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首页> 外文期刊>Journal of Geophysical Research, D. Atmospheres: JGR >A methodology for merging multisensor precipitation estimates based on expectation-maximization and scale-recursive estimation
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A methodology for merging multisensor precipitation estimates based on expectation-maximization and scale-recursive estimation

机译：基于期望最大化和尺度递归估计的多传感器降水估计合并方法

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Scale-recursive estimation (SRE) is a Kalman-filter-based methodology, which can be used to produce optimal (in terms of bias and minimum variance) estimates of a field at any desired scale given uncertain and sparse observations at different scales. SRE requires the specification of the state equation, which describes the variability of the precipitation process across scales, and the observation equation, which relates the observations to the state. Typical models for describing the multiscale rainfall variability are the multiplicative cascade models. However, in order to convert them into the additive form needed by SRE, one needs to work in the log space, thus creating a problem in handling zero-intermittency in a satisfactory way. In this paper, we propose an alternative approach, based on a data-driven identification methodology, which operates directly on the data and does not require a prespecified multiscale model structure. Rather, system identification and estimation are performed simultaneously via a likelihood-based expectation-maximization (EM) procedure. The merits of the proposed approach versus approaches based on multiplicative cascade models are explored via several examples of synthetic and real precipitation fields. For practical application the proposed approach will need to be extended to include the temporal evolution of storms. This extension presents theoretical challenges, and until these are addressed, a simple alternative is explored of coupling the EM-SRE approach with a spatial downscaling methodology to merge precipitation observations available at different spatial and temporal scales. An example application is presented motivated by its relevance to the Global Precipitation Measuring (GPM) mission.
机译：尺度递归估计（SRE）是一种基于卡尔曼滤波的方法，可用于在给定的不确定性和稀疏稀疏观测值的情况下，以任意期望的尺度生成最佳的场估计（根据偏差和最小方差）。 SRE需要规范状态方程式和观测方程式，状态方程式描述跨尺度的降水过程的变化，观测方程式将观测值与状态相关联。描述多尺度降雨变化性的典型模型是乘法级联模型。但是，为了将它们转换为SRE所需的加法形式，需要在对数空间中工作，从而在以令人满意的方式处理零间歇性方面产生了问题。在本文中，我们提出了一种基于数据驱动的识别方法的替代方法，该方法直接对数据进行操作，不需要预先指定的多尺度模型结构。相反，系统识别和估计是通过基于似然的期望最大化（EM）程序同时执行的。通过合成和实际降水场的几个示例，探索了所提出的方法与基于乘法级联模型的方法的优点。对于实际应用，建议的方法将需要扩展以包括风暴的时间演变。这种扩展提出了理论挑战，在解决这些挑战之前，探索了一种简单的替代方法，即将EM-SRE方法与空间缩减方法相结合，以合并在不同时空尺度可获得的降水观测。出于与全球降水测量（GPM）任务的相关性而提出了一个示例应用程序。

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