Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-2797-2019
https://doi.org/10.5194/gmd-12-2797-2019
Development and technical paper
 | 
10 Jul 2019
Development and technical paper |  | 10 Jul 2019

Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

Sebastian Scher and Gabriele Messori

Related authors

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021,https://doi.org/10.5194/hess-25-3207-2021, 2021
Short summary
A new view of heat wave dynamics and predictability over the eastern Mediterranean
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021,https://doi.org/10.5194/esd-12-133-2021, 2021
Short summary
Generalization properties of feed-forward neural networks trained on Lorenz systems
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019,https://doi.org/10.5194/npg-26-381-2019, 2019
Short summary

Related subject area

Atmospheric sciences
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024,https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary

Cited articles

Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Buschow, S. and Friederichs, P.: Local dimension and recurrent circulation patterns in long-term climate simulations, arXiv preprint arXiv:1803.11255, 2018. a
C3S: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), available at: https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 7 June 2019), 2017. a, b
Coors, B., Paul Condurache, A., and Geiger, A.: Spherenet: Learning spherical representations for detection and classification in omnidirectional images, in: Proceedings of the European Conference on Computer Vision (ECCV), September 2018, Munich, Germany, 518–533, 2018. a
Download
Short summary
Currently, weather forecasts are mainly produced by using computer models based on physical equations. It is an appealing idea to use neural networks and “deep learning” for weather forecasting instead. We successfully test the possibility of using deep learning for weather forecasting by considering climate models as simplified versions of reality. Our work therefore is a step towards potentially using deep learning to replace or accompany current weather forecasting models.