用时空联合建模提升山区天气预报精度，兼顾时空一致性与预测准确性。
STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
- 基于多变量合理评分规则，联合建模时空天气变化
- 相比基线方法，温度、风速、湿度和降水预测更准确
- 仅需6小时预报输入，即可生成每小时集合预测，适合实际业务
我们提出时空在位后处理（STIPP），一种机器学习模型，可为站点网络生成时空一致的天气预报。传统的数值天气预报或数据驱动模型的格点预报常因未解析局地效应而缺乏精度。典型统计后处理方法虽能纠正偏差，但常破坏时空相关结构。近期基于生成建模的方法虽改善了空间相关性，但需独立预测每个预报时长。相比之下，STIPP实现联合时空预报，在表面温度、风速、相对湿度和降水上均优于基线方法。它仅需六小时确定性预报输入，即可生成小时级集合预测，融合了后处理与时间插值的边界。通过使用多变量合理评分规则进行训练，STIPP推动了仅以分布边缘监督的数据驱动大气模型研究。
原文摘要 · Abstract (English)
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lack the necessary precision due to unresolved local effects. Typical statistical postprocessing methods correct these biases, but often degrade spatio-temporal correlation structures in doing so. Recent works based on generative modeling successfully improve spatial correlation structures but have to forecast every lead time independently. In contrast, STIPP makes joint spatio-temporal forecasts which have increased accuracy for surface temperature, wind, relative humidity and precipitation when compared to baseline methods. It makes hourly ensemble predictions given only a six-hourly deterministic forecast, blending the boundaries of postprocessing and temporal interpolation. By leveraging a multivariate proper scoring rule for training, STIPP contributes to ongoing work data-driven atmospheric models supervised only with distribution marginals.
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