The Spire TEC Environment Assimilation Model (STEAM)

The ionosphere can affect a wide range of radio frequency (RF) 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 satellites that can measure Total Electron Content (TEC) data in both zenith looking and radio occultation (RO) geometries and its large ground station network that will allow low data latency.

Spire data will be combined with an innovative data assimilation model (the Spire TEC Environment Assimilation Model, STEAM) to provide accurate and actionable ionospheric products. 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, 4D operation permits the use of data with varying latency. Localisation means that grid points are only modified by data within a local volume; this restricts spurious long-range spatial correlations and means that the ensemble only has to span the space locally. The LETKF transforms the problem into ensemble space which makes each grid point independent, resulting in an algorithm that is easily parallelised.

This paper will describe the data collection and processing chain, the data assimilation model, and plans for the ongoing development of the combined system.