Earth system forecasting has traditionally relied on complex physical models
that are computationally expensive and require significant domain expertise. In
the past decade, the unprecedented increase in spatiotemporal Earth observation
data has enabled data-driven forecasting models using deep learning techniques.
These models have shown promise for diverse Earth system forecasting tasks but
either struggle with handling uncertainty or neglect domain-specific prior
knowledge, resulting in averaging possible futures to blurred forecasts or
generating physically implausible predictions. To address these limitations, we
propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1)
We develop PreDiff, a conditional latent diffusion model capable of
probabilistic forecasts. 2) We incorporate an explicit knowledge control
mechanism to align forecasts with domain-specific physical constraints. This is
achieved by estimating the deviation from imposed constraints at each denoising
step and adjusting the transition distribution accordingly. We conduct
empirical studies on two datasets: N-body MNIST, a synthetic dataset with
chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Specifically, we impose the law of conservation of energy in N-body MNIST and
anticipated precipitation intensity in SEVIR. Experiments demonstrate the
effectiveness of PreDiff in handling uncertainty, incorporating domain-specific
prior knowledge, and generating forecasts that exhibit high operational
utility.

我们提出了一个两阶段的概率时空预测流程：1）我们开发了 PreDiff，一个能够进行概率预测的条件潜在扩散模型。2）我们通过在去噪步骤中估计所施加约束的偏差并相应地调整过渡分布，结合了显式的知识控制机制，以使预测与领域特定的物理约束相一致。实验在两个数据集上进行，分别是具有混沌行为的合成数据集 N-body MNIST 和代表降雨预测的真实世界数据集 SEVIR。实验结果显示 PreDiff 在处理不确定性、整合领域特定先验知识和生成具有高操作效能的预测方面的有效性。