Fetching the paper…
Reading the bibliography…
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting.
Abbe, C., 1901: The physical basis of long-range weather forecasts. Monthly Weather Review
1901
Earlier work this paper cites.
Bjerknes, V., 1904: Das Problem der Wettervorhersage, betrachtet vom Standpunkte der Mechanik und der Physik. Meteorologische Zeitschrift
1904
Earlier work this paper cites.
Hersbach, H., 2023: ERA5 reanalysis now available from 1940. ECMWF Newsletter
1940
Earlier work this paper cites.
Mason, I., 1982: A model for assessment of weather forecasts. Aust. Met. Mag
1982
Earlier work this paper cites.
Murphy, A. H., and R. L. Winkler, 1987: A general framework for forecast verification. Mon. Wea. Rev
1987
Earlier work this paper cites.
Harvey, L. O., J. K. Hammond, C. Lusk, and E. Mross, 1992: The application of signal detection theory to weather forecasting behavior. Mon. Wea. Rev
1992
Earlier work this paper cites.
Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: is it real or is it the varying climatology? Quart. J. Roy. Meteor. Soc
2006
Earlier work this paper cites.
Wilks, D. S., 2006: Statistical methods in the atmospheric sciences
2006
Earlier work this paper cites.
Leutbecher, M., and T. Palmer, 2008: Ensemble forecasting. Journal of Computational Physics
2007
Earlier work this paper cites.
2010
Earlier work this paper cites.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data. Bulletin of the American Meteorological Society
2010
Earlier work this paper cites.
Jolliffe, I. T., and D. B. Stephenson, 2011: Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd Edition
2011
Earlier work this paper cites.
Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature
2015
Earlier work this paper cites.
Ingleby, B., 2015: Global assimilation of air temperature, humidity, wind and pressure from surface stations. Quarterly Journal of the Royal Meteorological Society
2015
Earlier work this paper cites.
Geer, A. J., 2016: Significance of changes in medium-range forecast scores. Tellus A: Dynamic Meteorology and Oceanography
2016
Cited alongside, same era.
Dueben, P. D., and P. Bauer, 2018: Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development
2018
Cited alongside, same era.
Ben Bouallègue, Z., L. Magnusson, T. Haiden, and D. S. Richardson, 2019: Monitoring trends in ensemble forecast performance focusing on surface variables and high-impact events. Quart. J. Roy. Meteor. Soc
2019
Cited alongside, same era.
McGovern, A., R. Lagerquist, D. J. Gagne, G. E. Jergensen, K. L. Elmore, C. R. Homeyer, and T. Smith, 2019: Making the black box more transparent: Understanding the physical implications of machine learning. Bulletin of the American Meteorological Society
2019
Cited alongside, same era.
Ben Bouallègue, Z., and D. S. Richardson, 2022: On the roc area of ensemble forecasts for rare events. Weather and Forecasting
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Bi, K., L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, 2023: Accurate medium-range global weather forecasting with 3D neural networks. Nature
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Weyn, J. A., D. R. Durran, and R. Caruana, 2019: Can machines learn to predict weather? using deep learning to predict gridded 500-hpa geopotential height from historical weather data. Journal of Advances in Modeling Earth Systems
2019
Cited alongside, same era.
Day, J. J., G. Arduini, I. Sandu, L. Magnusson, A. Beljaars, G. Balsamo, M. Rodwell, and D. Richardson, 2020: Measuring the impact of a new snow model using surface energy budget process relationships. Journal of Advances in Modeling Earth Systems
2020
Cited alongside, same era.
Leutbecher, M., and Z. Ben Bouallègue, 2020: On the probabilistic skill of dual-resolution ensemble forecasts. Quarterly Journal of the Royal Meteorological Society
2020
Cited alongside, same era.
Sandu, I., and Coauthors, 2020: Addressing near-surface forecast biases: outcomes of the ECMWF project ’Understanding uncertainties in surface atmosphere exchange’ (USURF). ECMWF Technical Memorandum
2020
Cited alongside, same era.
Weyn, J. A., D. R. Durran, and R. Caruana, 2020: Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. Journal of Advances in Modeling Earth Systems
2020
Cited alongside, same era.
Forbes, R., P. Laloyaux, and M. Rodwell, 2021: IFS upgrade improves moist physics and use of satellite observations. ECMWF Newsletter
2021
Cited alongside, same era.
Magnusson, L., and Coauthors, 2021: Tropical cyclone activities at ECMWF. ECMWF Technical Memorandum
2021
Cited alongside, same era.
Rasp, S., and N. Thuerey, 2021: Data-driven medium-range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench. Journal of Advances in Modeling Earth Systems
2021
Cited alongside, same era.
2023
Closest in time.
de Burgh-Day, C. O., and T. Leeuwenburg, 2023: Machine learning for numerical weather and climate modelling: a review. EGUsphere
2023
Closest in time.
Ebert-Uphoff, I., and K. Hilburn, 2023: The outlook for AI weather prediction. Nature
2023
Closest in time.
Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, and C. J. I. Schreck, 2018: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4. NOAA National Centers for Environmental Information. doi: 10.25921/82ty-9e16 [access date: July 2023]
2023
Closest in time.
Lang, S., D. Schepers, and M. Rodwell, 2023: IFS upgrade brings many improvements and unifies medium-range resolutions. ECMWF Newsletter
2023
Closest in time.
Magnusson, L., 2023: First exploration of forecasts for extreme weather cases with data-driven models at ECMWF. ECMWF Newsl
2023
Closest in time.
Majumdar, S. J., L. Magnusson, P. Bechtold, J. R. Bidlot, and J. D. Doyle, 2023: Advanced tropical cyclone prediction using the experimental global ecmwf and operational regional coamps-tc systems. Monthly Weather Review
2048
Closest in time.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society
2049
Closest in time.
Magnusson, L., J.-H. Chen, S.-J. Lin, L. Zhou, and X. Chen, 2019: Dependence on initial conditions versus model formulations for medium-range forecast error variations. Quarterly Journal of the Royal Meteorological Society
2085
Closest in time.