text:   deep generative models",7,"['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 https://t.co/4KsLNimtbu', '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 https://t.co/uG7TnYRRF4', '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']",22,03,1360