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WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al.
Deterministic Nonperiodic Flow
Lorenz, E. N. (1963) · 1963
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The predictability of a flow which possesses many scales of motion
Lorenz, E. N. (1969) · 1969
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Ensemble prediction
Palmer, T., Molteni, F., Mureau, R., Buizza, R., Chapelet, P., and Tribbia, J. (1993) · 1993
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Ensemble Forecasting at NMC: The Generation of Perturbations
Toth, Z. and Kalnay, E. (1993) · 1993
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Stochastic representation of model uncertainties in the ECMWF ensemble prediction system
Buizza, R., Milleer, M., and Palmer, T. N. (1999) · 1999
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Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · 2002
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Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T. (2005) · 2005
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Statistical methods in the atmospheric sciences
Wilks, D. S. (2006) · 2006
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Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
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Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models
Stensrud, D. J. (2007) · 2007
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Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics
Zhang, F., Bei, N., Rotunno, R., Snyder, C., and Epifanio, C. C. (2007) · 2007
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Scale-dependent verification of ensemble forecasts
Jung, T. and Leutbecher, M. (2008) · 2008
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Learning Mesh-Based Simulation with Graph Networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W. (2021) · 2010
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A new equitable score suitable for verifying precipitation in numerical weather prediction: New Equitable Score for Precipitation in NWP
Rodwell, M. J., Richardson, D. S., Hewson, T. D., and Haiden, T. (2010) · 2010
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Factors Influencing Skill Improvements in the ECMWF Forecasting System
Magnusson, L. and Källén, E. (2013) · 2013
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Characteristics of Occasional Poor Medium-Range Weather Forecasts for Europe
Rodwell, M. J., Magnusson, L., Bauer, P., Bechtold, P., Bonavita, M., Cardinali, C., Diamantakis, M., Earnshaw, P., Garcia-Mendez, A., Isaksen, L., Källén, E., Klocke, D., Lopez, P., McNally, T., Persson, A., Prates, F., and Wedi, N. (2013) · 2013
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Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R. (2014) · 2014
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The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A., and Brunet, G. (2015) · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2017) · 2017
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Challenges and design choices for global weather and climate models based on machine learning
Dueben, P. D. and Bauer, P. (2018) · 2018
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Neural Networks for Postprocessing Ensemble Weather Forecasts
Rasp, S. and Lerch, S. (2018) · 2018
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Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning
Scher, S. (2018) · 2018
Data-Driven Medium-Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench
Rasp, S. and Thuerey, N. (2021) · 2021
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Manual on the Global Data-processing and Forecasting System (WMO-No. 485): Annex IV to the WMO Technical Regulations
(WMO), W. M. O. (2019) · 2021
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ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
Ashkboos, S., Huang, L., Dryden, N., Ben-Nun, T., Dueben, P., Gianinazzi, L., Kummer, L., and Hoefler, T. (2022) · 2022
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Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook
Dueben, P. D., Schultz, M. G., Chantry, M., Gagne, D. J., Hall, D. M., and McGovern, A. (2022) · 2022
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. (2018) · 2018
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Estimation of the Continuous Ranked Probability Score with Limited Information and Applications to Ensemble Weather Forecasts
Zamo, M. and Naveau, P. (2018) · 2018
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Estimating the Intrinsic Limit of Predictability Using a Stochastic Convection Scheme
Selz, T. (2019) · 2019
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Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data
Weyn, J. A., Durran, D. R., and Caruana, R. (2019) · 2019
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What Is the Predictability Limit of Midlatitude Weather?
Zhang, F., Sun, Y. Q., Magnusson, L., Buizza, R., Lin, S.-J., Chen, J.-H., and Emanuel, K. (2019) · 2019
Cited alongside, same era.
The ERA5 global reanalysis
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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. (2020) · 2020
Cited alongside, same era.
Garg, S., Rasp, S., and Thuerey, N. (2022) · 2022
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Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
Guibas, J., Mardani, M., Li, Z., Tao, A., Anandkumar, A., and Catanzaro, B. (2022) · 2022
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Forecasting Global Weather with Graph Neural Networks
Keisler, R. (2022) · 2022
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An evaluation of ERA5 precipitation for climate monitoring
Lavers, D. A., Simmons, A., Vamborg, F., and Rodwell, M. J. (2022) · 2022
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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. (2022) · 2022
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Deep Learning for Day Forecasts from Sparse Observations
Andrychowicz, M., Espeholt, L., Li, D., Merchant, S., Merose, A., Zyda, F., Agrawal, S., and Kalchbrenner, N. (2023) · 2023
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Accurate medium-range global weather forecasting with 3D neural networks
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q. (2023) · 2023
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Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Bonev, B., Kurth, T., Hundt, C., Pathak, J., Baust, M., Kashinath, K., and Anandkumar, A. (2023) · 2023
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The EUPPBench postprocessing benchmark dataset v1.0
Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A., Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M., and Vannitsem, S. (2023) · 2023
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Esteves, C., Slotine, J.-J., and Makadia, A. (2023) · 2023
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Neural General Circulation Models
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Lottes, J., Rasp, S., Düben, P., Klöwer, M., Hatfield, S., Battaglia, P., Sanchez-Gonzalez, A., Willson, M., Brenner, M. P., and Hoyer, S. (2023) · 2023
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Learning skillful medium-range global weather forecasting
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P. (2023) · 2023
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The Transition from Practical to Intrinsic Predictability of Midlatitude Weather
Selz, T., Riemer, M., and Craig, G. C. (2022) · 2030
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