Speaker
Description
Understanding and preparing for space weather events is critical for our technology innovations that operate in the near-earth space environment and/or rely on trans-ionospheric and skywave signal propagation. Safety-critical radio-based applications include satellite-based positioning, navigation, and timing (PNT), HF radar, and non-terrestrial communications networks. For example, Global Navigation Satellite System (GNSS) chipsets now deliver sub-decimetre-level precise positioning for billions of PNT devices worldwide, with rapidly growing downstream markets for autonomous location-based services driving ever more rigorous safety and integrity standards. With such technologies highly vulnerable to natural and human-made interference, there is urgent need for assured space domains which identify, classify and geo-reference system threats in real-time. Here we present multi-scale adaptive models for ionospheric estimation and parameterization via data-driven models.
Fundamental to this work is an ionospheric data assimilation framework that specifies three-dimensional temporally evolving electron density distributions over regional and global spatial scales. Input observations include integrated total electron content (TEC) for multi-frequency GNSS signal combinations from ground- and space-based receivers, electron density profiles (e.g. radio occultations and ionosondes), and in situ (e.g. Swarm plasma) observations. Unique to our model is the implementation of adaptive data driven bottomside (85-200 km altitude) precipitation profiles, HF hyperspectral sensing, and the capability to ingest thousands of low-earth orbiter PNT signals for improved vertical and horizontal resolution. Features such as the auroral oval, polar patches, and storm-enhanced density are resolved in a global unifying context with space weather benchmarks estimated to inform monitoring and mitigation approaches. Adapting methods to address NATO capabilities for integrity assurance, which includes capturing threats in under-sampled regions, we propagate ionosphere states and uncertainties into user domains. To assess model performance, predictions of key metrics were generated for a three-year period and compared with more than 200,000 truth observations. The framework was validated across all real-world performance requirements.
We provide examples of space weather events from our vast network of 120+ remote sensors, a national investment in over 40 new sophisticated optical and radio instruments across Alaska, Canada and Greenland. Combined with our modelling tools, this complement forms a national scale testbed for model development and verification, as well as basic research into the space environment, remote sensing techniques, space situational awareness and impacts on technology. Targeted investigations provide insight into the most impactful and meaningful observations from existing and emerging space- and ground-based observing systems, and identify gaps and opportunities in ionospheric modelling for space domain resilience.
| Numerical model | UCTOMO |
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