Statistical post-processing of ensemble forecasts of compound weather risk (Ref: 4342-6)

该项目将开发新的多元统计技术，用于重新校准捕获空间、时间和交叉变量依赖性的预测集合。这将提高复合天气风险的概率预测。特别强调的是高影响的极端天气事件。该研究将与作为CASE合作伙伴的英国气象局密切合作。我们将使用来自英国气象局集合预报系统MOGREPS的历史数据以及相应的验证。感兴趣的气象变量是温度、地面压力、风速和降水。该项目的主要目标是：(i)开发和探索对极端天气事件的预报集合进行多元统计后处理的新方法；（ii）改进由温度、风速和降水引起的英国复合天气风险的概率预测；（iii）帮助在气象局的操作后处理套件中实施更好的技术，以提高对英国复合天气风险的预测。

The project will develop novel multivariate statistical techniques for recalibrating forecast ensembles that capture spatial, temporal and cross-variable dependence. These will improve probabilistic prediction of compound weather risk. A particular emphasis will lie on high-impact extreme weather events.The research will be conducted in close collaboration with the Met Office as CASE partner. We will use historical data from the Met Office's ensemble prediction system MOGREPS together with the corresponding verifications. Meteorological variables of interest are temperature, surface pressure, wind speed and precipitation.The main objectives of the project are:(i) to develop and explore novel methods for multivariate statistical post-processing of forecast ensembles with a particular view to extreme weather events;(ii) to improve probabilistic prediction of UK compound weather risk due to temperature, wind speed and precipitation;(iii) to help implement better techniques in the Met Office's operational post-processing suite in order to improve prediction of UK compound weather risk.