Global Ionosphere Characterization: Observations from Spire's Growing CubeSat Constellation and Their Assimilation into the Spire Ionospheric Model

The ionosphere affects many radio frequency systems operating below 2 GHz. One option for mitigating these effects is to produce assimilative models of the ionospheric density from which products can be derived for specific systems. Such models aim to optimally combine a background model of the ionospheric state with measurements of the ionosphere. This approach is analogous to the use of numerical weather prediction in the meteorological community and has been evolving for ionospheric use for the last 10 to 15 years.

Published research has demonstrated the utility of this approach. However, obstacles to providing effective data products remain due to the sparseness of ionospheric data over large parts of the world and the timeliness with which data are available. Spire is working to overcome these issues through the use of its large constellation of nanosatellites that can measure total electron content (TEC) data and the use of its large ground station network that will allow low data latency. Each Spire satellite currently tracks 1 Hz, dual-frequency GNSS phase measurements through zenith and limb pointing antennas that are processed to produce slant total electron content estimates with low latency. Low-elevation GNSS links passing through the ionospheric portion below the satellite orbit are also used for electron density profile inversions. Additional 50 Hz phase data, primarily used for lower neutral atmospheric radio occultation (RO) processing, are utilized to resolve fine scale features of the E-region ionosphere, such as gravity waves and sporadic E-layers.

Spire data are also being combined with an innovative data assimilation model, the Spire TEC Environment Assimilation Model (STEAM), to provide a global representation of the ionosphere. Data assimilation is required to overcome the limitations and assumptions of the traditional Abel Transform analysis of RO data (i.e., spherical symmetry; transmitter and receiver in free space and the same plane) and to effectively combine RO data, topside data, ground-based GNSS data, and other sources of ionospheric information (i.e., ionosondes). STEAM uses a 4D Local ensemble transform Kalman Filter (LETKF). As with other ensemble methods, the LETKF uses an ensemble of models to approximate the background error covariance matrix. However, the LETKF provides a more efficient way to solve the ensemble equations; furthermore the algorithm is solved in ensemble space which makes each grid point independent, resulting in an algorithm that is easily parallelised.

Here we will highlight Spire’s ionospheric observational capabilities by providing an overview of the measurement data and processing algorithms. We also review recent results describing the coverage, quality, and potential applications of the constellation data. We will also discuss the STEAM data assimilation model and plans for the ongoing development of the combined Spire observational and modeling system.