用神经随机过程融合卫星与雨量站数据，提升降水预报精度
Neural Stochastic Processes for Satellite Precipitation Refinement
- 通过神经过程+隐变量随机微分方程建模时空降水结构
- 在43,756个样本上优于13种基线方法，超越日本气象厅产品
- 适用于需要高精度降水估计的防灾与水资源管理场景
精确的降水估计对洪水预报、水资源管理和灾害应对至关重要。卫星产品提供全球小时覆盖，但存在系统性偏差；地面雨量站数据准确但空间稀疏，难以直接用于网格化校正。现有方法将雨量站观测插值到卫星网格，但各时间步独立处理，忽略降水场的时序结构。本文提出神经随机过程（NSP），结合神经过程编码器（以任意雨量站观测为条件）与二维空间表示上的隐变量神经随机微分方程。NSP在单一变分目标下训练，无需模拟即可优化。我们还构建了QPEBench基准，包含2021–2025年美国本土43,756个小时样本，涵盖四种对齐数据源和六个评估指标。在该基准上，NSP在所有六项指标上均优于13种基线，超过日本气象厅（JAXA）的业务校准产品。在九州地区（日本）的额外实验验证了其在不同区域与独立数据源下的泛化能力。
原文摘要 · Abstract (English)
Accurate precipitation estimation is critical for flood forecasting, water resource management, and disaster preparedness. Satellite products provide global hourly coverage but contain systematic biases; ground-based gauges are accurate at point locations but too sparse for direct gridded correction. Existing methods fuse these sources by interpolating gauge observations onto the satellite grid, but treat each time step independently and therefore discard temporal structure in precipitation fields. We propose Neural Stochastic Process (NSP), a model that pairs a Neural Process encoder conditioning on arbitrary sets of gauge observations with a latent Neural SDE on a 2D spatial representation. NSP is trained under a single variational objective with simulation-free cost. We also introduce QPEBench, a benchmark of 43{,}756 hourly samples over the Contiguous United States (2021--2025) with four aligned data sources and six evaluation metrics. On QPEBench, NSP outperforms 13 baselines across all six metrics and surpasses JAXA's operational gauge-calibrated product. An additional experiment on Kyushu, Japan confirms generalization to a different region with independent data sources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。