Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks
Sebastian Scher
CORRESPONDING AUTHOR
Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Know-Center GmbH, Graz, Austria
Stefanie Peßenteiner
Department of Geography and Regional Science, University of Graz, Graz, Austria
Related authors
Florian Ladstädter, Matthias Stocker, Sebastian Scher, and Andrea K. Steiner
EGUsphere, https://doi.org/10.5194/egusphere-2025-2100, https://doi.org/10.5194/egusphere-2025-2100, 2025
Short summary
Short summary
The tropopause, the boundary between the lower and upper atmosphere, is a sensitive marker of climate change. We studied changes in tropopause height and temperature over the past two decades using precise satellite observations. We found warming in the tropics and rising tropopause heights in many regions, especially over Asia and the Middle East. These changes reflect how both atmospheric layers are responding to climate change and highlight the need for continued satellite monitoring.
Florina Roana Schalamon, Sebastian Scher, Andreas Trügler, Lea Hartl, Wolfgang Schöner, and Jakob Abermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4060, https://doi.org/10.5194/egusphere-2024-4060, 2025
Short summary
Short summary
Atmospheric patterns influence the air temperature in Greenland. We investigate two warming periods, from 1922–1932 and 1993–2007, both showing similar temperature increases. Using a neural network-based clustering method, we defined predominant atmospheric patterns for further analysis. Our findings reveal that while the connection between these patterns and local air temperature remains stable, the distribution of patterns changes between the warming periods and the full period (1900–2015).
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
Short summary
Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Florian Ladstädter, Matthias Stocker, Sebastian Scher, and Andrea K. Steiner
EGUsphere, https://doi.org/10.5194/egusphere-2025-2100, https://doi.org/10.5194/egusphere-2025-2100, 2025
Short summary
Short summary
The tropopause, the boundary between the lower and upper atmosphere, is a sensitive marker of climate change. We studied changes in tropopause height and temperature over the past two decades using precise satellite observations. We found warming in the tropics and rising tropopause heights in many regions, especially over Asia and the Middle East. These changes reflect how both atmospheric layers are responding to climate change and highlight the need for continued satellite monitoring.
Florina Roana Schalamon, Sebastian Scher, Andreas Trügler, Lea Hartl, Wolfgang Schöner, and Jakob Abermann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4060, https://doi.org/10.5194/egusphere-2024-4060, 2025
Short summary
Short summary
Atmospheric patterns influence the air temperature in Greenland. We investigate two warming periods, from 1922–1932 and 1993–2007, both showing similar temperature increases. Using a neural network-based clustering method, we defined predominant atmospheric patterns for further analysis. Our findings reveal that while the connection between these patterns and local air temperature remains stable, the distribution of patterns changes between the warming periods and the full period (1900–2015).
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
Short summary
Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Cited articles
Adadi, A. and Berrada, M.: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access, 6, 52138–52160, https://doi.org/10.1109/ACCESS.2018.2870052, 2018. a
Bihlo, A.: A generative adversarial network approach to (ensemble) weather
prediction, Neural Netw., 139, 1–16, https://doi.org/10.1016/j.neunet.2021.02.003, 2021. a
Breinl, K. and Di Baldassarre, G.: Space-time disaggregation of precipitation
and temperature across different climates and spatial scales, J. Hydrol.: Reg. Stud., 21, 126–146, https://doi.org/10.1016/j.ejrh.2018.12.002, 2019. a
Burian, S. J., Durrans, S. R., Tomic̆, S., Pimmel, R. L., and Chung Wai,
N.: Rainfall Disaggregation Using Artificial Neural Networks, J. Hydrol. Eng-ASCE, 5, 299–307, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(299), 2000. a
Burian, S. J., Durrans, S. R., Nix, S. J., and Pitt, R. E.: Training Artificial Neural Networks to Perform Rainfall Disaggregation, J. Hydrol. Eng., 6, 43–51, 2001. a
Chollet, F., et al.: Keras, GitHub, available at: https://github.com/keras-team/keras
(last access: 9 June 2021), 2015. a
Di Baldassarre, G., Castellarin, A., and Brath, A.: Relationships between
statistics of rainfall extremes and mean annual precipitation: an application
for design-storm estimation in northern central Italy, Hydrol. Earth Syst. Sci., 10, 589–601, https://doi.org/10.5194/hess-10-589-2006, 2006. a
Förster, K., Hanzer, F., Winter, B., Marke, T., and Strasser, U.: An
Open-Source MEteoroLOgical Observation Time Series DISaggregation Tool (MELODIST v0.1.1), Geosci. Model Dev., 9, 2315–2333,
https://doi.org/10.5194/gmd-9-2315-2016, 2016. a
Gagne II, D. J., Christensen, H. M., Subramanian, A. C., and Monahan, A. H.: Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model, arXiv: preprint, arXiv:1909.04711 [nlin, physics:physics, stat], 2019. a
