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

数据同化将变量的每个观测值与来自离散动力模式的变量的先验估计值进行比较，以便在模式网格上推导出修正的估计值。这一过程需要了解或假设观测值和先验估计的误差特征或不确定性属性。在这一过程中遇到的困难之一是，离散地球物理模型不能代表观测到的地球物理状态的所有物理过程，也不能代表所有的空间和时间尺度，需要更多的近似才能代表任何观测的等值。这些不确定性需要在数据同化算法中通过模式误差和观测误差统计加以考虑。只有这样，我们才能用偏振雷达测量来最佳地初始化允许对流的模型。这一提议的目标是找到一种利用集合卡尔曼滤波同化极化雷达测量值的最佳方法。为此，将研究在数据同化过程中扰动水流星以提高其预报精度的策略；我们将探索相关观测误差统计对极化雷达测量的好处。最后，观测误差中的表示误差部分将使用高分辨率模拟进行参数化，并包括在数据同化算法中。该提案的目的是解决以下目标：“通过将偏振雷达观测同化到大气预报模式来生成降水系统分析”。

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".