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.

GPTCast 是一种基于大型语言模型（LLMs）的生成式深度学习方法，用于雷达降水的集成预测，该方法使用 GPT 模型作为预测器，使用标记化的雷达图像学习时空降水动态，模型通过学习数据中的变化性并在推理时以模型暴露的形式生成集成，无需使用随机性，提供了具有准确不确定性估计的逼真集成预测，通过在意大利北部的 Emilia-Romagna 地区的 6 年雷达数据集上进行训练和测试，展示了与最先进的集成外推方法相比的卓越结果。