NSWP: Space Weather Prediction Using Hybrid Physics/Black-Box Methods

NSWP：使用混合物理/黑盒方法进行空间天气预报

• 批准号: 0720201
• 负责人: Edmund Spencer
• 金额: $ 15.91万
• 依托单位: Utah State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720201&HistoricalAwards=false
• 关键词: NSWP; Space; Weather; Prediction; Using

This project will combine a relatively low-order (N=8), physics-based dynamical model of the Earth's magnetosphere (WINDMI) with a nonlinear black box model based on multiresolution wavelet decomposition. The goal is to have the hybrid model take into account physics that is missing from the low-dimension analytic model. Three different approaches will be investigated. The first suggested configuration involves replacing significant portions of the low-order model that are already dedicated to calculating plasma pressure with a single black box model. The second proposed configuration would replace only a relatively small nonlinear unloading function embedded in the previously mentioned pressure calculation with a black box model. The last configuration will assume the physics in WINDMI are mostly correct, and use a nonlinear black box model to correct for residual errors. This hybrid model will be trained using a genetic nonlinear optimization algorithm to predict the AL and Dst indices using ACE solar wind data as input. The result will be a model of the magnetosphere that can predict the Dst and AL magnetic indices with high accuracy.

该项目将结合一个相对低阶（N=8）、基于物理的地球磁层动力学模型（WINDMI）和一个基于多分辨率小波分解的非线性黑箱模型。目标是让混合模型考虑低维分析模型中缺少的物理特性。我们将研究三种不同的方法。第一个建议的配置包括用一个单一的黑匣子模型取代低阶模型中已经用于计算等离子体压力的重要部分。提出的第二种配置将仅用黑盒模型取代前面提到的压力计算中嵌入的相对较小的非线性卸载函数。最后一种配置将假设WINDMI中的物理基本正确，并使用非线性黑盒模型来校正残余误差。该混合模型将使用遗传非线性优化算法进行训练，以ACE太阳风数据为输入预测AL和Dst指数。结果将建立一个能高精度预测Dst和AL磁指数的磁层模型。