Efficiency of Ionospheric Model Correction by Vertical-Incidence Sounding Data from an Ionosonde during Low Sunspot Activity

Kotonaeva, N. G.; Kolomin, M. V.; Михайлов, В. В.; Tsybulya, K.; Philippov, M.

doi:10.1134/s0016793221010084

Cited by 2 publications

(2 citation statements)

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“…Accurate forecasting is hard and impractical without involving the external geospace context, making high-data-latency ionospheric weather monitoring significantly more complex. Simply sustaining previously observed anomalies forward in time (persistence forecast) is one possibility; however, the accuracy of such plasma specification drops quickly ( [18,19] and others), reaching a 50% loss of the real-time sensing advantage versus the long-term prediction after just 2 h of delay and becoming statistically insignificant after 4 h [8]. The underlying reason for such a disappointing outcome is the ability of the ionosphere to demonstrate a nearly immediate response to changes in its drivers, such that its prior state does not effectively inform the future state of the system.…”

Section: Requirements To Sensors For Ionospheric Weather Nowcastmentioning

confidence: 99%

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Global Monitoring of Ionospheric Weather by GIRO and GNSS Data Fusion

et al. 2022

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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.

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Section: Requirements To Sensors For Ionospheric Weather Nowcastmentioning

confidence: 99%

“…Sensor network is global and continuously operating. Given relatively low spatial covariance of the ionospheric plasma dynamics (typically not to exceed 700-1000 km [18,19,21,22], coordinated observations by sensor networks are needed in most scenarios of weather monitoring.…”

Section: Requirements To Sensors For Ionospheric Weather Nowcastmentioning

confidence: 99%