将基于空间的热层中性密度 (TND) 数据同化到太阳活动频繁和频繁时期的 TIE-GCM 耦合模型中

IF 3.7 2区 地球科学
Space Weather Pub Date : 2024-03-31 DOI:10.1029/2023sw003811
Mona Kosary, Saeed Farzaneh, Maike Schumacher, Ehsan Forootan
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引用次数: 0

摘要

高层大气模式提供了不同高度的热层中性密度(TND)和电子密度(Ne)的全球估算,但其预测质量有待提高。在本研究中,我们介绍了将在低地球轨道(LEO)任务中测量到的天基热层中性密度同化到 NCAR 热层-电离层-电动力学大气环流模式(TIE-GCM)中所产生的影响。在这些实验中,应用了数据同化研究试验台(DART)社区软件的集合卡尔曼滤波器(EnKF)合并器。为了涵盖各种天基 TND 数据以及低太阳活动期和高太阳活动期,我们使用了 CHAMP(挑战性微型卫星有效载荷)和 Swarm-C 的测量数据作为同化观测数据。然后,分别根据 GRACE(重力恢复和气候实验任务)和 Swarm-B 的独立 TND 预测验证了 TND 预测。为了介绍热层对电离层参数估计的影响,Ne 的输出与无线电掩星数据进行了验证。数据同化(DA)结果表明,在太阳活动低(高)时,TIE-GCM 高估(低估)了 TND 和 Ne。经过数据同化后,对 TND 的预报有了显著改善,即在太阳活动低期和高期,均方根误差(RMSE)分别降低了 79% 和 51%。Ne 的降低值分别为 52.3% 和 40.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assimilating Space-Based Thermospheric Neutral Density (TND) Data Into the TIE-GCM Coupled Model During Periods With Low and High Solar Activity
The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.
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