A microphysical bulk formulation based on scaling normalization of the particle size distribution. Part II: Data assimilation into physical processes
A microphysical bulk formulation based on scaling normalization of the particle size distribution. Part II: Data assimilation into physical processes
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基于粒度分布缩放标准化的微物理散装配方。
DOI：
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发表时间：
2005
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通讯作者：
I. Zawadzki
中科院分区：
文献类型：
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作者：
S. Laroche;W. Szyrmer;I. Zawadzki
微物理方案的尺度归一化的颗粒大小分布（PSD）的基础上投到变分数据同化方法，以评估其检索的降水结构和湿度的时刻，可以来自雷达和地面的disdrometer测量的PSD的能力。分析了云底以下的主要沉降和蒸发过程。各种相同的双胞胎实验中的列时间依赖性模型的背景下，用于模拟沉淀细胞在一段短的时间内通过。假设相对湿度曲线恒定。忽略了微物理过程对热力学场的反馈。观测结果是从一个三阶矩方案产生的，该方案将PSD的零阶、三阶和六阶矩作为预测变量。该模型是离散化的预测矩，这使得调整的模型变量更容易的观察。然而，六阶矩的特征直径的上限是必要的，以防止在数据同化过程中发展数值不稳定性。三阶矩方案的切线线性模型在同化窗口（8 min）内很好地再现了两个非线性积分之间的差异，这验证了其伴随在最小化成本函数中的使用，该成本函数测量观测值与相应模型变量之间的失配。同化含噪观测值时，应在代价函数中加入弱平滑惩罚函数。当所有的预测时刻都被观测和同化时，即使有40%的观测误差，最小化也能很好地收敛。在这种情况下，与六阶矩相关的反射率因子可以以0.2 dB的精度进行检索。当只观察到六阶矩时，无法恢复浓度的总数（与零阶矩有关）。然而，恒定的相对湿度可以以1%的精度获得。当使用简单的一阶矩和二阶矩方案从观测的六阶矩反演降水结构时，模式误差强烈地投射在PSD的非观测矩上。
Microphysical schemes based on the scaling normalization of the particle size distribution (PSD) are cast into a variational data assimilation method to assess their ability to retrieve the precipitation structure and humidity from moments of the PSD that can be derived from radar- and ground-based disdrometer measurements. The sedimentation and evaporation, which are the main processes below the cloud base, are examined. Various identical twin experiments are presented in the context of a column time-dependent model used to simulate the passage of precipitating cells over a short period of time. The relative humidity profile is assumed constant. The feedback of the microphysical processes on the thermodynamic fields is ignored. Observations are generated from a three-moment scheme having the zeroth, third, and sixth moments of the PSD as prognostic variables. The model is discretized in terms of the logarithms of the predictive moments, which render the adjustment of the model variables easier to the observations. An upper bound for the characteristic diameter for the sixth moment is however necessary to prevent numerical instabilities from developing during the data assimilation process. The tangent linear model of the three-moment scheme reproduces well the difference between two nonlinear integrations over the assimilation window (8 min), which validates the use of its adjoint in the minimization of the cost function that measures the misfit between observations and corresponding model variables. A weak smoothness penalty function should be added to the cost function when noisy observations are assimilated. When all the predicted moments are observed and assimilated, the minimization converges very well, even with 40% observation error. In this case, the reflectivity factor, which is related to the sixth moment, can be retrieved with 0.2-dB accuracy. When only the sixth moment is observed, the total number of concentration (related to the zeroth moment) cannot be recovered. However, the constant relative humidity can be obtained with 1% accuracy. When simpler one-moment and two-moment schemes are used to retrieve the precipitation structure from the observed sixth moment, the model error strongly projects on the nonobserved moments of the PSD.