Current postprocessing techniques often require separate models for each lead time and disregard possible inter-ensemble relationships by either correcting each member separately or by employing distributional approaches. In this work, we tackle these shortcomings with an innovative, fast and accurate Transformer which postprocesses each ensemble member individually while allowing information exchange across variables, spatial dimensions and lead times by means of multi-headed self-attention. Weather foreacasts are postprocessed over 20 lead times simultaneously while including up to twelve meteorological predictors. We use the EUPPBench dataset for training which contains ensemble predictions from the European Center for Medium-range Weather Forecasts' integrated forecasting system alongside corresponding observations. The work presented here is the first to postprocess the ten and one hundred-meter wind speed forecasts within this benchmark dataset, while also correcting the two-meter temperature. Our approach significantly improves the original forecasts, as measured by the CRPS, with 17.5 % for two-meter temperature, nearly 5% for ten-meter wind speed and 5.3 % for one hundred-meter wind speed, outperforming a classical member-by-member approach employed as competitive benchmark. Furthermore, being up to 75 times faster, it fulfills the demand for rapid operational weather forecasts in various downstream applications, including renewable energy forecasting.

翻译：当前的后处理技术通常需要为每个预报时效建立独立模型，并且通过单独修正各集合成员或采用分布估计方法，忽视了可能的集合间关联性。本研究提出一种创新、快速且精准的Transformer模型来解决这些缺陷，该模型通过对每个集合成员进行独立后处理，同时利用多头自注意力机制实现跨变量、空间维度和预报时效的信息交互。该模型可同步处理包含多达十二个气象预测因子的二十个预报时效的天气预测数据。我们使用EUPPBench数据集进行训练，该数据集包含欧洲中期天气预报中心集成预报系统的集合预报及相应观测数据。本研究首次在该基准数据集内实现了对十米和百米风速预报的后处理，同时修正了两米温度预报。通过连续概率评分指标衡量，我们的方法显著提升了原始预报精度：两米温度预报提升17.5%，十米风速预报提升近5%，百米风速预报提升5.3%，其性能优于作为竞争基准的传统逐成员处理方法。此外，该方法最高可提速75倍，能够满足包括可再生能源预测在内的各类下游应用对快速业务化天气预报的需求。