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Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems.
Brankovic, C., Palmer, T. N., Molteni, F., Tibaldi, S., and Cubasch, U.: Extended-range predictions with ECMWF models: Time-lagged ensemble forecasting, Quarterly Journal of the Royal Meteorological Society, 116, 867–912, 10.1002/qj.49711649405 , 1990
1990
Earlier work this paper cites.
Toth, Z. and Kalnay, E.: Ensemble Forecasting at NMC: The Generation of Perturbations, Bulletin of the American Meteorological Society, 74, 2317–2330, 10.1175/1520-0477(1993)074<2317:efantg>2.0.co;2 , 1993
1993
Earlier work this paper cites.
Toth, Z. and Kalnay, E.: Ensemble Forecasting at NCEP and the Breeding Method, Monthly Weather Review, 125, 3297–3319, 10.1175/1520-0493(1997)125<3297:efanat>2.0.co;2 , 1997
1997
Earlier work this paper cites.
Lalaurette, F.: Early detection of abnormal weather using a probabilistic Extreme Forecast Index, 10.21957/ehfunckhs , 2002
2002
Earlier work this paper cites.
Palmer, T., Buizza, R., Hagedorn, R., Lawrence, A., Leutbecher, M., and Smith, L.: Ensemble prediction: A pedagogical perspective, ECMWF newsletter, 106, 10–17, 2006
2006
Earlier work this paper cites.
Zsótér, E.: Recent developments in extreme weather forecasting, 10.21957/kl9821hnc7 , 2006
2006
Earlier work this paper cites.
Leutbecher, M. and Palmer, T.: Ensemble forecasting, Journal of Computational Physics, 227, 3515–3539, 10.1016/j.jcp.2007.02.014 , 2008
2008
Earlier work this paper cites.
Gneiting, T. and Ranjan, R.: Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules, Journal of Business & Economic Statistics, 29, 411–422, https://EconPapers.repec.org/RePEc:bes:jnlbes:v:29:i:3:y:2011:p:411-422 , 2011
2011
Earlier work this paper cites.
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R.: Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?, Journal of Hydrometeorology, 15, 1708–1713, 10.1175/jhm-d-14-0008.1 , 2014
2014
Earlier work this paper cites.
Mittermaier, M. P.: A Strategy for Verifying Near-Convection-Resolving Model Forecasts at Observing Sites, Weather and Forecasting, 29, 185–204, 10.1175/waf-d-12-00075.1 , 2014
2014
Earlier work this paper cites.
Lavers, D. A., Pappenberger, F., Richardson, D. S., and Zsoter, E.: ECMWF Extreme Forecast Index for water vapor transport: A forecast tool for atmospheric rivers and extreme precipitation, Geophysical Research Letters, 43, 10.1002/2016gl071320 , 2016
2016
Earlier work this paper cites.
Lerch, S., Thorarinsdottir, T. L., Ravazzolo, F., and Gneiting, T.: Forecaster’s Dilemma: Extreme Events and Forecast Evaluation, Statistical Science, 32, 10.1214/16-sts588 , 2017
2017
Earlier work this paper cites.
Murage, P., Hajat, S., and Kovats, R. S.: Effect of night-time temperatures on cause and age-specific mortality in London, Environmental Epidemiology, 1, e005, 10.1097/ee9.0000000000000005 , 2017
2017
Earlier work this paper cites.
Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B. R., and Ott, E.: A Machine Learning-Based Global Atmospheric Forecast Model, Geophysical Research Letters, 47, 10.1029/2020gl087776 , 2020
2020
Earlier work this paper cites.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, 10.1002/qj.3803 , 2020
2020
Earlier work this paper cites.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A.: Fourier Neural Operator for Parametric Partial Differential Equations, 10.48550/ARXIV.2010.08895 , 2020
2020
Earlier work this paper cites.
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., and Thuerey, N.: WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting, Journal of Advances in Modeling Earth Systems, 12, 10.1029/2020ms002203 , 2020
2020
Earlier work this paper cites.
Scher, S. and Messori, G.: Ensemble Methods for Neural Network-Based Weather Forecasts, Journal of Advances in Modeling Earth Systems, 13, 10.1029/2020ms002331 , 2021
2021
Earlier work this paper cites.
Seneviratne, S., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Luca, A. D., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S., Wehner, M., and Zhou, B.: Weather and Climate Extreme Events, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Ṕéan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., pp. 1513–1766, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 10.1017/9781009157896.013 , 2021
2021
Earlier work this paper cites.
Weyn, J. A., Durran, D. R., Caruana, R., and Cresswell-Clay, N.: Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models, Journal of Advances in Modeling Earth Systems, 13, 10.1029/2021ms002502 , 2021
2021
Earlier work this paper cites.
Allen, S., Ginsbourger, D., and Ziegel, J.: Evaluating forecasts for high-impact events using transformed kernel scores, 10.48550/ARXIV.2202.12732 , 2022
2022
Earlier work this paper cites.
