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 dimensions of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, 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, climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000 x 4000 km², 21 years) and the Swiss Alpine region (≈500 x 500 km², 6 years).

中文翻译：

---

在天气雷达档案中寻找降水吸引子

摘要。合成天气雷达图像档案是研究降水可预测性的宝贵资源。在本文中，我们比较了从雷达降水场构造经验低维吸引子的两种不同方法。在第一种方法中，吸引子的相空间维度是使用降水场的域尺度统计来定义的，例如平均降水量、降雨比例、空间和时间相关性。第二类吸引子考虑降水的空间分布，并通过主成分分析（PCA）建立。对于这两个吸引子，我们研究了相空间中轨迹的密度、模拟态误差的增长以及分形特性。为了代表不同的尺度、气候和地形条件，分析使用了美国大陆（约 4000 x 4000 km ² , 21 年）和瑞士阿尔卑斯地区（约 500 x 500 km ² , 6 ）的多年雷达档案。年）。