  This work introduces GPTCast, a generative deep-learning method for ensemble
nowcast of radar-based precipitation, inspired by advancements in large
language models (LLMs). We employ a GPT model as a forecaster to learn
spatiotemporal precipitation dynamics using tokenized radar images. The
tokenizer is based on a Quantized Variational Autoencoder featuring a novel
reconstruction loss tailored for the skewed distribution of precipitation that
promotes faithful reconstruction of high rainfall rates. The approach produces
realistic ensemble forecasts and provides probabilistic outputs with accurate
uncertainty estimation. The model is trained without resorting to randomness,
all variability is learned solely from the data and exposed by model at
inference for ensemble generation. We train and test GPTCast using a 6-year
radar dataset over the Emilia-Romagna region in Northern Italy, showing
superior results compared to state-of-the-art ensemble extrapolation methods.
