Representing model error and observation error uncertainty for data assimilation of polarimetric radar measurements

表示极化雷达测量数据同化的模型误差和观测误差不确定性

• 批准号: 408063057
• 负责人: Professorin Dr. Tijana Janjic Pfander
• 金额: --
• 依托单位: Deutscher Wetterdienst (DWD)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/408063057?language=en
• 关键词: Representing; model; error; observation; uncertainty

Data assimilation compares each observation of a variable with a prior estimate of the variable taken from a discrete dynamical model, in order to deduce a revised estimate on the model grid. This process requires knowledge of, or assumptions on, the error characteristics or uncertainty properties of the observed values and prior estimates. One of the difficulties encountered in this process is that the discrete geophysical model is not able to represent all the physical processes, nor all of the spatial and temporal scales, of the observed geophysical state and that additional approximations are needed to represent the equivalent of any observation. These uncertainties need to be accounted for in data assimilation algorithms through the model error and observation error statistics. Only this way, we can optimally initialize convection-permitting models with polarimetric radar measurements. The goal of this proposal is to find an optimal approach for assimilating polarimetric radar measurements with an ensemble Kalman filter. To this end, strategies of perturbing hydrometeors during data assimilation to improve their forecast accuracy will be investigated; we will explore benefits of correlated observation error statistics for polarimetric radar measurements. Finally, representation error part of the observation error will be parameterized using high-resolution simulations and included in data assimilation algorithm. The aim of the proposal is to address the objective: "generation of precipitation system analyses by assimilation of polarimetric radar observations into atmospheric models for weather forecasting".

数据同化将变量的每次观测值与从离散动力学模型中获取的变量的先前估计进行比较，以便在模型网格上推导出修正的估计。这个过程需要对观测值和先前估计的误差特性或不确定性特性的知识或假设。在此过程中遇到的困难之一是，离散地球物理模型不能表示观测到的地球物理状态的所有物理过程，也不能表示所有的空间和时间尺度，并且需要额外的近似来表示任何观测的等效。在数据同化算法中，需要通过模型误差和观测误差统计来考虑这些不确定性。只有这样，我们才能用极化雷达测量最佳地初始化允许对流的模型。本提案的目标是找到一个最优的方法来同化极化雷达测量与一个集合卡尔曼滤波器。为此，研究了扰动水成物在同化过程中提高预报精度的策略；我们将探讨相关观测误差统计对极化雷达测量的好处。最后，利用高分辨率模拟将观测误差的表征误差部分参数化，并纳入数据同化算法。该提案的目的是解决以下目标：“通过同化极化雷达观测到用于天气预报的大气模式来生成降水系统分析”。