Singular Vector Decomposition (SVD) of satellite datasets: relation between cloud properties and climate indices

IF 3.2 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Elisa Carboni, Gareth E. Thomas, Richard Siddans, Brian Kerridge
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引用次数: 0

Abstract

Abstract. We describe a technique using singular vector decomposition (SVD), that can identify the spatial patterns that best describe the temporal variability of a global satellite dataset. These patterns, and their temporal evolution, are then correlated with established climate indices. We apply this technique to datasets of cloud properties over three decades, derived from five visible/IR imagers ((A)ATSR, SLSTR-A/-B and MODIS and jointly from the IR and microwave sounders on MetOp (IASI, MHS,AMSU-A), but it can be more generically used to extract the pattern of variability of any regular gridded dataset such as different parameters from satellite products and models. The leading singular vector for these three independent global data sets, on both cloud fraction and cloud-top height, from these polar orbiting satellites covering different time periods, is found to be strongly correlated with the ENSO index. The SVD approach could potentially offer a new tool for using global satellite observations in assessing global climate model (GCM) performance.
卫星数据集的奇异向量分解(SVD):云特性与气候指数的关系
摘要。我们描述了一种使用奇异向量分解(SVD)的技术,该技术可以识别最能描述全球卫星数据集时间变异性的空间模式。然后,这些模式及其时间演变与已建立的气候指数相关联。我们将该技术应用于三十年来的云特性数据集,这些数据集来自五个可见光/红外成像仪((A)ATSR, SLSTR-A/-B和MODIS,以及MetOp上的红外和微波探测仪(IASI, MHS,AMSU-A),但它可以更普遍地用于提取任何规则网格数据集的变率模式,例如来自卫星产品和模型的不同参数。研究发现,这三个独立的全球数据集(包括覆盖不同时间段的极地轨道卫星的云分数和云顶高度)的领先奇异向量与ENSO指数密切相关。SVD方法可能为利用全球卫星观测来评估全球气候模式(GCM)的性能提供一种新的工具。
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来源期刊
Atmospheric Measurement Techniques
Atmospheric Measurement Techniques METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
7.10
自引率
18.40%
发文量
331
审稿时长
3 months
期刊介绍: Atmospheric Measurement Techniques (AMT) is an international scientific journal dedicated to the publication and discussion of advances in remote sensing, in-situ and laboratory measurement techniques for the constituents and properties of the Earth’s atmosphere. The main subject areas comprise the development, intercomparison and validation of measurement instruments and techniques of data processing and information retrieval for gases, aerosols, and clouds. The manuscript types considered for peer-reviewed publication are research articles, review articles, and commentaries.
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