Speaker
Description
The ionosphere and thermosphere are critical regions of Earth’s upper atmosphere, playing a significant role in technologies such as radio communication and Global Navigation Satellite Systems (GNSS). However, their variability, driven by solar and geomagnetic activity, as well as interactions with neutral molecules and the lower atmosphere, makes accurate prediction of their state particularly challenging. Reliable ionospheric and thermospheric models are essential to mitigate risks to terrestrial technologies, prompting significant investment in upper atmosphere nowcasting and forecasting. The Advanced Ensemble Networked Assimilation System (AENeAS) is being deployed at the UK Met Office as an operational model for nowcasting and forecasting the coupled ionosphere-thermosphere system. It is built on the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) and enhanced through data assimilation of GNSS, ionosonde, and radio occultation observations, amongst others. This study presents a systematic validation of AENeAS under a wide range of geophysical conditions, with a particular focus on the design and application of robust validation methods. We evaluate model performance across different latitudes and under a range of solar and geomagnetic forcing scenarios, using independent observational datasets not assimilated into the model. Comparisons are also made with other established upper atmosphere models to benchmark AENeAS’s relative performance. Special attention is given to how different assimilated data types influence output accuracy, and how ensemble spread and bias evolve in time and space. We also examine the impact of key tuning parameters on model performance, with the aim of informing future operational improvements. This validation effort benchmarks AENeAS against established observational datasets, including ionosonde and GNSS TEC data, and against other upper atmosphere models, such as its background model TIE-GCM, the International Reference Ionosphere (IRI), and the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM). This work provides a clear assessment of its capabilities and limitations, as well as representing a key step in strengthening confidence in operational upper atmosphere forecasting and guiding AENeAS’s continued development.
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