The usefulness of satellite multi-sensor precipitation (SMP) and other satellite-informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is to combine SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground-reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climatology to the depiction of individual storm systems. Characterizing and simulating autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which combines uncalibrated, anisotropic and nonstationary SMP spatiotemporal correlation modeling with a pixel-scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1-hr, 0.1°) and evaluate these ensembles at multiple scales. STREAM ensembles consistently bracket ground-truth observations and replicate the autocorrelation structure of ground-truth precipitation fields. STREAM is compatible with pixel-scale error/uncertainty formulations beyond those presented here, and could be applied to other precipitation sources such as numerical weather predictions or blended products. Although ground truth is used here to parameterize pixel-scale uncertainty, if combined with other recent work in SMP uncertainty characterization, STREAM could be used without any ground data.

中文翻译：

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使用非平稳、各向异性自相关模型的卫星降水不确定性的集合表示

卫星多传感器降水 (SMP) 和其他卫星信息降水产品在水资源建模中的有用性可能会受到随时空尺度变化很大的大量误差的阻碍。处理这些错误的一种方法是将 SMP 与集合生成方法相结合，这样每个集合成员都反映了对真实但未知的降水的一个合理认识。这需要复制 SMP 错误的时空自相关结构。由于缺乏地面参考观测，大多数地点的这种结构的气候学是未知的，而任何特定降水系统内独特的各向异性和非平稳性限制了这种气候学与描述单个风暴系统的相关性。因此，表征和模拟跨时空尺度的自相关被称为降水界的一项重大挑战。我们引入了时空降雨误差和自相关模型 (STREAM)，它将未校准、各向异性和非平稳 SMP 时空相关模型与像素尺度降水误差模型相结合，以随机生成类似于“地面真实”降水的集合降水场。我们以高分辨率（1 小时，0.1°）生成 STREAM 降水集合，并在多个尺度上评估这些集合。STREAM 集成始终支持地面实况观测并复制地面实况降水场的自相关结构。STREAM 与此处介绍的以外的像素级误差/不确定性公式兼容，并可应用于其他降水源，例如数值天气预报或混合产品。尽管此处使用地面实况来参数化像素尺度的不确定性，但如果与 SMP 不确定性表征中的其他近期工作相结合，则可以在没有任何地面数据的情况下使用 STREAM。