Abstract
Prompt and accurate imaging of the ionosphere is essential to space weather services, given a broad spectrum of applications that rely on ionospherically propagating radio signals. As the 3D spatial extent of the ionosphere is vast and covered only fragmentarily, data fusion is a strong candidate for solving imaging tasks. Data fusion has been used to blend models and observations for the integrated and consistent views of geosystems. In space weather scenarios, low latency of the sensor data availability is one of the strongest requirements that limits the selection of potential datasets for fusion. Since remote plasma sensing instrumentation for ionospheric weather is complex, scarce, and prone to unavoidable data noise, conventional 3D-var assimilative schemas are not optimal. We describe a novel substantially 4D data fusion service based on near-real-time data feeds from Global Ionosphere Radio Observatory (GIRO) and Global Navigation Satellite System (GNSS) called GAMBIT (Global Assimilative Model of the Bottomside Ionosphere with Topside estimate). GAMBIT operates with a few-minute latency, and it releases, among other data products, the anomaly maps of the effective slab thickness (EST) obtained by fusing GIRO and GNSS data. The anomaly EST mapping aids understanding of the vertical plasma restructuring during disturbed conditions.
Highlights
Up to 50% of the VTEC must be attributed to the plasmasphere content above the ionosphere [57], which introduces significant and, in many cases, unacceptable uncertainty regarding whether such direct attribution of delta-VTEC to delta-NmF2 is realistic
The ionospheric Equivalent Slab Thickness τ is defined as a ratio of the vertical total electron content to the F2 layer peak electron density [58,59]: τ = VTEC/NmF2 where the vertical TEC is given in TEC units, NmF2 is in electrons per m3, and τ is in meters
Sensor data fusion remains a practical requirement of modern ionospheric monitoring as the 3D specification of the ionospheric plasma requires populating the vast spaces of the near-Earth environment with sensors, which is still impractical without collaboration
Summary
Ionosonde was not viewed as a weather monitoring sensor until the 1930s, when it was still called “apparatus” in the UK and “recorder” in the US. It was not that academic interest in the recently discovered ionosphere started to wear off after three decades of extraordinary advances; the yearly number of scientific publications stemming from ionosonde observations would continue to grow well into the 1970s It was the onset of high-frequency (HF) broadcasting and telecommunications that placed ionosondes into the ranks of radio-weather forecasters. The unique technical challenge in such space weather service, very unlike its terrestrial counterpart, was the complexity of sensor instrumentation: ionosondes of the 1930s required six workers to operate [1], making their continuous weather monitoring problematic without automation It was not until the late 1980s that the first fully autonomous ICED/PRISM ionospheric weather system [2,3] was established by the Air Weather Service (AWS) of the US Air Force. One IRTAM computation involves a 24-h prior history of observations at all contributing GIRO sites and produces the best IRI fit to all 24-h timelines at the sites
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