Global Real-Time Nowcasting of Ionosphere with Giro-Driven Assimilative IRI

Abstract:

Real-time prediction of the ionosphere beyond its quiet-time median behavior has proved to be a great challenge: low-latency sensor data streams are scarce, and early comparisons conducted within the CEDAR ETI Assessment framework showed that, on average, the assimilative physics-based models perform on par with the long-term empirical predictions. This rather surprising result led to the formation of the Real-Time Task Force of the International Reference Ionosphere (IRI) science team in 2011, with a simple objective to develop a method for correcting the IRI long-term climatology definitions on the fly, i.e., in near real-time, using suitable observations. Three years later, a pilot version of the IRI-based Real-Time Assimilative Model “IRTAM” started its continuous operations at the Global Ionosphere Radio Observatory (GIRO) Data Center, using online feeds from the ionosondes contributing data to GIRO. The IRTAM version 0.1B builds and publishes every 15-minutes an updated “global weather” map of the peak density and height in the ionosphere, as well as a map of deviations from the classic IRI climate.

Incidentally, the IRTAM verification and validation efforts shed light on the forecasting capabilities of the assimilative IRI extension, even though it has not yet involved external activity indicators. At the core of the assimilative computations, a Non-linear Error Compensation Technique for Associative Restoration (NECTAR) seeks agreement between IRI prediction and the 24-hour history of latest observations at GIRO sensor sites to produce the one map frame. The NECTAR first evaluates the diurnal harmonics of the observed deviations from the IRI climatology at each GIRO site to then independently compute the spatial maps for each diurnal harmonic. Thus obtained “corrective” coefficients of the spatial-diurnal expansion are added to the original IRI set of coefficients to obtain the IRTAM specification. We are intrigued by the IRTAM capability to glean ionospheric dynamics over no-data areas, and the potential for short-term forecasting.