Balch, J. K., Abatzoglou, J. T., Joseph, M. B., Koontz, M. J., Mahood, A. L., McGlinchy, J., Cattau, M. E., and Williams, A. P.: Warming weakens the night-time barrier to global fire, Nature, 602, 442–448, 10.1038/s41586-021-04325-1 , 2022
2022
Earlier work this paper cites.
Bercos-Hickey, E., O’Brien, T. A., Wehner, M. F., Zhang, L., Patricola, C. M., Huang, H., and Risser, M. D.: Anthropogenic Contributions to the 2021 Pacific Northwest Heatwave, Geophysical Research Letters, 49, 10.1029/2022gl099396 , 2022
2022
Earlier work this paper cites.
He, C., Kim, H., Hashizume, M., Lee, W., Honda, Y., Kim, S. E., Kinney, P. L., Schneider, A., Zhang, Y., Zhu, Y., Zhou, L., Chen, R., and Kan, H.: The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study, The Lancet Planetary Health, 6, e648–e657, 10.1016/s2542-5196(22)00139-5 , 2022
2022
Cited alongside, same era.
Keisler, R.: Forecasting Global Weather with Graph Neural Networks, 10.48550/ARXIV.2202.07575 , 2022
2022
Cited alongside, same era.
Lu, X., O’Neill, C. M., Warner, S., Xiong, Q., Chen, X., Wells, R., and Penfield, S.: Winter warming post floral initiation delays flowering via bud dormancy activation and affects yield in a winter annual crop, Proceedings of the National Academy of Sciences, 119, 10.1073/pnas.2204355119 , 2022
2022
Cited alongside, same era.
Lu, Y.-C. and Romps, D. M.: Extending the Heat Index, Journal of Applied Meteorology and Climatology, 61, 1367–1383, 10.1175/jamc-d-22-0021.1 , 2022
2022
Watt-Meyer, O., Dresdner, G., McGibbon, J., Clark, S. K., Henn, B., Duncan, J., Brenowitz, N. D., Kashinath, K., Pritchard, M. S., Bonev, B., Peters, M. E., and Bretherton, C. S.: ACE: A fast, skillful learned global atmospheric model for climate prediction, 10.48550/ARXIV.2310.02074 , 2023
2023
Later among the works it cites.
Baño-Medina, J., Sengupta, A., Watson-Parris, D., Hu, W., and Monache, L. D.: Towards calibrated ensembles of neural weather model forecasts, 10.22541/essoar.171536034.43833039/v1 , ESS Open Archive, 2024
2024
Closest in time.
Ben Bouallègue, Z., Clare, M. C. A., Magnusson, L., Gascón, E., Maier-Gerber, M., Janoušek, M., Rodwell, M., Pinault, F., Dramsch, J. S., Lang, S. T. K., Raoult, B., Rabier, F., Chevallier, M., Sandu, I., Dueben, P., Chantry, M., and Pappenberger, F.: The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context, Bulletin of the American Meteorological Society, 105, E864–E883, 10.1175/bams-d-23-0162.1 , 2024
2024
Closest in time.
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Cited alongside, same era.
McKinnon, K. A. and Simpson, I. R.: How Unexpected Was the 2021 Pacific Northwest Heatwave?, Geophysical Research Letters, 49, 10.1029/2022gl100380 , 2022
2022
Cited alongside, same era.
Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar, A.: FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators, 10.48550/ARXIV.2202.11214 , 2022
2022
Cited alongside, same era.
Vargas Zeppetello, L. R., Raftery, A. E., and Battisti, D. S.: Probabilistic projections of increased heat stress driven by climate change, Communications Earth and Environment, 3, 10.1038/s43247-022-00524-4 , 2022
2022
Cited alongside, same era.
Agrawal, S., Carver, R., Gazen, C., Maddy, E., Krasnopolsky, V., Bromberg, C., Ontiveros, Z., Russell, T., Hickey, J., and Boukabara, S.: A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts, 10.48550/ARXIV.2303.16301 , 2023
2023
Cited alongside, same era.
Allen, S., Bhend, J., Martius, O., and Ziegel, J.: Weighted Verification Tools to Evaluate Univariate and Multivariate Probabilistic Forecasts for High-Impact Weather Events, Weather and Forecasting, 38, 499–516, 10.1175/waf-d-22-0161.1 , 2023
2023
Cited alongside, same era.
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, 10.1038/s41586-023-06185-3 , 2023
2023
Cited alongside, same era.
Bonavita, M.: On some limitations of data-driven weather forecasting models, 10.48550/ARXIV.2309.08473 , 2023
2023
Cited alongside, same era.
Bonev, B., Kurth, T., Hundt, C., Pathak, J., Baust, M., Kashinath, K., and Anandkumar, A.: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere, 10.48550/ARXIV.2306.03838 , 2023
2023
Cited alongside, same era.
Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J., Dong, H., Vaughan, A., Gupta, J. K., Tambiratnam, K., Archibald, A., Heider, E., Welling, M., Turner, R. E., and Perdikaris, P.: Aurora: A Foundation Model of the Atmosphere, 10.48550/ARXIV.2405.13063 , 2024
2024
Closest in time.
