CAREER: Self-Consistent Density Estimates from Accelerometers for Improved Understanding, Modeling, and Forecasting of Upper Atmosphere Variability

职业：通过加速度计进行自洽密度估计，以提高对高层大气变化的理解、建模和预测

• 批准号: 2140204
• 负责人: Piyush Mehta
• 金额: $ 64.07万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140204&HistoricalAwards=false
• 关键词: CAREER; Self; Consistent; Density; Estimates

This CAREER award supports a study at the University of West Virginia (UWV) designed to enhance the understanding, modeling, and forecasting of upper atmosphere variability by developing an orbital decay prediction capability for active satellites and space debris. High precision, high resolution accelerometer-derived mass density estimates have been the workhorse of scientific investigations and modeling efforts for the upper atmosphere density over the past two decades. However, the current limited knowledge of Gas-Surface Interaction (GSI) used in the density determination based upon measured atmospheric drag has the consequence that model results are not self-consistent. An iterative process can address this inconsistency, but this requirement complicates an already complex process and can be computationally challenging. The award is aimed at resolving this inconsistency through the development of a full-state (composition, temperature, winds) reduced order model (ROM) and directly assimilating drag acceleration measurements to simultaneously estimate the GSI effects and thermosphere parameters. Using these estimated parameters, the developed GSI model would be used to comprehensively investigate the spatial and temporal response of neutral density to geomagnetic storms and would serve as an invaluable resource for advancing science and operations. Accurate orbit predictions can provide more confidence in decision-making, enhance the safety of humans in space and facilitate long-term preservation of the space environment. This CAREER award would support the UWA space science education through initiatives targeted at rural, first-generation, Appalachian students and K-12 teachers. The award would support the training of two graduate and multiple undergraduate students in both research and educational outreach activities. A new course called “Space Physics and Space Systems” would be introduced into the mechanical and aeronautical engineering curriculum.The research supported by this award would achieve the development of a ROM for physics-based upper atmosphere density models, expanding its role in science and operations. The ROM would make the process of deriving densities estimates using measurements of acceleration on-board satellites become more self-consistent and would provide new insights into the temporal and spatial response of the neutral density to geomagnetic storms. It would also help operators advance accurate modeling of satellite drag for orbit prediction, collision avoidance and space environment sustainability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该职业奖支持西弗吉尼亚大学 (UWV) 的一项研究，旨在通过开发活动卫星和空间碎片的轨道衰变预测能力来增强对高层大气变化的理解、建模和预测。在过去的二十年中，推导的质量密度估计一直是高密度大气科学研究和建模工作的主力，然而，目前用于基于测量的大气阻力确定密度的气体表面相互作用（GSI）的知识有限。的后果是迭代过程可以解决这种不一致问题，但这一要求使本已复杂的过程变得复杂，并且在计算上可能具有挑战性。该奖项旨在通过开发全状态（成分、温度）来解决这种不一致问题。 、风）降阶模型（ROM）和直接同化阻力加速度测量来同时估计GSI效应和热层参数，利用这些估计参数，开发的GSI模型将用于综合研究空间和时间。中性密度对地磁风暴的响应，将成为推进科学和操作的宝贵资源，可以为决策提供更多信心，增强人类在太空的安全，并促进太空环境的长期保护。该职业奖将通过针对农村第一代阿巴拉契亚学生和 K-12 教师的举措来支持西澳大学的空间科学教育。该奖项将支持对两名研究生和多名本科生进行研究和教育推广活动的培训。新课程称为“空间物理和空间系统”将被引入机械和航空工程课程。该奖项支持的研究将实现基于物理的高层大气密度模型的ROM的开发，扩大其在科学和操作中的作用。将使使用卫星上加速度测量得出密度估计的过程变得更加自洽，并将为中性密度对地磁暴的时间和空间响应提供新的见解，它还将帮助运营商推进卫星的精确建模。拖该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response

极地电离层加热与中性密度响应之间时滞的全球变化

• DOI: 10.1029/2022sw003410
• 发表时间: 2023-04
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: Weimer, Daniel R.;Mehta, Piyush M.;Licata, R. J.;Tobiska, W. K.
• 通讯作者: Tobiska, W. K.

Science Through Machine Learning: Quantification of Post‐Storm Thermospheric Cooling

通过机器学习进行科学：风暴后热层冷却的量化

• DOI: 10.1029/2022sw003189
• 发表时间: 2022-06-12
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: R. Licata;P. Mehta;D. Weimer;D. Drob;W. Tobiska;J. Yoshii
• 通讯作者: J. Yoshii

Thermosphere and satellite drag

热层和卫星阻力

• DOI: 10.1016/j.asr.2023.05.011
• 发表时间: 2023-05
• 期刊: Advances in Space Research
• 影响因子: 2.6
• 作者: Bruinsma, Sean;Dudok de Wit, Thierry;Fuller;Garcia;Mehta, Piyush;Schiemenz, Fabian;Shprits, Yuri Y.;Vasile, Ruggero;Yue, Jia;Elvidge, Sean
• 通讯作者: Elvidge, Sean

MSIS‐UQ: Calibrated and Enhanced NRLMSIS 2.0 Model With Uncertainty Quantification

• DOI: 10.1029/2022sw003267
• 发表时间: 2022-08-24
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: R. Licata;P. Mehta;D. Weimer;W. Tobiska;J. Yoshii
• 通讯作者: J. Yoshii

Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models

基于物理热层模型的降阶概率仿真

• DOI: 10.1029/2022sw003345
• 发表时间: 2022-11-08
• 期刊: Space Weather
• 影响因子: 3.7
• 作者: R. Licata;P. Mehta
• 通讯作者: P. Mehta