Can we estimate atmospheric predictability by performance of neural network forecasting? the toy case studies of unforced and forced lorenz models
Can we estimate atmospheric predictability by performance of neural network forecasting? the toy case studies of unforced and forced lorenz models
复制标题
我们可以通过神经网络预测的性能来估计大气的可预测性吗？
DOI：
10.1109/cimsa.2005.1522829
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
V. Pelino
中科院分区：
文献类型：
--
作者：
A. Pasini;V. Pelino
我们提出了一个分析的可预测性的几个区域的吸引子的洛伦兹-63系统，一个简单的非线性模型，模仿的一些功能的大气，如其混乱的行为和存在的首选状态或“制度”。在这个框架中，通过对吸引子的预测活动，多层感知器显示出其识别吸引子的各个区域中的可预测性的不同值的能力，如果与本地可预测性的其他估计相比，如所谓的“繁殖矢量”的增长率。此外，根据最近的研究弱强加强迫对洛伦兹模型的影响，作为一个玩具模拟增加的人为强迫对气候系统的影响，我们分析了一个新的情况下，神经网络预测的可预测性的变化。因此，即使本文件必须被认为是一个初步的尝试，在使用神经网络的可预测性评估，这项活动显示了良好的效果，并打开了进一步改进和应用的前景。
We present an analysis of the predictability for several regions on the attractor of the Lorenz-63 system, a simple non- linear model which mimics some features of the atmosphere, like its chaotic behaviour and the presence of preferred states or "regimes". In this framework, through a forecasting activity on the attractor, a multilayer perceptron shows its ability to recognise different values of predictability in various zones of the attractor, if compared with other estimations of local predictability, like the growth rates of the so called "bred vectors". Furthermore, following recent studies on the impact of weak imposed forcings on the Lorenz model, as a toy simulation of increased anthropogenic forcings on the climate system, we analyse the changes of predictability for a new scenario by neural network forecasting. Therefore, even if the present paper must be considered as a preliminary attempt at the use of neural networks for predictability assessments, this activity shows good results and opens perspectives of further improvements and applications.