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Assessing the performance of thermospheric modeling with data assimilation throughout solar cycles 23 and 24

机译:通过整个太阳周期23和24的数据同化评估热层建模的性能

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Data assimilation procedures have been developed for thermospheric models using satellite density measurements as part of the EU Framework Package 7 Advanced Thermosphere Modelling of Orbital Prediction project. Two models were studied: one a general circulation model, Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM), and the other a semiempirical drag temperature model, Drag Temperature Model (DTM). Results of runs using data assimilation with these models were compared with independent density observations from CHAMP and GRACE satellites throughout solar cycles 23 and 24. Time periods of 60 days were examined at solar minimum and maximum, including the 2003 Hallowe'en storms. The differences between the physical and the semiempirical models have been characterized. Results indicate that both models tend to show similar behavior; underestimating densities at solar maximum and overestimating them at solar minimum. DTM performed better at solar minimum, with both models less accurate at solar maximum. A mean improvement of ∼4% was found using data assimilation with TIEGCM. With further improvements, the use of general circulation models in operational space weather forecasting (in addition to empirical methods currently used) is plausible. Future work will allow near-real-time assimilation of thermospheric data for improved forecasting.
机译:已经使用卫星密度测量为热层模型开发了数据同化程序,这是欧盟框架计划7轨道预测高级热层建模的一部分。研究了两种模型:一种是通用循环模型,即热球电离层电动力学通用循环模型(TIEGCM),另一种是半经验阻力温度模型,即阻力温度模型(DTM)。将使用这些模型的数据同化的运行结果与CHAMP和GRACE卫星在整个太阳周期23和24中进行的独立密度观测进行了比较。以太阳的最小和最大时间(包括2003年的万圣节风暴)检查了60天的时间段。物理模型和半经验模型之间的差异已被表征。结果表明,两种模型都倾向于表现出相似的行为。低估了太阳最大值时的密度,而高估了它们在太阳最小值时的密度。 DTM在最低日照下表现更好,而两个模型在最高日照时精度都较低。使用TIEGCM进行数据同化后,平均改进率约为4%。随着进一步的改进,在运行空间天气预报中使用通用循环模型(除了当前使用的经验方法之外)似乎是合理的。未来的工作将允许近乎实时地吸收热层数据,以改善预报。

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