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.

翻译：我们提出了一种时空原位后处理（STIPP）机器学习模型，该模型可为站点观测网络生成时空一致的天气预报。传统数值天气预报或数据驱动模型提供的格点预报常因未解析的局地效应而缺乏必要精度。典型的统计后处理方法虽能修正此类偏差，却常在处理过程中破坏时空相关结构。近期基于生成式建模的研究成功改善了空间相关结构，但必须独立预测每个预报时效。相比之下，STIPP能够进行联合时空预报，在近地表温度、风、相对湿度和降水等要素的预报精度上均优于基线方法。该模型仅需输入六小时一次的确定性预报，即可生成逐小时集合预报，从而模糊了后处理与时间插值之间的界限。通过采用多元严格评分规则进行训练，STIPP为仅通过分布边缘进行监督的数据驱动大气模型研究提供了新的思路。