Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes
Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes
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DOI：
10.1029/2018wr022726
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发表时间：
2018-10
影响因子：
5.4
通讯作者：
S. Papalexiou;Y. Markonis;F. Lombardo;A. Aghakouchak;E. Foufoula‐Georgiou
中科院分区：
文献类型：
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作者：
S. Papalexiou;Y. Markonis;F. Lombardo;A. Aghakouchak;E. Foufoula‐Georgiou
降水和温度等水文气候变量通常是由气候模式在比业务目的所需的更粗的时空尺度上测量或模拟的。这激发了半个多世纪的研究，开发分解方法，将粗尺度时间序列分解成更细的尺度，有两个主要目标：(a)再现细尺度过程的统计特性；(b)保留原始的粗尺度数据。现有的方法要么在精细尺度上保留有限数量的统计矩，这往往是不够的，可能导致实际边际分布的非代表性近似值，要么是基于有限数量的先验分布假设，例如对数正态分布。此外，它们不能解释潜在的精细尺度过程中的潜在非平稳性。本文介绍了一种新的解聚方法，称为保持边际和相关性的解聚方法（DiPMaC），它能够将粗尺度时间序列解聚到任何更细的尺度，同时再现精细尺度过程的概率分布和线性相关结构。将DiPMaC推广到任意非平稳情形，以再现时变的边际。此外，我们还引入了一种基于伯努利试验的计算效率算法，以优化分解过程并保证粗尺度值的保留。我们将重点放在时间分解上，并通过将月降水量分解为每小时和具有趋势的时间序列（例如，气候模式预测）来演示该方法，同时我们展示了其基于一般非平稳情景分解的潜力。实例应用验证了DiPMaC的性能和鲁棒性。
Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse‐scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine‐scale process and (b) preserving the original coarse‐scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine‐scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (DiPMaC), that is able to disaggregate a coarse‐scale time series to any finer scale, while reproducing the probability distribution and the linear correlation structure of the fine‐scale process. DiPMaC is also generalized for arbitrary nonstationary scenarios to reproduce time varying marginals. Additionally, we introduce a computationally efficient algorithm, based on Bernoulli trials, to optimize the disaggregation procedure and guarantee preservation of the coarse‐scale values. We focus on temporal disaggregation and demonstrate the method by disaggregating monthly precipitation to hourly, and time series with trends (e.g., climate model projections), while we show its potential to disaggregate based on general nonstationary scenarios. The example applications demonstrate the performance and robustness of DiPMaC.