文章基本信息

标题：Data-Driven Inference of Thermosphere Composition During Solar Minimum Conditions
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作者：Piyush M. Mehta ; Richard Linares ; Eric K. Sutton 等
期刊名称：Space Weather
印刷版ISSN：1542-7390
出版年度：2019
卷号：17
期号：9
页码：1364-1379
DOI：10.1029/2019SW002264
语种：English
出版社：American Geophysical Union
摘要：Mass density variations can deviate from the expected behavior caused by temperature due to changes in the composition. Such deviations can be especially significant during solar minimum conditions. Model-data differences are typically resolved through temperature corrections while overlooking the role of errors in lower boundary composition. In this work, we use a data-driven methodology to simultaneously estimate thermosphere composition and temperature contributions to model-data differences. The methodology uses modal decomposition to extract high-dimensional, reduced order basis functions for the covariance of the neutral thermospheric species and temperature. The extracted basis functions are combined with CHAllenging Minisatellite Payload and Gravity Recovery And Climate Experiment mass density measurements using a nonlinear least squares solver. We demonstrate the methodology using the Naval Research Laboratory's empirical Mass Spectrometer and Incoherent Scatter (MSIS) model to derive the high-dimensional basis functions. We characterize and quantify the contribution of temperature and lower boundary effects with oxygen and helium since the two species have a direct impact on drag and orbit prediction through gas-surface interactions and mass density. We analyze the month of December in 2008, based on the work of Thayer et al. (2012), and estimate that lower boundary composition errors contribute approximately 50% of the model-data differences.