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A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts by Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N. is a scholarly article available to read on EtoBox.

What is A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts about?

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolut

Author

Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N.

Published

2022

Language

EN