.3593412"},{"key":"8252_CR16","unstructured":"Chen, K. et al. Fengwu: pushing the skillful global medium-range weather forecast beyond 10 days lead. Preprint at arxiv.org\/abs\/2304.02948 (2023)."},{"key":"8252_CR17","unstructured":"Nguyen, T. et al. Scaling transformer neural networks for skillful and reliable medium-range weather forecasting. Preprint at arxiv.org\/abs\/2312.03876 (2023)."},{"key":"8252_CR18","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1038\/s41612-023-00512-1","volume":"6","author":"H Li","year":"2023","unstructured":"Li, H. et al. uXi: a cascade machine learning forecasting system for 15-day global weather forecast. npj Clim. Atmos. Sci. 6, 190 (2023).","journal-title":"npj Clim. Atmos. Sci."},{"key":"8252_CR19","unstructured":"Graubner, A. et al. Calibration of large neural weather models. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning (NeurIPS, 2022)."},{"key":"8252_CR20","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1038\/s41586-024-07744-y","volume":"632","author":"D Kochkov","year":"2024","unstructured":"Kochkov, D. et al. Neural general circulation models for weather and climate. Nature 632, 1060\u20131066 (2024).","journal-title":"Nature"},{"key":"8252_CR21","first-page":"26565","volume":"35","author":"T Karras","year":"2022","unstructured":"Karras, T., Aittala, M., Aila, T. & Laine, S. Elucidating the design space of diffusion-based generative models. Adv. Neural Inf. Process. Syst. 35, 26565\u201326577 (2022).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"8252_CR22","unstructured":"Song, Y. et al. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations (OpenReview.net, 2021)."},{"key":"8252_CR23","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, 2256\u20132265 (PMLR, 2015)."},{"key":"8252_CR24","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.3390\/e25101469","volume":"25","author":"R Yang","year":"2023","unstructured":"Yang, R., Srivastava, P. & Mandt, S. Diffusion probabilistic modeling for video generation. Entropy 25, 1469 (2023).","journal-title":"Entropy"},{"key":"8252_CR25","doi-asserted-by":"publisher","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","volume":"45","author":"F-A Croitoru","year":"2023","unstructured":"Croitoru, F.-A., Hondru, V., Ionescu, R. T. & Shah, M. Diffusion models in vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45, 10850\u201310869 (2023).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8252_CR26","unstructured":"Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems Vol. 30 (NeurIPS, 2017)."},{"key":"8252_CR27","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1002\/qj.3803","volume":"146","author":"H Hersbach","year":"2020","unstructured":"Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999\u20132049 (2020).","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"8252_CR28","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1175\/BAMS-D-13-00191.1","volume":"97","author":"R Swinbank","year":"2016","unstructured":"Swinbank, R. et al. The TIGGE project and its achievements. Bull. Am. Meteorol. Soc. 97, 49\u201367 (2016).","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"8252_CR29","unstructured":"WMO. Manual on the Global Data-Processing and Forecasting System, Appendix 2.2.35, Section 7. (World Meteorological Organization, 2023)."},{"key":"8252_CR30","doi-asserted-by":"crossref","unstructured":"Rasp, S. et al. WeatherBench 2: a benchmark for the next generation of data\u2010driven global weather models. J. Adv. Model. Earth Syst. 16, e2023MS004019 (2024).","DOI":"10.1029\/2023MS004019"},{"key":"8252_CR31","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T. & Raftery, A. E. Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359\u2013378 (2007).","journal-title":"J. Am. Stat. Assoc."},{"key":"8252_CR32","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1175\/JHM-D-14-0008.1","volume":"15","author":"V Fortin","year":"2014","unstructured":"Fortin, V., Abaza, M., Anctil, F. & Turcotte, R. Why should ensemble spread match the RMSE of the ensemble mean? J. Hydrometeorol.\u00a015, 1708\u20131713 (2014).","journal-title":"J. Hydrometeorol."},{"key":"8252_CR33","unstructured":"Talagrand, O., Vautard, R. & Strauss, B. Evaluation of probabilistic prediction systems. In Proc. Workshop on Predictability (ECMWF, 1999)."},{"key":"8252_CR34","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1002\/qj.3712","volume":"146","author":"HA Titley","year":"2020","unstructured":"Titley, H. A., Bowyer, R. L. & Cloke, H. L. A global evaluation of multi-model ensemble tropical cyclone track probability forecasts. Q. J. R. Meteorol. Soc. 146, 531\u2013545 (2020).","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"8252_CR35","doi-asserted-by":"crossref","unstructured":"Katz, R. W. & Murphy, A. H. (eds) Economic Value of Weather and Climate Forecasts (Cambridge Univ. Press, 1997).","DOI":"10.1017\/CBO9780511608278"},{"key":"8252_CR36","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1175\/1520-0450(1966)005<0534:ANOTUO>2.0.CO;2","volume":"5","author":"AH Murphy","year":"1966","unstructured":"Murphy, A. H. A note on the utility of probabilistic predictions and the probability score in the cost-loss ratio decision situation. J. Appl. Meteorol. Climatol. 