Impacts of uncertainties in climate data analyses (IUCliD): Approaches to working with measurements as a series of probability distributions

气候数据分析中不确定性的影响 (IUCliD)：将测量结果作为一系列概率分布进行处理的方法

• 批准号: 318206658
• 负责人: Privatdozent Dr. Norbert Marwan
• 金额: --
• 依托单位: Forschungsbereich Komplexitätsforschung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/318206658?language=en
• 关键词: Impacts; uncertainties; climate; data; analyses

Time series analysis plays a crucial role for observation-based understanding of real-world complex systems. However, most existing methods for analysing measured data are not equipped to deal with uncertainties arising from spatiotemporal variations or imprecision in the measurements themselves. In this project, a new framework that models observations as probability distributions will be developed, inherently taking into account dataset uncertainties. The focus is on reformulating two specific branches of time series analysis in terms of probability distributions. First focus is to quantify dynamical characteristics of the data and their interrelations as probabilistic likelihoods using concepts such as correlation, mutual information and power spectrum. The second step is to extend the framework of state space embedding to determine the posterior likelihood of a chosen embedding given the dataset uncertainties. These theoretical extensions will be used to estimate probabilistically defined complex networks from time series data, and to determine posterior likelihoods that a system recurs to earlier dynamical states. The developed techniques will then be applied to understand the dynamics of climatic processes as well as the impact of dataset uncertainties on such processes. Climate networks will be estimated from spatially gridded climatological data for the last 50 years which encode the likelihood of a climatic link between two spatially separated points. Such networks will help to determine self- and re-organisation of climatic links in the time periods of analysis. Recurrence analysis on spatially distributed palaeoclimatic datasets going back to 15,000 years will also be used to identify periods of abrupt climate change in the past. These methods will extend the reach of earlier time series methods which have already proven to be of use in a wide range of scientific disciplines such as climate and neuroscience, finance, astrophysics, ecology, and medicine, particularly when they involve measurement imprecision and spatiotemporal variabilities that need to be taken into account. This project will pave the way for a new kind of data analysis that is explicitly based on probability distributions instead of analysing point-like objects. A software package as well as a workshop series will support this goal.

时间序列分析对于基于观测的复杂系统的理解起着至关重要的作用。然而，大多数现有的分析测量数据的方法不具备处理时空变化或测量本身不精确所产生的不确定性的能力。在这个项目中，将开发一个新的框架，将观测值建模为概率分布，内在地考虑到数据集的不确定性。重点是在概率分布方面重新制定时间序列分析的两个具体分支。第一个重点是量化的数据的动态特性和它们之间的相互关系作为概率似然使用的概念，如相关性，互信息和功率谱。第二步是扩展状态空间嵌入的框架，以确定给定数据集不确定性的所选嵌入的后验似然。这些理论扩展将被用来估计概率定义的复杂网络的时间序列数据，并确定后验似然系统重现到早期的动态状态。开发的技术将被应用于了解气候过程的动态以及数据集的不确定性对这些过程的影响。气候网络将根据过去50年的空间网格气候数据进行估算，这些数据对两个空间分离点之间气候联系的可能性进行编码。这种网络将有助于确定分析期间气候联系的自我组织和重组。还将利用对15，000年以来空间分布的古气候数据集的重现分析来确定过去气候突变的时期。这些方法将扩展早期时间序列方法的范围，这些方法已经被证明在气候和神经科学，金融，天体物理学，生态学和医学等广泛的科学学科中使用，特别是当它们涉及需要考虑的测量不精确性和时空变化时。该项目将为一种新的数据分析铺平道路，这种数据分析明确地基于概率分布，而不是分析点状对象。一个软件包以及一系列讲习班将支持这一目标。

项目成果

Inferring interdependencies from short time series

从短时间序列推断相互依赖性

• DOI: 10.29195/iascs.01.01.0021
• 发表时间: 2017
• 作者: Goswami;P. Schultz;B. Heinze;N. Marwan;B. Bodirsky;H. Lotze-Campen;J. Kurths
• 通讯作者: J. Kurths

A unified and automated approach to attractor reconstruction

• DOI: 10.1088/1367-2630/abe336
• 发表时间: 2021-03-01
• 期刊: NEW JOURNAL OF PHYSICS
• 影响因子: 3.3
• 作者: Kraemer, K. H.;Datseris, G.;Marwan, N.
• 通讯作者: Marwan, N.

Optimal state space reconstruction via Monte Carlo decision tree search

• DOI: 10.1007/s11071-022-07280-2
• 发表时间: 2022-03-02
• 期刊: NONLINEAR DYNAMICS
• 影响因子: 5.6
• 作者: Kraemer, K. Hauke;Gelbrecht, Maximilian;Marwan, Norbert
• 通讯作者: Marwan, Norbert

Holocene climate forcings and lacustrine regime shifts in the Indian summer monsoon realm

• DOI: 10.1002/esp.5004
• 发表时间: 2020-09
• 期刊: Earth Surface Processes and Landforms
• 影响因子: 3.3
• 作者: Sushma Prasad;N. Marwan;Deniz Eroglu;B. Goswami;P. Mishra;B. Gaye;A. Anoop;N. Basavaiah;M. Stebich;A. Jehangir

Border effect corrections for diagonal line based recurrence quantification analysis measures

• DOI: 10.1016/j.physleta.2019.125977
• 发表时间: 2019-12-05
• 期刊: PHYSICS LETTERS A
• 影响因子: 2.6
• 作者: Kraemer, K. Hauke;Marwan, Norbert
• 通讯作者: Marwan, Norbert