Abstract
Predicting precipitation maps is a highly complex spatiotemporal modeling task, critical for mitigating the impacts of extreme weather events. Short-term precipitation forecasting, or nowcasting, requires models that are not only accurate but also computationally efficient for real-time applications. Current methods, such as token-based autoregressive models, often suffer from flawed inductive biases and slow inference, while diffusion models can be computationally intensive. To address these limitations, we introduce BlockGPT, a generative autoregressive transformer using batched tokenization (Block) method that predicts full two-dimensional fields (frames) at each time step. Conceived as a model-agnostic paradigm for video prediction, BlockGPT factorizes space-time by using self-attention within each frame and causal attention across frames; in this work, we instantiate it for precipitation nowcasting. We evaluate BlockGPT on two precipitation datasets, viz. KNMI (Netherlands) and SEVIR (U.S.), comparing it to state-of-the-art baselines including token-based (NowcastingGPT) and diffusion-based (DiffCast+Phydnet) models. The results show that BlockGPT achieves superior accuracy, event localization as measured by categorical metrics, and inference speeds up to 31x faster than comparable baselines.
Abstract (translated)
预测降水地图是一项高度复杂的时空建模任务，对于减轻极端天气事件的影响至关重要。短期降水预报（即临近预报）需要既准确又计算效率高的模型以支持实时应用。目前的方法，如基于令牌的自回归模型，常常存在推理偏差的问题和较慢的推断速度，而扩散模型则可能过于计算密集。为了克服这些限制，我们引入了BlockGPT，这是一种使用批量标记化方法（Block）的生成式自回归变压器，在每个时间步预测完整的二维字段（帧）。作为一种用于视频预测的模型无关范例，BlockGPT通过在每帧内部使用自我注意机制以及跨帧因果注意力来因子分解时空；在这项工作中，我们将其应用于降水临近预报。我们在两个降水数据集上评估了BlockGPT：KNMI（荷兰）和SEVIR（美国），并与最先进的基线模型进行了比较，包括基于令牌的NowcastingGPT和基于扩散的DiffCast+Phydnet模型。结果表明，BlockGPT在准确性、通过类别指标衡量的事态定位以及推理速度方面优于同类基准模型，速度快达31倍。
URL
https://arxiv.org/abs/2510.06293