5, 534\u2013537 (1966).","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"8252_CR37","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1002\/qj.49712656313","volume":"126","author":"DS Richardson","year":"2000","unstructured":"Richardson, D. S. Skill and relative economic value of the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 126, 649\u2013667 (2000).","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"8252_CR38","doi-asserted-by":"crossref","unstructured":"Richardson, D. S. in Predictability and Economic Value (eds Palmer, T. & Hagedorn, R.) 628\u2013644 (Cambridge Univ. Press, 2006).","DOI":"10.1017\/CBO9780511617652.026"},{"key":"8252_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/met.25","volume":"15","author":"EE Ebert","year":"2008","unstructured":"Ebert, E. E. Fuzzy verification of high-resolution gridded forecasts: a review and proposed framework. Meteorol. Appl. 15, 51\u201364 (2008).","journal-title":"Meteorol. Appl."},{"key":"8252_CR40","unstructured":"Siebert, N. Development of methods for regional wind power forecasting. PhD thesis, \u00c9cole Nationale Sup\u00e9rieure des Mines de Paris (2008)."},{"key":"8252_CR41","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPWRS.2010.2065818","volume":"26","author":"MA Matos","year":"2011","unstructured":"Matos, M. A. & Bessa, R. J. Setting the operating reserve using probabilistic wind power forecasts. IEEE Trans. Power Syst. 26, 594\u2013603 (2011).","journal-title":"IEEE Trans. Power Syst."},{"key":"8252_CR42","doi-asserted-by":"publisher","first-page":"114986","DOI":"10.1016\/j.apenergy.2020.114986","volume":"268","author":"B Rachunok","year":"2020","unstructured":"Rachunok, B., Staid, A., Watson, J.-P. & Woodruff, D. L. Assessment of wind power scenario creation methods for stochastic power systems operations. Appl. Energy 268, 114986 (2020).","journal-title":"Appl. Energy"},{"key":"8252_CR43","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.esr.2019.01.006","volume":"24","author":"D Gielen","year":"2019","unstructured":"Gielen, D. et al. The role of renewable energy in the global energy transformation. Energy Strategy Rev. 24, 38\u201350 (2019).","journal-title":"Energy Strategy Rev."},{"key":"8252_CR44","unstructured":"Byers, L. et al. A Global Database of Power Plants (World Resources Institute, 2018)."},{"key":"8252_CR45","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/econometrics8020018","volume":"8","author":"AB Martinez","year":"2020","unstructured":"Martinez, A. B. Forecast accuracy matters for hurricane damage. Econometrics 8, 18 (2020).","journal-title":"Econometrics"},{"key":"8252_CR46","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.tcrr.2023.11.003","volume":"12","author":"JP Dunion","year":"2023","unstructured":"Dunion, J. P. et al. Recommendations for improved tropical cyclone formation and position probabilistic forecast products. Trop. Cyclone Res. Rev. 12, 241\u2013258 (2023).","journal-title":"Trop. Cyclone Res. Rev."},{"key":"8252_CR47","doi-asserted-by":"publisher","first-page":"5023","DOI":"10.5194\/gmd-14-5023-2021","volume":"14","author":"PA Ullrich","year":"2021","unstructured":"Ullrich, P. A. et al. TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets. Geosci. Model Dev. 14, 5023\u20135048 (2021).","journal-title":"Geosci. Model Dev."},{"key":"8252_CR48","unstructured":"Magnusson, L. et al. Tropical Cyclone Activities at ECMWF. ECMWF Technical Memo 888 (ECMWF, 2021)."},{"key":"8252_CR49","unstructured":"Salimans, T. & Ho, J. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations (OpenReview.net, 2022)."},{"key":"8252_CR50","unstructured":"Huang, L., Gianinazzi, L., Yu, Y., Dueben, P. D. & Hoefler, T. DiffDA: a diffusion model for weather-scale data assimilation. In Forty-first International Conference on Machine Learning (OpenReview.net, 2024)."},{"key":"8252_CR51","doi-asserted-by":"publisher","first-page":"eadk4489","DOI":"10.1126\/sciadv.adk4489","volume":"10","author":"L Li","year":"2024","unstructured":"Li, L., Carver, R., Lopez-Gomez, I., Sha, F. & Anderson, J. Generative emulation of weather forecast ensembles with diffusion models. Sci. Adv. 10, eadk4489 (2024).","journal-title":"Sci. Adv."},{"key":"8252_CR52","doi-asserted-by":"crossref","unstructured":"Addison, H., Kendon, E., Ravuri, S., Aitchison, L. & Watson, P. Machine learning emulation of a local-scale UK climate model. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning (NeurIPS, 2022).","DOI":"10.5194\/egusphere-egu23-14253"},{"key":"8252_CR53","unstructured":"Lu, C. et al. DPM-Solver++: fast solver for guided sampling of diffusion probabilistic models. Preprint at arxiv.org\/abs\/2211.01095 (2022)."},{"key":"8252_CR54","unstructured":"Batzolis, G., Stanczuk, J., Sch\u00f6nlieb, C.-B. & Etmann, C. Conditional image generation with score-based diffusion models. Preprint at arxiv.org\/abs\/2111.13606 (2021)."},{"key":"8252_CR55","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840\u20136851 (2020).