Advanced statistical post-processing of ensemble weather forecasts

*动机*目前的天气预报依赖于基于流体力学和热力学基本定律的复杂的大气环流数值模型。预报采用动态集合预报系统；为了考虑初始条件下的不确定性，在模型下传播初始状态的集合。尽管在过去的几十年里，数值天气预报技术有了显著的进步，但由于模式误差和生成集合的问题，仍然存在局限性。模型误差可以通过多模型集成或随机参数化来解决。尽管如此，在英国气象局的实践中可以观察到，预报集合在位置和离散度上仍然存在偏差(参见，例如，Hamill和Colucci，周一。牧师,1997;Gneiting等人，周一。牧师,2005;拉特里等人，周一。牧师,2005)。它们往往分散不足，导致对不确定性估计过于自信，对极端天气事件估计不足。在亚电网尺度的天气现象中，如英国特定地点的温度、降水或风速，系统偏差是显著的，最先进的系统偶尔会错过集合分布中的极端天气事件。因此，不能期望原始集成分布直接转换为感兴趣变量的预测分布。*统计后处理*这导致了将动态信息和统计信息结合起来的想法，通过对动态集合的统计后处理来改进预测。提出的方法包括从20世纪70年代以来已知的简单模型输出统计方案到更先进的方法，如整体着装（Roulston和Smith， Tellus A, 2003； Wang和Bishop， Q. J. Roy）。流星。Soc。， 2005)，贝叶斯模型平均(Raftery et al.，周一。Rev., 2005)和非齐次高斯回归（Gneiting et al.，周一至周五）。牧师,2005)。最近的一项研究（Hemri等人，《地球物理学》）。卷。（2014）基于历史ECMWF预测数据表明，即使由于更好的预测模型和更好的数据同化程序，原始集合本身的技能提高了，但由于后处理，原始集合的预测技能增益仍然保持不变。这表明，在可预见的未来，后处理将增加技能。到目前为止，统计后处理的研究主要集中在平均情况下，很少提及具有高度社会经济影响的罕见或极端天气事件。*项目策略*该项目将根据极端天气事件的特定观点，调整现有和开发新的预报集合统计后处理方法。我们将开发有前途的状态依赖后处理的新方法。后处理将取决于预报模式所处的大尺度环流状态。为此，我们将使用英国气象局现有的天气状况目录。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。我们感兴趣的是短期到中期的天气预报，其中有相当大的变化，但在集合中仍有一些技巧。该研究将与作为CASE合作伙伴的英国气象局密切合作。该项目有可能出版重要的学术出版物，并切实提高对极端天气事件的业务预测能力。*目标*该项目的主要目标是：(i)开发和探索极端事件预测集合统计后处理的新方法；（ii）改进对英国极端温度、地表压力、降水和风速的概率预测；（iii）帮助在气象局的操作后处理套件中实施更好的技术，以提高对英国极端天气事件的预测。

* Motivation *Current weather prediction relies on complex numerical models of atmospheric circulation based on the fundamental laws of hydrodynamics and thermodynamics. Forecasts are made by dynamical ensemble prediction systems; to account for the uncertainty in initial conditions an ensemble of initial states is propagated under the model. Despite impressive improvements in the forecast skill of numerical weather prediction in the past decades there are still limitations due to model error and problems in generating ensembles. Model error may be addressed using multi-model ensembles or by stochastic parametrization. Nevertheless, it is observed in the Met Office's practice that forecast ensembles are still biased both in location and dispersion (see also, e.g., Hamill and Colucci, Mon. Wea. Rev., 1997; Gneiting et al., Mon. Wea. Rev., 2005; Raftery et al., Mon. Wea. Rev., 2005). They tend to be underdispersive, leading to overconfident uncertainty estimates and an underestimation of extreme weather events. Systematic biases are significant in subgrid-scale weather phenomena such as UK temperature, precipitation or wind speed at particular locations and state-of-the-art systems occasionally miss extreme weather events within the ensemble distribution. The raw ensemble distribution can thus not be expected to convert directly into a predictive distribution for a variable of interest.* Statistical post-processing *This leads to the idea of combining dynamical and statistical information to improve prediction by statistical post-processing of the dynamical ensemble. Proposed methods range from simple model output statistics schemes known since the 1970s to more advanced approaches such as ensemble dressing (Roulston and Smith, Tellus A, 2003; Wang and Bishop, Q. J. Roy. Meteor. Soc., 2005), Bayesian model averaging (Raftery et al., Mon. Wea. Rev., 2005) and non-homogeneous Gaussian regression (Gneiting et al., Mon. Wea. Rev., 2005). A recent study (Hemri et al., Geophys. Res. Lett., 2014) based on historical ECMWF forecast data has shown that the forecast skill gain over the raw ensemble due to post-processing remains constant even if the skill of the raw ensemble itself increases due to better forecast models and better data assimilation procedures. This indicates that post-processing will add skill for the foreseeable future. Until now, research on statistical post-processing has focussed on the average case, there has been little mention of rare or extreme weather events which are of high socio-economic impact.* Project strategy *The project will tailor existing and develop new methods for statistical post-processing of forecast ensembles with a particular view on extreme weather events. We will develop the promising novel approach of state-dependent post-processing. The post-processing will be conditional on the large-scale circulation regime the forecast model is in. We will use the Met Office's existing catalogue of weather regimes for this purpose. We will use historical data from the Met Office's ensemble prediction system MOGREPS together with the corresponding verifications. We are interested in short- to medium-range weather forecasting where there is considerable variability but still some skill in the ensemble. The research will be conducted in close collaboration with the Met Office as CASE partner. The project has the potential to produce key academic publications as well as real improvements in operational prediction capacity for extreme weather events.* Objectives *The main objectives of the project are:(i) to develop and explore novel methods for statistical post-processing of forecast ensembles for extreme events;(ii) to improve probabilistic prediction of extreme UK temperature, surface pressure, precipitation and wind speed;(iii) to help implement better techniques in the Met Office's operational post-processing suite in order to improve prediction of extreme UK weather events.