Particle flux measurements from polar orbiting low altitude satellites provide a view of the near Earth radiation environment that is extremely valuable for science as well as space weather monitoring. Unlike, geosynchronous satellites that sample only a limited region of space (L = ∼6.6), these low altitude satellites sample the extended radiation environment (L = 1 to >10) at a relatively high time cadence (tens of minutes) that captures its global evolution. While these data are clearly useful, it is also challenging to work with because the particle flux measurements have large orbital variations related to the changing geographic location of the satellites. These orbital flux variations can sometimes obscure the time variations of interest for scientific study or space weather hazard awareness. Here, we describe and evaluate a method for removing these variations that is based on Statistical Asynchronous Regression. We demonstrate the utility and accuracy of the method by applying it to electron flux measurements from the NOAA POES and EUMetSat MetOp low altitude satellites.

中文翻译：

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从低空粒子数据中去除轨道变化的方法和应用

来自极地轨道低空卫星的粒子通量测量结果提供了近地辐射环境的视图，这对于科学以及太空天气监测都非常有价值。与地球同步卫星仅对有限的空间区域采样（L  =〜6.6）不同，这些低空卫星对扩展的辐射环境进行采样（L = 1到> 10）以相对较高的时间节奏（数十分钟）捕获了其全局演变。尽管这些数据显然有用，但使用时也面临挑战，因为粒子通量测量值的轨道变化与卫星的地理位置变化有关。这些轨道通量变化有时会掩盖科学研究或太空天气危害意识所需的时间变化。在这里，我们描述和评估一种基于统计异步回归的消除这些变化的方法。通过将其应用于NOAA POES和EUMetSat MetOp低空卫星的电子通量测量，我们证明了该方法的实用性和准确性。