Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 6
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.
https://doi.org/10.5194/hess-25-3207-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 6
Technical note
 | 
11 Jun 2021
Technical note |  | 11 Jun 2021

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Sebastian Scher and Stefanie Peßenteiner

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Latest update: 18 Apr 2024
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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 learn what a typical distribution over the day looks like.
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