Increasing the accuracy and resolution of precipitation forecasts using
deep generative models
- URL: http://arxiv.org/abs/2203.12297v1
- Date: Wed, 23 Mar 2022 09:45:12 GMT
- Title: Increasing the accuracy and resolution of precipitation forecasts using
deep generative models
- Authors: Ilan Price, Stephan Rasp
- Abstract summary: We train a conditional Generative Adversarial Network -- coined CorrectorGAN -- to produce ensembles of high-resolution, bias-corrected forecasts.
CorrectorGAN, once trained, produces predictions in seconds on a single machine.
Results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions.
- Score: 3.8073142980733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurately forecasting extreme rainfall is notoriously difficult, but is also
ever more crucial for society as climate change increases the frequency of such
extremes. Global numerical weather prediction models often fail to capture
extremes, and are produced at too low a resolution to be actionable, while
regional, high-resolution models are hugely expensive both in computation and
labour. In this paper we explore the use of deep generative models to
simultaneously correct and downscale (super-resolve) global ensemble forecasts
over the Continental US. Specifically, using fine-grained radar observations as
our ground truth, we train a conditional Generative Adversarial Network --
coined CorrectorGAN -- via a custom training procedure and augmented loss
function, to produce ensembles of high-resolution, bias-corrected forecasts
based on coarse, global precipitation forecasts in addition to other relevant
meteorological fields. Our model outperforms an interpolation baseline, as well
as super-resolution-only and CNN-based univariate methods, and approaches the
performance of an operational regional high-resolution model across an array of
established probabilistic metrics. Crucially, CorrectorGAN, once trained,
produces predictions in seconds on a single machine. These results raise
exciting questions about the necessity of regional models, and whether
data-driven downscaling and correction methods can be transferred to data-poor
regions that so far have had no access to high-resolution forecasts.
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