Fetching the paper…
Reading the bibliography…
Artificial intelligence (AI)-based data-driven weather forecasting models have experienced rapid progress over the last years.
Scoring rules for continuous probability distributions
Matheson, J. E. and Winkler, R. L. (1976) · 1976
Earlier work this paper cites.
The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B. and Wilby, R. (2004) · 2004
Earlier work this paper cites.
Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
Ensemble forecasting
Leutbecher, M. and Palmer, T. (2008) · 2008
Earlier work this paper cites.
Flow-dependent versus flow-independent initial perturbations for ensemble prediction
Magnusson, L., Nycander, J. and Källén, E. (2009) · 2009
Earlier work this paper cites.
Ensemble of data assimilations at ECMWF
Isaksen, L., Bonavita, M., Buizza, R., Fisher, M., Haseler, J., Leutbecher, M. and Raynaud, L. (2010) · 2010
Earlier work this paper cites.
Uncertainty quantification in complex simulation models using ensemble copula coupling
Schefzik, R., Thorarinsdottir, T. L. and Gneiting, T. (2013) · 2013
Earlier work this paper cites.
Why should ensemble spread match the rmse of the ensemble mean?
Fortin, V., Abaza, M., Anctil, F. and Turcotte, R. (2014) · 2014
Earlier work this paper cites.
Probabilistic forecasting
Gneiting, T. and Katzfuss, M. (2014) · 2014
Earlier work this paper cites.
The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A. and Brunet, G. (2015) · 2015
Earlier work this paper cites.
Forecaster’s dilemma: Extreme events and forecast evaluation
Lerch, S., Thorarinsdottir, T. L., Ravazzolo, F. and Gneiting, T. (2017) · 2017
Earlier work this paper cites.
Neural networks for postprocessing ensemble weather forecasts
Rasp, S. and Lerch, S. (2018) · 2018
Earlier work this paper cites.
Evaluating probabilistic forecasts with scoringRules
Jordan, A., Krüger, F. and Lerch, S. (2019) · 2019
Earlier work this paper cites.
Simulation-based comparison of multivariate ensemble post-processing methods
Lerch, S., Baran, S., Möller, A., Groß, J., Schefzik, R., Hemri, S. and Graeter, M. (2020) · 2020
Earlier work this paper cites.
A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Angelopoulos, A. N. and Bates, S. (2021) · 2021
Cited alongside, same era.
Deep learning for post-processing ensemble weather forecasts
Grönquist, P., Yao, C., Ben-Nun, T., Dryden, N., Dueben, P., Li, S. and Hoefler, T. (2021) · 2021
Cited alongside, same era.
Isotonic distributional regression
Henzi, A., Ziegel, J. F. and Gneiting, T. (2021) · 2021
Cited alongside, same era.
Ensemble methods for neural network-based weather forecasts
Scher, S. and Messori, G. (2021) · 2021
Cited alongside, same era.
Statistical postprocessing for weather forecasts: Review, challenges, and avenues in a big data world
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Bouallègue, Z. B., Bhend, J., Dabernig, M., Cruz, L. D., Hieta, L., Mestre, O., Moret, L., Plenković, I. O., Schmeits, M., Taillardat, M., den Bergh, J. V., Schaeybroeck, B. V., Whan, K. and Ylhaisi, J. (2021) · 2021
Cited alongside, same era.
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
Later among the works it cites.
Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts
Lakatos, M., Lerch, S., Hemri, S. and Baran, S. (2023) · 2023
Later among the works it cites.
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning
Lessig, C., Luise, I., Gong, B., Langguth, M., Stadler, S. and Schultz, M. (2023) · 2023
Later among the works it cites.
Gencast: Diffusion-based ensemble forecasting for medium-range weather
Price, I., Sanchez-Gonzalez, A., Alet, F., Ewalds, T., El-Kadi, A., Stott, J., Mohamed, S., Battaglia, P., Lam, R. and Willson, M. (2023) · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
ENS-10: A dataset for post-processing ensemble weather forecasts
Ashkboos, S., Huang, L., Dryden, N., Ben-Nun, T., Dueben, P. D., Gianinazzi, L., Kummer, L. N. and Hoefler, T. (2022) · 2022
Cited alongside, same era.
Probabilistic predictions from deterministic atmospheric river forecasts with deep learning
Chapman, W. E., Monache, L. D., Alessandrini, S., Subramanian, A. C., Ralph, F. M., Xie, S.-P., Lerch, S. and Hayatbini, N. (2022) · 2022
Cited alongside, same era.
Forecasting global weather with graph neural networks
Keisler, R. (2022) · 2022
Cited alongside, same era.
GraphCast: Learning skillful medium-range global weather forecasting
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Pritzel, A., Ravuri, S., Ewalds, T., Alet, F., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S. and Battaglia, P. (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. (2022) · 2022
Cited alongside, same era.
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Schulz, B. and Lerch, S. (2022) · 2022
Cited alongside, same era.
FuXi-ENS: A machine learning model for medium-range ensemble weather forecasting
Zhong, X., Chen, L., Li, H., Feng, J. and Lu, B. (2024) · 2022
Cited alongside, same era.
Can artificial intelligence-based weather prediction models simulate the butterfly effect?
Selz, T. and Craig, G. C. (2023) · 2023
Later among the works it cites.
Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
Bremnes, J. B., Nipen, T. N. and Seierstad, I. A. (2024) · 2024
Closest in time.
A Practical Probabilistic Benchmark for AI Weather Models
Brenowitz, N. D., Cohen, Y., Pathak, J., Mahesh, A., Bonev, B., Kurth, T., Durran, D. R., Harrington, P. and Pritchard, M. S. (2024) · 2024
Closest in time.
Generative machine learning methods for multivariate ensemble post-processing
Chen, J., Janke, T., Steinke, F. and Lerch, S. (2024) · 2024
Closest in time.
Deep learning for post-processing global probabilistic forecasts on sub-seasonal time scales
Horat, N. and Lerch, S. (2024) · 2024
Closest in time.
Aifs – ecmwf’s data-driven forecasting system
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. (2024) · 2024
Closest in time.
Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts
Primo, C., Schulz, B., Lerch, S. and Hess, R. (2024) · 2024
Closest in time.
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russel, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Bouallegue, Z. B., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A. and Sha, F. (2024) · 2024
Closest in time.
Physics-based vs data-driven 24-hour probabilistic forecasts of precipitation for northern tropical africa
Walz, E.-M., Knippertz, P., Fink, A. H., Köhler, G. and Gneiting, T. (2024b) · 2031
Closest in time.
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., 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.-N. (2020) · 2049
Closest in time.
Potential use of an ensemble of analyses in the ECMWF ensemble prediction system
Buizza, R., Leutbecher, M. and Isaksen, L. (2008) · 2066
Closest in time.