On correlation between SID monitor and GPS-derived TEC observations during a massive ionospheric storm development

Eyjafjallajökull Volcanic Ash 2010 Effects on GPS Positioning Performance in the Adriatic Sea Region

“…Although the major contributor in the TEC equation ( 2) is charged particles of the ionosphere layer [12,[14][15][16]; in general, the ionised patches found on the rest of the satellite signal's propagation path also play an important role [11,17]. These ionised patches may appear both above the height of the ionosphere's top level (due to mainly solar storms and cosmic radiation) [18], as well as below the ionosphere's bottom level (due to volcanic eruptions, thunderstorms and supersonic flights, respectively) [4,16].…”

“…This model ( 3) comprises of stochastic and stable patterns of the GPS ionospheric delay dynamics, a nightly constant value and a daily cosine-shaped GPS ionospheric delay rhythm, as exploited in the Klobuchar model of GPS ionospheric delay correction [3,17,20]. The stochastic component of (3) contains the GPS ionospheric delay resulting from the unexpected and unpredictable processes that contribute to the overall TEC during events of unknown and limited duration, and constrained spatial outreach, such as: ionospheric/geomagnetic storms, earthquakes, volcanic eruptions, thunderstorms and supersonic flights.…”

“…Additional attempts to model the GPS ionospheric delay were conducted using various approaches on both global and regional scales [14,17,18,21,22]. However, they still failed to recognise the sudden and stochastic local ionospheric events.…”

GNSS Positioning Error Change-point Detection in GNSS Positioning Performance Modelling

“…Sub-set formation in consideration of local variance may be extended further to analyses of the cause and result. Overlapping intervals of the same-level variance in different descriptors time series will allow determination of the cause-effect assessment more, such as in studies (Filić and Filjar, 2019) and (Filić and Filjar, 2018) conducted earlier, more accurately and efficiently.…”

“…Model development process suffers from the complexity of the positioning environment, as described with our Space Weather-GNSS positioning performance coupling model (Filić and Filjar, 2018), (Filić and Filjar, 2019). GNSS resilience has been attempted to be achieved through methodologies, including those that addresses identification of the closest linear descriptor of GNSS positioning errors (Filić and Filjar, 2019), examining GNSS positioning errors dynamics (Lenac, Filić, and Filjar, 2019), and advanced statistical learning approaches applied over massive data sets of potential positioning environment descriptors (Filić and Filjar, 2018). Still, on all attempted approaches and scenarios, the identification of the GNSS positioning performance degradation onset and duration appeared to be the critical issue that raised the model's uncertainty.…”