BlockGPT通过帧级自回归预测降雨，速度快31倍且更准
BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression
- 用分块批量编码实现帧级自回归，空间时间分离建模
- 在荷兰和美国数据集上精度超主流模型，事件定位更准
- 适合需要实时降雨预测的气象系统或应急响应场景
降水地图预测是复杂的时空建模任务，对减轻极端天气影响至关重要。短期降水预报（即临近预报）要求模型既准确又计算高效，以支持实时应用。现有方法如基于标记的自回归模型常存在归纳偏置缺陷和推理缓慢问题，而扩散模型则计算开销大。为此，我们提出BlockGPT，一种采用批处理标记化（块）方法的生成式自回归变压器，可在每个时间步预测完整的二维场（帧）。该模型被构想为视频预测的通用范式，通过每帧内自注意力与帧间因果注意力实现时空解耦；本研究将其应用于降水临近预报。我们在两个降水数据集（KNMI，荷兰；SEVIR，美国）上评估BlockGPT，对比了当前最优基线模型，包括基于标记的（NowcastingGPT）和扩散基的（DiffCast+Phydnet）模型。结果表明，BlockGPT在准确性、事件定位能力（分类指标）方面均表现更优，且推理速度最高达同类模型的31倍。
原文摘要 · Abstract (English)
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.
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