基于空间图像的气候突变检测方法研究

Many abrupt climate change events often can not be detected timely and effectively by existing abrupt change detection methods, because these mothods rely on abundant, long-term and continuous observational data. However, these data is lag behind the abrupt change. This severely restricted the understanding and awareness of the climate system. In view of this, this project intends to investigate an abrupt change detection method on the spatial images based on the nonlinear dynamical characteristics of the spatio-temporal evolutionary of the climate system. Firstly, in this project, the scaling analysis methods and approximate entropy for analyzing one-dimensional data will be generalized to space field in order to study the correlation and complexity of the spatial data. Then the spatial scaling analysis method and spatial entropy will be proposed to quantify the correlation and complexity of the spatial data. Finally, a new method will be presented for identifing the abrupt dynamic change of a spatio-temporal distribution system.A set of new theory and technologies of abrupt climate change detection from space field will be proposed in this research project. Because the images contain a wealth of information which is much more than a single time series,the new method can achieve timely and efficient abrupt change detection results and then make people have enough time to get ready for the new challenges induced by abrupt climate change.

现有突变检测技术均建立在长时间的、连续的观测记录的基础上，对于气候突变事件往往不能够进行及时有效的检测，具有很大的滞后性，这严重制约了人们对于气候系统的理解和认识。鉴于此，本项目拟从气候系统时空演变的非线性动力学特征出发，基于大气演变的时空关联特征和图像复杂性特征，将分析时间序列的标度方法和近似熵推广至空间场中，提出定量描述空间图像的空间标度方法和空间熵，进而研发识别时空分布系统的动力学突变检测技术。项目将提出一套从空间场中检测气候突变的理论和方法，由于空间图像包含的信息较单点时间序列更为丰富，新方法能实现更加及时有效地进行突变检测，这将使得人们有足够的时间去为适应气候突变所带来的新挑战做好准备。

本项目提出了空间图像的气候突变检测理论和方法，突破了以往仅仅针对单个时间序列的突变检测思路。发展了提取空间图像所蕴藏的动力学信息的非线性识别技术；提出了能够识别气候系统动力学突变的新方法。揭示了气候系统在时空演变上均具有长期记忆性特征，并基于此，提出了一种定量评估再分析资料质量的新途径。