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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-464
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-464
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Sep 2020

30 Sep 2020

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This preprint is currently under review for the journal HESS.

Technical Note: Temporal Disaggregation of Spatial Rainfall Fields with Generative Adversarial Networks

Sebastian Scher1 and Stefanie Peßenteiner2 Sebastian Scher and Stefanie Peßenteiner
  • 1Stockholm University, Department of Meteorology and Bolin Centre for Climate Research, Stockholm, Sweden
  • 2University of Graz, Department of Geography and Regional Science, Graz, Austria

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly under-determined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of Generative Adversarial Networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly lead to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Sebastian Scher and Stefanie Peßenteiner

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Sebastian Scher and Stefanie Peßenteiner

Sebastian Scher and Stefanie Peßenteiner

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Latest update: 26 Oct 2020
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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 GAN can learn what a typical distribution over the day looks like.
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible...
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