Bonev, B., Kamenev, A., and Kurth, T.: Makani: Massively parallel training of machine-learning based weather and climate models, https://github.com/NVIDIA/modulus-makani/tree/v0.1.0 , accessed 07.17.2024, 2024
2024
Closest in time.
Brenowitz, N. D., Cohen, Y., Pathak, J., Mahesh, A., Bonev, B., Kurth, T., Durran, D. R., Harrington, P., and Pritchard, M. S.: A Practical Probabilistic Benchmark for AI Weather Models, 10.48550/ARXIV.2401.15305 , 2024
2024
Closest in time.
Bülte, C., Horat, N., Quinting, J., and Lerch, S.: Uncertainty quantification for data-driven weather models, 10.48550/ARXIV.2403.13458 , 2024
2024
Closest in time.
Collins, W., Pritchard, M., Brenowitz, N., Cohen, Y., Harrington, P., Kashinath, K., Mahesh, A., and Subramanian, S.: Huge Ensembles of Weather Extremes using the Fourier Forecasting Neural Network, 10.5194/egusphere-egu24-4460 , Abstract EGU24-4460, Session ITS1.1/CL0.1.17, Machine Learning for Climate Science, EGU General Assembly, Vienna, Austria, 2024
2024
Closest in time.
Couairon, G., Lessig, C., Charantonis, A., and Monteleoni, C.: ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution, 10.48550/ARXIV.2405.14527 , 2024
2024
Closest in time.
ECMWF: IFS Documentation, https://www.ecmwf.int/en/publications/ifs-documentation , https://www.ecmwf.int/en/publications/ifs-documentation , accessed: 2024-07-17
2024
Closest in time.
Esper, J., Torbenson, M., and Büntgen, U.: 2023 summer warmth unparalleled over the past 2,000 years, Nature, 10.1038/s41586-024-07512-y , 2024
2024
Closest in time.
Lang, S., Alexe, M., Chantry, M., Dramsch, J., Pinault, F., Raoult, B., Clare, M. C. A., Lessig, C., Maier-Gerber, M., Magnusson, L., Bouallègue, Z. B., Nemesio, A. P., Dueben, P. D., Brown, A., Pappenberger, F., and Rabier, F.: AIFS - ECMWF’s data-driven forecasting system, 10.48550/ARXIV.2406.01465 , 2024
2024
Closest in time.
Li, L., Carver, R., Lopez-Gomez, I., Sha, F., and Anderson, J.: Generative emulation of weather forecast ensembles with diffusion models, Science Advances, 10, 10.1126/sciadv.adk4489 , 2024
2024
Closest in time.
Liu, X., Saravanan, R., Fu, D., Chang, P., Patricola, C. M., and O’Brien, T. A.: How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?, Geophysical Research Letters, 51, 10.1029/2024gl108160 , 2024
2024
Closest in time.
Mahesh, A., Collins, W., Bonev, B., Brenowitz, N., Cohen, Y., Harrington, P., Kashinath, K., Kurth, T., North, J., OBrien, T., Pritchard, M., Pruitt, D., Risser, M., Subramanian, S., and Willard, J.: Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators, 10.48550/ARXIV.2408.01581 , 2024
2024
Closest in time.
McGovern, A., Bostrom, A., McGraw, M., Chase, R. J., Gagne, D. J., Ebert-Uphoff, I., Musgrave, K. D., and Schumacher, A.: Identifying and Categorizing Bias in AI/ML for Earth Sciences, Bulletin of the American Meteorological Society, 105, E567–E583, 10.1175/bams-d-23-0196.1 , 2024
2024
Closest in time.
Olivetti, L. and Messori, G.: Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather and GraphCast, EGUsphere, 10.5194/egusphere-2024-1042 , 2024
2024
Closest in time.
Pasche, O. C., Wider, J., Zhang, Z., Zscheischler, J., and Engelke, S.: Validating Deep Learning Weather Forecast Models on Recent High-Impact Extreme Events, 10.48550/ARXIV.2404.17652 , 2024
2024
Closest in time.
Ramavajjala, V.: HEAL-ViT: Vision Transformers on a spherical mesh for medium-range weather forecasting, 10.48550/ARXIV.2403.17016 , 2024
2024
Closest in time.
Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Ben Bouallegue, Z., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., and Sha, F.: WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models, Journal of Advances in Modeling Earth Systems, 16, 10.1029/2023ms004019 , 2024
2024
Closest in time.
Willard, J. D., Harrington, P., Subramanian, S., Mahesh, A., O’Brien, T. A., and Collins, W. D.: Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction, 10.48550/ARXIV.2404.19630 , 2024
2024
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Zhang, L., Risser, M. D., Wehner, M. F., and O’Brien, T. A.: Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave, Journal of Agricultural, Biological and Environmental Statistics, 10.1007/s13253-024-00636-8 , 2024
2024
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Zhong, X., Chen, L., Li, H., Feng, J., and Lu, B.: FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting, 10.48550/ARXIV.2405.05925 , 2024
2024
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