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Pro‐L* ‐ A Probabilistic L* Mapping Tool for Ground Observations
Space Weather ( IF 3.8 ) Pub Date : 2020-12-16 , DOI: 10.1029/2020sw002602
R. L. Thompson 1 , S. K. Morley 2 , C. E. J. Watt 3 , S. N. Bentley 3 , P. D. Williams 3
Affiliation  

Both ground and space observations are used extensively in the modeling of space weather processes within the Earth’s magnetosphere. In radiation belt physics modeling, one of the key phase‐space coordinates is L*, which indicates the location of the drift paths of energetic electrons. Global magnetic field models allow a subset of locations on the ground (mainly subauroral) to be mapped along field lines to a location in space and transformed into L*, provided that the initial ground location maps to a closed drift path. This allows observations from ground, or low‐altitude space‐based platforms to be mapped into space in order to inform radiation belt modeling. Many data‐based magnetic field models exist; however, these models can significantly disagree on mapped L* values for a single point on the ground, during both quiet times and storms. We present a state of the art probabilistic L* mapping tool, Pro‐L*, which produces probability distributions for L* corresponding to a given ground location. Pro‐L* has been calculated for a high resolution magnetic latitude by magnetic local time grid in the Earth’s Northern Hemisphere. We have developed the probabilistic model using 11 years of L* calculations for seven widely used magnetic field models. Usage of the tool is highlighted for both event studies and statistical models, and we demonstrate a number of potential applications.

中文翻译:

Pro‐L * –用于地面观测的概率L *映射工具

地面和空间观测都广泛用于对地球磁层内的空间天气过程进行建模。在辐射带物理建模中,关键的相空间坐标之一是L *,它指示高能电子的漂移路径的位置。全局磁场模型允许将地面上某个位置的子集(主要是极耳以下)沿着磁力线映射到空间中的某个位置,并转换为L *,前提是初始地面位置映射到封闭的漂移路径。这样可以将来自地面或低空空基平台的观测结果映射到空间中,以为辐射带建模提供依据。存在许多基于数据的磁场模型。但是,这些模型在映射的L上可能会明显不同*在安静时间和暴风雨期间地面上单个点的值。我们提供了一种最新的概率L *映射工具Pro‐ L *,它可以为L *生成与给定地面位置相对应的概率分布。Pro‐ L *是通过地球北半球的局部磁场来计算的高分辨率纬度。我们已经使用11年的L *计算方法为七个广泛使用的磁场模型开发了概率模型。突出显示了该工具在事件研究和统计模型中的用法,并且我们演示了许多潜在的应用程序。
更新日期:2021-02-21
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