MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification
MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification
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DOI：
10.1029/2022sw003267
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发表时间：
2022-08
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影响因子：
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通讯作者：
R. Licata;P. Mehta;D. Weimer;W. Tobiska;J. Yoshii
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作者：
R. Licata;P. Mehta;D. Weimer;W. Tobiska;J. Yoshii
质谱仪和非相干散射雷达（MSIS）模型系列自20世纪70年代初以来一直在发展和改进。最新版本的MSIS是海军研究实验室（NRL）MSIS 2.0经验大气模型。NRLMSIS 2.0提供了物种密度、质量密度和温度估计，作为位置和空间天气条件的函数。MSIS模型长期以来一直是研究和运营界热层模型的流行选择，但与许多模型一样，它不能提供不确定性估计。在这项工作中，我们开发了一个基于机器学习的外逸层温度模型，可以与NRLMSIS 2.0一起使用，通过外逸层温度参数直接相对于高保真卫星密度估计进行校准。我们的模型（称为MSIS-UQ）没有提供点估计，而是输出一个分布，该分布使用称为校准误差分数的指标进行评估。我们发现，MSIS-UQ对NRLMSIS 2.0进行了去偏，使模型和卫星密度之间的差异减少了25%，并且比空间部队的高精度卫星阻力模型更接近卫星密度11%。我们还显示了该模型的不确定性估计能力，通过生成物种密度，质量密度和温度的海拔剖面。这清楚地表明了NRLMSIS 2.0中外逸层温度概率如何影响密度和温度分布。另一项研究显示，相对于单独的NRLMSIS 2.0，风暴后过冷能力有所改善，增强了它可以捕获的现象。
The Mass Spectrometer and Incoherent Scatter radar (MSIS) model family has been developed and improved since the early 1970's. The most recent version of MSIS is the Naval Research Laboratory (NRL) MSIS 2.0 empirical atmospheric model. NRLMSIS 2.0 provides species density, mass density, and temperature estimates as function of location and space weather conditions. MSIS models have long been a popular choice of thermosphere model in the research and operations community alike, but—like many models—does not provide uncertainty estimates. In this work, we develop an exospheric temperature model based in machine learning that can be used with NRLMSIS 2.0 to calibrate it relative to high‐fidelity satellite density estimates directly through the exospheric temperature parameter. Instead of providing point estimates, our model (called MSIS‐UQ) outputs a distribution which is assessed using a metric called the calibration error score. We show that MSIS‐UQ debiases NRLMSIS 2.0 resulting in reduced differences between model and satellite density of 25% and is 11% closer to satellite density than the Space Force's High Accuracy Satellite Drag Model. We also show the model's uncertainty estimation capabilities by generating altitude profiles for species density, mass density, and temperature. This explicitly demonstrates how exospheric temperature probabilities affect density and temperature profiles within NRLMSIS 2.0. Another study displays improved post‐storm overcooling capabilities relative to NRLMSIS 2.0 alone, enhancing the phenomena that it can capture.