Stochastic Parameterisation of Precipitation in Ensemble Simulations with an Intermediate Complexity General Circulation Model

近年来，天气预报已开始产生不同初始条件的单个预报集合，并在预报期间随机强制。不同的初始条件对应于观测和初始化中的不确定性。对应于模型中的不确定性和未解决的小尺度过程的可变性的随机力不包括在确定性参数化中。随机集合预报方法已被证明可以改善预报，特别是降水，并增加集合内的传播，这是一种可预测性的衡量标准。然而，许多与天气预报模型中使用的噪声相关的问题是开放的，并且不能在昂贵的天气预报模型中回答。因此，本项目的目的是研究噪声特性，并通过一个数值有效的大气环流模型（GCM）的中间复杂性检测最佳设置。我们尤其认为：（1）不同的噪声幅度和空间与时间的相关性，（2）噪声对不同动力学变量的影响，主要是对湿度的影响，（3）充分探索天气状态所必需的集合的大小，以及（4）极端事件的统计特性。随后，对该项目初期部分的研究结果进行了测试，并将其应用于综合气候和天气预报模型。

In the recent years, weather prediction has begun to produce ensembles of individual forecasts which start with different initial conditions and are forced randomly during the forecast period. The different initial conditions correspond to uncertainties in the observations and the initialisation. The random forces, which correspond to uncertainties in the model and to the variability of the unresolved small scale processes, are not included in the deterministic parameterisations. The stochastic ensemble prediction approach has shown up to improve the forecast, in particular precipitation, and to increase the spread within the ensembles which is used as a measure of predictability. However, many questions related to the noise used in weather forecast models are open and cannot be answered within costly weather forecast models. Therefore, the aim of this project is to study the noise properties and to detect optimal settings by means of a numerically efficient general circulation model (GCM) of intermediate complexity. In particular we consider: (1) different noise amplitudes and spatial and temporal correlations, (2) the impact of noise on different dynamical variables, mainly on humidity, (3) the sizes of the ensembles which are necessary for a sufficient exploration of weather states, and (4) the statistical properties of extreme events. Subsequently, the findings of the initial part of this project are tested and applied in comprehensive climate and weather forecast models.