Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges
Improving the spatial accuracy of extreme tropical cyclone rainfall in ERA5 using deep learning
Guido Ascenso · Andrea Ficchì · Matteo Giuliani · Leone Cavicchia · Enrico Scoccimarro · Andrea Castelletti
Keywords: [ Climate science and climate modeling ] [ Classification, regression, and supervised learning ] [ Computer vision and remote sensing ] [ Extreme weather ]
We propose a novel method for the bias adjustment and post-processing of gridded rainfall data products. Our method uses U-Net (a deep convolutional neural network) as a backbone, and a novel loss function given by the combination of a pixelwise bias component (Mean Absolute Error) and a spatial accuracy component (Fractions Skill Score). We evaluate the proposed approach by adjusting extreme rainfall from the popular ERA5 reanalysis dataset, using the multi-source observational dataset MSWEP as a target. We focus on a sample of extreme rainfall events induced by tropical cyclones and show that the proposed method significantly reduces both the MAE (by 16\%) and FSS (by 53\%) of ERA5.