text: 236,58,1506998476005126152,484490200,Ilan Price,"Can ML help us produce cheap, reliable, high-resolution stochastic rain forecasts? In our new #AISTATS2022 paper we develop a promising approach using deep generative models. (with @raspstephan, done while at @climateai) <LINK> 🧵 1/7 <LINK> Weather forecasts are typically produced by numerical weather models (NWMs). These are very expensive to run, and so global NWMs are run at relatively low spatial resolution. Only richer countries can run their own regional, high-res NWMs. 2/7 We propose and train a GAN model - CorrectorGAN - to map from low resolution NWM ensembles to distributions of high-resolution forecasts. 3/7 <LINK> This goal is to combine (1) bias-correction, (2) super-resolution, and (3) uncertainty generation and calibration, producing reliable high resolution stochastic forecasts without running high-resolution NWMs. 4/7 We compare against an operational regional NWM (HREF), as well as DL based methods. CorrectorGAN approaches HREF’s performance on CRPS, Brier scores, and reliability, but its forecasts are generated at a tiny fraction of the cost (just generator forward passes). 5/7 <LINK> The architecture and training were informed by the specifics of the task - for details on these, and on the experimental setup and evaluation, check out the paper. 6/7 Big thanks @raspstephan and @climateai for the great collaboration. 7/7",https://arxiv.org/abs/2203.12297,"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. ","Increasing the accuracy and resolution of precipitation forecasts using