Abstract. With numerical weather prediction ensembles unable to produce sufficiently calibrated forecasts, statistical post-processing is needed to correct deterministic and probabilistic biases. Over the past decades, a number of methods addressing this issue have been proposed, with ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) among the most popular. They are able to produce skillful deterministic and probabilistic forecasts for a wide range of applications. These methods are usually applied to the newest model run as soon as it has finished, before the entire forecast trajectory is issued. RAFT (rapid adjustment of forecast trajectories), a recently proposed novel approach, aims to improve these forecasts even further, utilizing the error correlation patterns between lead times. As soon as the first forecasts are verified, we start updating the remainder of the trajectory based on the newly gathered error information. As RAFT works particularly well in conjunction with other post-processing methods like EMOS and techniques designed to reconstruct the multivariate dependency structure like ensemble copula coupling (ECC), we look to identify the optimal combination of these methods. In our study, we apply multi-stage post-processing to wind speed forecasts from the UK Met Office's convective-scale MOGREPS-UK ensemble and analyze results for short-range forecasts at a number of sites in the UK and the Republic of Ireland.

中文翻译：

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集合风预报轨迹多阶段后处理的运算顺序

摘要。由于数值天气预报集合无法产生足够校准的预报，因此需要进行统计后处理以纠正确定性和概率性偏差。在过去的几十年里，已经提出了许多解决这个问题的方法，其中最流行的是集成模型输出统计 (EMOS) 和贝叶斯模型平均 (BMA)。他们能够为广泛的应用程序生成熟练的确定性和概率预测。在发布整个预测轨迹之前，这些方法通常会在最新的模型运行完成后立即应用。RAFT（预测轨迹的快速调整）是最近提出的一种新方法，旨在利用提前期之间的误差相关模式进一步改进这些预测。一旦第一个预测得到验证，我们就开始根据新收集的错误信息更新轨迹的其余部分。由于 RAFT 与其他后处理方法（如 EMOS）和旨在重建多元依赖结构（如集成联结耦合 (ECC)）的技术结合使用效果特别好，我们希望确定这些方法的最佳组合。在我们的研究中，我们将多阶段后处理应用于来自英国气象局的对流尺度 MOGREPS-UK 集合的风速预测，并分析了英国和爱尔兰共和国多个站点的短期预报结果。由于 RAFT 与其他后处理方法（如 EMOS）和旨在重建多元依赖结构（如集成联结耦合 (ECC)）的技术结合使用效果特别好，因此我们希望确定这些方法的最佳组合。在我们的研究中，我们将多阶段后处理应用于来自英国气象局的对流尺度 MOGREPS-UK 集合的风速预测，并分析了英国和爱尔兰共和国多个站点的短期预报结果。由于 RAFT 与其他后处理方法（如 EMOS）和旨在重建多元依赖结构（如集成联结耦合 (ECC)）的技术结合使用效果特别好，因此我们希望确定这些方法的最佳组合。在我们的研究中，我们将多阶段后处理应用于来自英国气象局的对流尺度 MOGREPS-UK 集合的风速预测，并分析了英国和爱尔兰共和国多个站点的短期预报结果。