AENeAS Development

This project aims to develop the Advanced European electron density (Ne) Assimilation System (AENeAS). AENeAS is a physics-based ionosphere/thermosphere model which combines advanced data assimilation techniques. Such an approach should be able to provide good nowcast performance and accurate and actionable forecasts up to 6 hours ahead.

Historically, only nowcasts and basic statistical forecasts have been produced by ionospheric models. However, improved knowledge of ionospheric physics, combined with both the scope and timeliness of ionospheric data offers new opportunities to not only improve nowcasting but also provide an enhanced forecast capability. A forecast capability means that users can adapt their operations to best overcome environmental limitations. For example, aircraft may be rerouted even before take-off to avoid poor communication conditions and activities requiring precision navigation may be suspended.

Community Model Testing

There are a wide range of ionospheric models in active use and development around the globe, utilizing a number of modelling techniques. Understanding the different abilities of these models is important for the wider community. As such a community test scenario has been developed to compare electron density profiles below the peak of the F2 layer, and the total electron content (TEC). More information about the test scenario, and the data for it, is avaialble from tinyurl.com/testscenario.

Multi-Model Ensembles (MMEs)

The neutral atmospheric density from 200 to 1000 km altitude can change by 80% diurnally as well as by one to two orders of magnitude during geomagnetic storms; sometimes in just a few hours. The upper atmosphere forecast models currently in use can result in large uncertainties in the orbital parameters when applied to satellite orbit forecasts (i.e. positional errors on the order of kilometres after a day). MMEs have demonstrated a significant reduction in RMS errors of total neutral density and further work is underway to demonstrate the use of MMEs over a wider range of conditions.