用神经过程融合雷达与雨量站数据，生成高精度降雨图并量化不确定性。
From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
- 基于神经过程融合雷达与不规则分布雨量站数据
- 在少站点和跨区域场景下仍保持高精度与校准不确定性
- 适合气象预报、洪水预警等需要高分辨率降雨估计的场景
高分辨率降雨观测对天气预报、水资源管理和灾害防控至关重要。传统观测常存在偏差且空间分辨率低，难以捕捉局部降雨特征。精确的高分辨率降雨地图需整合稀疏地面观测数据，但现有深度学习方法受限于降雨分布偏斜、局部性强、噪声大及时空融合能力不足。本文提出 DropsToGrid，一种基于神经过程的方法，通过融合来自噪声干扰、分布不均的私有气象站的时间序列数据与雷达提供的空间上下文信息，生成密集降雨场。该模型采用多尺度特征提取、时间注意力机制和多模态融合，输出随机且连续的降雨估计，并显式量化不确定性。在真实数据集上的评估表明，即使仅有少量站点且在跨区域场景中，DropsToGrid 仍优于现有业务系统与深度学习基线，生成准确且不确定性校准的高分辨率降雨图。
原文摘要 · Abstract (English)
High-resolution rainfall observations are crucial for weather forecasting, water management, and hazard mitigation. Traditional operational measurements are often biased and low-resolution, limiting their ability to capture local rainfall. Accurate high-resolution rainfall maps require integrating sparse surface observations, yet existing deep learning densification methods are hindered by rainfall's skewed, localized nature, noise, and limited spatio-temporal fusion. We present DropsToGrid, a Neural Process-based method that generates dense rainfall fields by fusing temporal sequences from noisy, irregularly distributed private weather stations with spatial context from radar. Leveraging multi-scale feature extraction, temporal attention, and multi-modal fusion, the model produces stochastic, continuous rainfall estimates and explicitly quantifies uncertainty. Evaluations on real-world datasets demonstrate that DropsToGrid outperforms both operational and deep learning baselines, generating accurate high-resolution rainfall maps with well-calibrated uncertainty, even when only few stations are available and in cross-regional scenarios.
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