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Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model

机译：在对流级数值天气预报（NWP）模型中，朝着极性雷达观测的变分同化

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摘要

This paper presents the potential of nonlinear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarized backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France numerical weather prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A nonlinear observation operator, based on the T-matrix method, allows us to simulate the horizontal and the vertical reflectivities (Z(HH) and Z(VV)), the differential reflectivity Z(DR), the specific differential phase K-DP and the co-polar correlation coefficient rho(HV). To assess the uncertainty of such simulations, perturbations have been applied to input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to a Gaussian distribution have been found for both reflectivities and for Z(DR), contrary to K-DP and rho(HV). A linearized version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows us to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables Z(HH) and Z(DR) appear to be good candidates for hydrometeor initialization, while K-DP seems to be useful only for rain contents. Due to the weak sensitivity of rho(HV), its use in data assimilation is expected to be very challenging.
机译：本文介绍了观察操作员的非线性和线性版本的潜力，用于模拟天气雷达观察到的极性变量。这些变量从水平和垂直极化的反向散射辐射推导出来，提供有关液压仪的形状，相位和分布的信息。观察空间的不同研究作为朝向其包含在变分数据同化上下文中的第一步，这在此处不受约束。输入变量是由对流尺度的Arome-France数值天气预报（NWP）模型预测的预后变量，包括液体和固体水流仪含量。基于T矩阵方法的非线性观测操作员允许我们模拟水平和垂直反射率（Z（HH）和Z（VV）），差分反射率Z（DR），特定差分相位K-DP和有源相关系数Rho（HV）。为了评估这种模拟的不确定性，扰动已经应用于操作者的输入参数，例如散射体的介电常数，形状和取向。然后执行由模拟和观察值之间的差异定义的创新统计数据。在一些特定的过滤过程之后，已经发现了靠近高斯分布的形状，用于反射率和Z（DR），与K-DP和RHO（HV）相反。通过具有有限差分法估计的Jacobian矩阵获得了这种观察操作员的线性化版本。这一步骤允许我们研究偏振变量对水流仪内容扰动的敏感性，在模型几何和雷达中。 Polarimetric变量Z（HH）和Z（DR）似乎是水流仪初始化的好候选者，而K-DP似乎仅用于雨林内容。由于Rho（HV）的敏感性较弱，预计其在数据同化中的使用非常具有挑战性。

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