Abstract A dynamical–system approach is proposed to describe the relationship between precipitation and a chosen predictor. This is done by constructing a two dimensional phase space spanned by predictor and predictant. This study uses two sounding data sets from the Tropical Western Pacific and Friuli Venezia Giulia (FVG) over North–East Italy as representatives of the tropics and midlatitudes, in addition to a basin–scale average over the winter–period North Atlantic from global re–analysis data. In contrast to conventional correlation-based approaches, the proposed approach depicts periodic cycles, as well as discharge–recharge cycles as its nonlinear extension. Discharge–recharge cycles for tropical convection are identified by using both the convective available potential energy (CAPE) and the column–integrated water vapor (CIW) as predictors, as well as the baroclinicity for the winter–period North–Atlantic rain. On the other hand, the midlatitude rain, as seen over FVG as well as the winter–period North Atlantic, does not constitute a well–defined periodic cycle either with CAPE or CIW as a predictor. The inferred phase–space trajectories are more deterministic at peripheries of dense data areas rather than at a middle in the phase space. Data–dense areas in phase space, where traditional approaches primarily focus, are associated with more prediction uncertainties in our analysis due to more phase-velocity fluctuations.

中文翻译：

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热带和中纬度地区降水的动力学系统描述

摘要 提出了一种动力学系统方法来描述降水与选定预测因子之间的关系。这是通过构建由预测器和预测器跨越的二维相空间来完成的。本研究使用来自热带西太平洋和意大利东北部的弗留利威尼斯朱利亚 (FVG) 的两个探测数据集作为热带和中纬度地区的代表，此外还使用了来自全球资源的冬季北大西洋盆地尺度平均值。 ——分析数据。与传统的基于相关性的方法相比，所提出的方法将周期性循环以及放电-充电循环作为其非线性扩展。热带对流的放电-补给循环是通过使用对流可用势能 (CAPE) 和柱状综合水汽 (CIW) 作为预测因子来确定的，以及冬季北大西洋降雨的斜压性。另一方面，如 FVG 和冬季北大西洋上空所见的中纬度降雨，无论以 CAPE 还是 CIW 作为预测因子，都不构成明确定义的周期循环。推断的相空间轨迹在密集数据区域的外围比在相空间的中间更具确定性。由于更多的相速度波动，相空间中的数据密集区域是传统方法主要关注的地方，在我们的分析中与更多的预测不确定性相关。推断的相空间轨迹在密集数据区域的外围比在相空间的中间更具确定性。由于更多的相速度波动，相空间中的数据密集区域是传统方法主要关注的地方，在我们的分析中与更多的预测不确定性相关。推断的相空间轨迹在密集数据区域的外围比在相空间的中间更具确定性。由于更多的相速度波动，相空间中的数据密集区域是传统方法主要关注的地方，在我们的分析中与更多的预测不确定性相关。