EGU23-1365, updated on 22 Feb 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Improving post-processing of East African precipitation forecasts using a generative machine learning model

Bobby Antonio1, Andrew McRae2, Dave MacLeod3, Fenwick Cooper2, John Marsham4, Laurence Aitchison5, Tim Palmer2, and Peter Watson1
Bobby Antonio et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, UK (
  • 2Department of Physics, University of Oxford, Oxford UK
  • 3School of Earth and Environment Sciences, University of Cardiff, Cardiff, UK
  • 4School of Earth and Environment, Univeristy of Leeds, Leeds, UK
  • 5Machine Learning and Computational Neuroscience Unit, University of Bristol, UK

Existing weather models are known to have poor skill over Africa, where there are regular threats of drought and floods that present significant risks to people's lives and livelihoods. Improved precipitation forecasts could help mitigate the negative effects of these extreme weather events, as well as providing significant financial benefits to the region. Building on work that successfully applied a state-of-the-art machine learning method (a conditional Generative Adversarial Network, cGAN) to postprocess precipitation forecasts in the UK, we present a novel way to improve precipitation forecasts in East Africa. We address the challenge of realistically representing tropical convective rainfall in this region, which is poorly simulated in conventional forecast models. We use a cGAN to postprocess ECMWF high resolution forecasts at 0.1 degree resolution and 6-18h lead times, using the iMERG dataset as ground truth, and investigate how well this model can correct bias, produce reliable probability distributions and create samples of rainfall with realistic spatial structure. We will also present performance in extreme rainfall events. This has the potential to enable cost effective improvements to early warning systems in the affected areas.

How to cite: Antonio, B., McRae, A., MacLeod, D., Cooper, F., Marsham, J., Aitchison, L., Palmer, T., and Watson, P.: Improving post-processing of East African precipitation forecasts using a generative machine learning model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1365,, 2023.

Supplementary materials

Supplementary material file