Met Office, FitzRoy Road, Exeter EX1 3PB, UK
AEMET, Arquitecte Sert 1, Barcelona, 08005 Catalonia, Spain
Philos Trans A Math Phys Eng Sci. 2014 Jun 28;372(2018):20130284. doi: 10.1098/rsta.2013.0284.
The need to represent uncertainty resulting from model error in ensemble weather prediction systems has spawned a variety of ad hoc stochastic algorithms based on plausible assumptions about sub-grid-scale variability. Currently, few studies have been carried out to prove the veracity of such schemes and it seems likely that some implementations of stochastic parametrization are misrepresentations of the true source of model uncertainty. This paper describes an attempt to quantify the uncertainty in physical parametrization tendencies in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System with respect to horizontal resolution deficiency. High-resolution truth forecasts are compared with matching target forecasts at much lower resolution after coarse-graining to a common spatial and temporal resolution. In this way, model error is defined and its probability distribution function is examined as a function of tendency magnitude. It is found that the temperature tendency error associated with convection parametrization and explicit water phase changes behaves like a Poisson process for which the variance grows in proportion to the mean, which suggests that the assumptions underpinning the Craig and Cohen statistical model of convection might also apply to parametrized convection. By contrast, radiation temperature tendency errors have a very different relationship to their mean value. These findings suggest that the ECMWF stochastic perturbed parametrization tendency scheme could be improved since it assumes that the standard deviation of the tendency error is proportional to the mean. Using our finding that the variance error is proportional to the mean, a prototype stochastic parametrization scheme is devised for convective and large-scale condensation temperature tendencies and tested within the ECMWF Ensemble Prediction System. Significant impact on forecast skill is shown, implying its potential for further development.
由于模型误差在集合天气预报系统中需要表示不确定性,因此产生了各种基于子网格尺度可变性的合理假设的特殊随机算法。目前,很少有研究证明这些方案的真实性,而且似乎一些随机参数化的实现可能是对模型不确定性真实来源的误解。本文描述了一种尝试量化欧洲中期天气预报中心(ECMWF)综合预报系统中物理参数化倾向不确定性的方法,该方法针对水平分辨率不足。通过粗化到公共时空分辨率,将高分辨率真实预报与匹配的低分辨率目标预报进行比较。以这种方式定义模型误差,并检查其概率分布函数作为倾向大小的函数。结果发现,与对流参数化和显式水相变化相关的温度倾向误差表现为泊松过程,其方差与均值成比例增长,这表明Craig 和 Cohen 对流统计模型的假设也可能适用于参数化对流。相比之下,辐射温度倾向误差与其平均值有非常不同的关系。这些发现表明,由于假定倾向误差的标准差与平均值成比例,因此 ECMWF 随机扰动参数化倾向方案可以得到改进。利用我们发现的方差误差与平均值成比例的结果,为对流和大尺度凝结温度倾向设计了一个原型随机参数化方案,并在 ECMWF 集合预报系统中进行了测试。结果表明,该方案对预报技巧有显著影响,这意味着它具有进一步发展的潜力。