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Data assimilation for neutral thermospheric species during geomagnetic storms

机译:地磁暴期间中性热层物质的数据同化

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摘要

During a geomagnetic storm, Joule heating heats the neutral gas and drives horizontally divergent winds which force upwelling of the neutral atmosphere. The heavier molecular species N2 and O2, abundant in the lower thermosphere, are transported to high altitude where they increase the loss rate of the F region ionosphere. The “bulge” of enhanced molecular species, or depleted atomic oxygen, is long-lived, returning to equilibrium mainly through the slow process of molecular diffusion. Its longevity, of the order of a day, enables the global wind system to transport the composition disturbance over thousands of kilometers, driven by the combination of quiet and storm-time wind fields. In a stand-alone physical model the formation and subsequent movement of the composition features depend on accurate specification of the spatial and temporal distribution of the Joule heating from the magnetosphere and knowledge of the time-dependent wind fields to define the transport. Neither is sufficiently well known given current observational capability. An alternative approach is to combine the knowledge contained in a physical model with observations of the thermospheric composition. It has been demonstrated that FUV images can provide a reliable estimate of the magnitude and structure of oxygen-depleted regions on the sunlit side of Earth. A Kalman filter data assimilation method has been developed to combine FUV observations with a physical model in order to optimally define the global distribution of neutral thermosphere composition. This distribution is used as one of the important drivers in a model for Global Assimilation of Ionospheric Measurements (GAIM) in order to improve specification and forecast of the response of the ionosphere to geomagnetic storms.
机译:在地磁风暴期间,焦耳加热会加热中性气体并驱动水平发散的风,这会迫使中性大气上升。在较低的热层中富集的较重的分子物种N2和O2被输送到高海拔,在那里它们增加了F区电离层的损失率。增强的分子种类(或耗尽的原子氧)的“凸起”是长寿命的,主要通过分子扩散的缓慢过程恢复到平衡。它的使用寿命长达一天左右,使全球风能系统在静风和暴风时风场的共同作用下,可以将成千上万的干扰物输送到数千公里之外。在独立的物理模型中,组成特征的形成和随后的运动取决于对磁层焦耳热的时空分布的准确说明以及对随时间变化的风场的知识以定义运输方式。给定当前的观测能力,这两个都不是众所周知的。一种替代方法是将物理模型中包含的知识与对热层组成的观测结合起来。已经证明,FUV图像可以提供对地球阳光照射侧的缺氧区域的大小和结构的可靠估计。已开发出卡尔曼滤波数据同化方法,将FUV观测值与物理模型相结合,以最佳地定义中性热层成分的整体分布。此分布被用作全球电离层测量同化(GAIM)模型中的重要驱动因素之一,以改善电离层对地磁风暴响应的规范和预测。

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