A quest for precipitation attractors in weather radar archives

IF 1.7 4区地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Nonlinear Processes in Geophysics Pub Date : 2024-06-26 DOI:10.5194/npg-31-259-2024

Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, Isztar Zawadzki

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Abstract

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km2, 21 years) and the Swiss Alpine region (≈500×500 km2, 6 years).

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寻找天气雷达档案中的降水吸引子

摘要综合天气雷达图像档案是研究降水可预测性的宝贵资源。本文比较了从雷达降水场构建经验低维吸引子的两种不同方法。在第一种方法中，吸引子的相空间变量是通过降水场的域尺度统计来定义的，如平均降水量、降雨分量以及空间和时间相关性。第二种吸引子考虑了降水的空间分布，并通过主成分分析（PCA）建立。对于这两种吸引子，我们研究了相空间中的轨迹密度、模拟状态的误差增长以及分形特性。为了代表不同的尺度、气候和地貌条件，我们利用美国大陆（≈4000×4000 平方公里，21 年）和瑞士阿尔卑斯地区（≈500×500 平方公里，6 年）的多年雷达档案进行了分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nonlinear Processes in Geophysics 地学-地球化学与地球物理

CiteScore

4.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Nonlinear Processes in Geophysics (NPG) is an international, inter-/trans-disciplinary, non-profit journal devoted to breaking the deadlocks often faced by standard approaches in Earth and space sciences. It therefore solicits disruptive and innovative concepts and methodologies, as well as original applications of these to address the ubiquitous complexity in geoscience systems, and in interacting social and biological systems. Such systems are nonlinear, with responses strongly non-proportional to perturbations, and show an associated extreme variability across scales.