Correcting Biased Observation Model Error in Data Assimilation
Correcting Biased Observation Model Error in Data Assimilation
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DOI：
10.1175/mwr-d-16-0428.1
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发表时间：
2017-07-01
影响因子：
3.2
通讯作者：
Harlim, John
中科院分区：
文献类型：
--
作者：
Berry, Tyrus;Harlim, John
虽然大多数数据同化方案的公式假定观测模型误差是无偏的，但在实际应用中，带有非平凡偏差的模型误差是不可避免的。一个实际的例子是在有云的情况下辐射传输模型(用于同化卫星测量)的误差。再加上动力学模型的误差，结果是许多(实际上99%)云层观测数据没有被使用，尽管它们可能包含有用的信息。本文提出了一种新的非参数贝叶斯方法，该方法能够学习观测模型的误差分布，并纠正观测数据中的偏差。该方案可与任何资料同化预报系统配套使用。所提出的模型误差估计器使用基于机器学习领域发展的条件分布核嵌入理论的数据驱动基函数构造的非参数似然函数。在数值上，给出了两个算例，结果是肯定的。第一个例子是通过在空间和时间上随机发生的观测引入障碍物，在观测模型误差中产生双峰现象(典型的“多云”观测)；第二个例子是同化热带对流的随机多云模式和一个简单的辐射传输模式产生的类似云卫星亮度温度的量，这在物理上更接近实际。
While the formulation of most data assimilation schemes assumes an unbiased observation model error, in real applications model error with nontrivial biases is unavoidable. A practical example is errors in the radiative transfer model (which is used to assimilate satellite measurements) in the presence of clouds. Together with the dynamical model error, the result is that many ( in fact 99%) of the cloudy observed measurements are not being used although they may contain useful information. This paper presents a novel nonparametric Bayesian scheme that is able to learn the observation model error distribution and correct the bias in incoming observations. This scheme can be used in tandem with any data assimilation forecasting system. The proposed model error estimator uses nonparametric likelihood functions constructed with data-driven basis functions based on the theory of kernel embeddings of conditional distributions developed in the machine learning community. Numerically, positive results are shown with two examples. The first example is designed to produce a bimodality in the observation model error (typical of "cloudy'' observations) by introducing obstructions to the observations that occur randomly in space and time. The second example, which is physically more realistic, is to assimilate cloudy satellite brightness temperature-like quantities, generated from a stochastic multicloud model for tropical convection and a simple radiative transfer model.