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"8252_CR56","unstructured":"Nguyen, T. Q. & Salazar, J. Transformers without tears: improving the normalization of self-attention. In Proc. 16th International Conference on Spoken Language Translation (eds Niehues, J. et al.) (ACL, 2019)."},{"key":"8252_CR57","unstructured":"Chen, M. et al. Adaspeech: adaptive text to speech for custom voice. In International Conference on Learning Representations (ICLR, 2021)."},{"key":"8252_CR58","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1080\/01621459.1994.10476870","volume":"89","author":"DN Politis","year":"1994","unstructured":"Politis, D. N. & Romano, J. P. The stationary bootstrap. J. Am. Stat. Assoc. 89, 1303\u20131313 (1994).","journal-title":"J. Am. Stat. Assoc."},{"key":"8252_CR59","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1081\/ETC-120028836","volume":"23","author":"DN Politis","year":"2004","unstructured":"Politis, D. N. & White, H. Automatic block-length selection for the dependent bootstrap. Econ. Rev. 23, 53\u201370 (2004).","journal-title":"Econ. Rev."},{"key":"8252_CR60","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1080\/07474930802459016","volume":"28","author":"A Patton","year":"2009","unstructured":"Patton, A., Politis, D. N. & White, H. Correction to \u201cAutomatic block-length selection for the dependent bootstrap\u201d by D. Politis and H. White. Econ. Rev. 28, 372\u2013375 (2009).","journal-title":"Econ. Rev."},{"key":"8252_CR61","doi-asserted-by":"crossref","unstructured":"Davison, A. C. & Hinkley, D. V. Bootstrap Methods and their Application, 100\u2013101 (Cambridge Univ. Press, 1997).","DOI":"10.1017\/CBO9780511802843"},{"key":"8252_CR62","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1080\/10618600.2020.1714633","volume":"29","author":"B Efron","year":"2020","unstructured":"Efron, B. & Narasimhan, B. The automatic construction of bootstrap confidence intervals. J. Comput. Graph. Stat. 29, 608\u2013619 (2020).","journal-title":"J. Comput. Graph. Stat."},{"key":"8252_CR63","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1175\/2009BAMS2755.1","volume":"91","author":"KR Knapp","year":"2010","unstructured":"Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neumann, C. J. The international best track archive for climate stewardship (IBTrACS): unifying tropical cyclone data. Bull. Am. Meteorol. Soc. 91, 363\u2013376 (2010).","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"8252_CR64","unstructured":"Gahtan, J. et al. International Best Track Archive for Climate Stewardship (IBTrACS) Project v.4r01. NOAA National Centers for Environmental Information (NOAA, 2024)."},{"key":"8252_CR65","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.5194\/gmd-10-1069-2017","volume":"10","author":"PA Ullrich","year":"2017","unstructured":"Ullrich, P. A. & Zarzycki, C. M. TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids. Geosci. Model Dev. 10, 1069\u20131090 (2017).","journal-title":"Geosci. Model Dev."},{"key":"8252_CR66","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1002\/2016GL071606","volume":"44","author":"CM Zarzycki","year":"2017","unstructured":"Zarzycki, C. M. & Ullrich, P. A. Assessing sensitivities in algorithmic detection of tropical cyclones in climate data. Geophys. Res. Lett. 44, 1141\u20131149 (2017).","journal-title":"Geophys. Res. Lett."},{"key":"8252_CR67","doi-asserted-by":"crossref","unstructured":"King, J., Clifton, A. & Hodge, B. M. Validation of Power Output for the Wind Toolkit (NREL, 2014).","DOI":"10.2172\/1159354"},{"key":"8252_CR68","unstructured":"Lean, P., Bonavita, M., H\u00f3lm, E., Bormann, N. & McNally, T. Continuous data assimilation for the IFS. ECMWF Newsletter 21\u201326 (2019)."},{"key":"8252_CR69","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90\u201395 (2007).","journal-title":"Comput. Sci. Eng."},{"key":"8252_CR70","unstructured":"Met Office. Cartopy: a cartographic Python library with a Matplotlib interface. Exeter, Devon (2010\u20132015)."}],"container-title":["Nature"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41586-024-08252-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-024-08252-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-024-08252-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T14:08:31Z","timestamp":1734962911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41586-024-08252-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":70,"journal-issue":{"issue":"8044","published-print":{"date-parts":[[2025,1,2]]}},"alternative-id":["8252"],"URL":"https:\/\/doi.org\/10.1038\/s41586-024-08252-9","relation":{},"ISSN":["0028-0836","1476-4687"],"issn-type":[{"value":"0028-0836","type":"print"},{"value":"1476-4687","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"I.P., A.S.-G., F.A., T.R.A., A.E.K., D.M., T.E., J.S., S.M., P.B., R.L. and M.W. are employees of Alphabet and own Alphabet stock. Provisional patent 63\/614,461 was filed covering the algorithm described in this paper, listing the authors I.P., M.W., A.S.-G., F.A., R.L. and P.B. as inventors. The authors declare no other competing interests related to the paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}