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative Adversarial Nets, in: Advances in Neural Information Processing Systems 27, edited by: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., and Weinberger, K. Q., Curran Associates, Inc., 2672–2680, 2014. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score for
Ensemble Prediction Systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a
King, R., Hennigh, O., Mohan, A., and Chertkov, M.: From Deep to Physics-Informed Learning of Turbulence: Diagnostics, arXiv: preprint, arXiv:1810.07785 [nlin, physics:physics, stat], 2018. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, arXiv: preprint, arXiv:1412.6980 [cs], 2017. a
Kingma, D. P. and Welling, M.: Auto-Encoding Variational Bayes, arXiv: preprint arXiv:1312.6114 [cs, stat], 2014. a
Koutsoyiannis, D. and Onof, C.: Rainfall Disaggregation Using Adjusting
Procedures on a Poisson Cluster Model, J. Hydrol., 246, 109–122, https://doi.org/10.1016/S0022-1694(01)00363-8, 2001. a
Koutsoyiannis, D., Onof, C., and Wheater, H. S.: Multivariate Rainfall
Disaggregation at a Fine Timescale, Water Resour. Res., 39, 1173, https://doi.org/10.1029/2002WR001600, 2003. a
Kumar, J., Brooks, B.-G. J., Thornton, P. E., and Dietze, M. C.: Sub-daily
Statistical Downscaling of Meteorological Variables Using Neural Networks, Proced. Comput. Sci., 9, 887–896, https://doi.org/10.1016/j.procs.2012.04.095, 2012. a
Leinonen, J., Guillaume, A., and Yuan, T.: Reconstruction of Cloud Vertical
Structure With a Generative Adversarial Network, Geophys. Res. Lett., 46, 7035–7044, https://doi.org/10.1029/2019GL082532, 2019. a
Leinonen, J., Nerini, D., and Berne, A.: Stochastic Super-Resolution for
Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial
Network, IEEE T. Geosci. Remote, https://doi.org/10.1109/TGRS.2020.3032790, in press, 2020. a
Lewis, E., Fowler, H., Alexander, L., Dunn, R., McClean, F., Barbero, R.,
Guerreiro, S., Li, X.-F., and Blenkinsop, S.: GSDR: A Global Sub-Daily Rainfall Dataset, J. Climate, 32, 4715–4729, https://doi.org/10.1175/JCLI-D-18-0143.1, 2019. a
Martín, A., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K.,
Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, google research,
available at:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf (last access: 9 June 2021), 2015. a
Mirza, M. and Osindero, S.: Conditional Generative Adversarial Nets, arXiv: preprint, arXiv:1411.1784 [cs, stat], 2014. a
Müller, H. and Haberlandt, U.: Temporal Rainfall Disaggregation Using a
Multiplicative Cascade Model for Spatial Application in Urban Hydrology, J. Hydrol., 556, 847–864, https://doi.org/10.1016/j.jhydrol.2016.01.031, 2018. a
Müller-Thomy, H. and Sikorska-Senoner, A. E.: Does the complexity in
temporal precipitation disaggregation matter for a lumped hydrological
model?, Hydrolog. Sci. J., 64, 1453–1471, https://doi.org/10.1080/02626667.2019.1638926, 2019. a, b
Pui, A., Sharma, A., Mehrotra, R., Sivakumar, B., and Jeremiah, E.: A
Comparison of Alternatives for Daily to Sub-Daily Rainfall Disaggregation, J. Hydrol., 470-471, 138–157, https://doi.org/10.1016/j.jhydrol.2012.08.041, 2012.
a
Radford, A., Metz, L., and Chintala, S.: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, arXiv: preprint,
arXiv:1511.06434 [cs], 2016. a
Raut, B. A., Seed, A. W., Reeder, M. J., and Jakob, C.: A Multiplicative
Cascade Model for High-Resolution Space-Time Downscaling of Rainfall, J. Geophys. Res.-Atmos., 123, 2050–2067, https://doi.org/10.1002/2017JD027148, 2018. a, b
Rebora, N., Ferraris, L., von Hardenberg, J., and Provenzale, A.: RainFARM:
Rainfall Downscaling by a Filtered Autoregressive Model, J. Hydrometeorol., 7, 724–738, https://doi.org/10.1175/JHM517.1, 2006. a
Samek, W., Wiegand, T., and Müller, K.-R.: Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models, arXiv: preprint, arXiv:1708.08296 [cs, stat], 2017. a
Scher, S.: pr-disagg-radar-gan, Github, available at: https://github.com/sipposip/pr-disagg-radar-gan, last access: 9 June 2021. a
Scher, S. and Peßenteiner, S.: pr-disagg-gan, Zenodo, https://doi.org/10.5281/zenodo.3733065, 2020. a
Sharma, A. and Srikanthan, S.: Continuous Rainfall Simulation: A Nonparametric Alternative, in: 30th Hydrology & Water Resources Symposium: Past, Present & Future, 4–7 December 2006, Launceston, Tasmania, p. 86, 2006. a
SMHI: Länksida för radar nedladdningstjänster, available at: http://opendata-download-radar.smhi.se/, last access: 9 June 2021. a
Verfaillie, D., Déqué, M., Morin, S., and Lafaysse, M.: The method
ADAMONT v1.0 for statistical adjustment of climate projections applicable
to energy balance land surface models, Geosci. Model Dev., 10, 4257–4283, https://doi.org/10.5194/gmd-10-4257-2017, 2017. a
Westra, S., Mehrotra, R., Sharma, A., and Srikanthan, R.: Continuous Rainfall
Simulation: 1. A Regionalized Subdaily Disaggregation Approach, Water Resour. Res., 48, 1535, https://doi.org/10.1029/2011WR010489, 2012. a
Wu, J.-L., Kashinath, K., Albert, A., Chirila, D., Prabhat, and Xiao, H.:
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems, J. Comput. Phys., 406, 109209, https://doi.org/10.1016/j.jcp.2019.109209, 2020. a, b
Short summary
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible...