engleski

Empirical statistical model for predicting geomagnetic storm levels based on remote solar observations

Prediction of strongly geoeffective events, caused by coronal mass ejections (CMEs), is one of main issues of space weather. Predictions can be made by monitoring near-Earth interplanetary parameters, but only about 1 hour in advance. For early warnings, remote solar observations should be considered instead. With current knowledge on CMEs we are still not able to predict its arrival and geoeffectiveness, or even if it will entirely miss the Earth. Therefore, an empirical statistical model was developed that can be used as an early geomagnetic storm warning. For that purpose, a random sample of solar flare-associated CMEs in the time period 1996-2011 was compiled and the statistics of the associated Dst (disturbance storm time) index was analyzed. The sample contains geoeffective and non- geoeffective CMEs, as well as CMEs that missed the Earth. An extensive statistical study was performed to determine the probability distributions for Dst depending on the CME and flare characteristics and to quantify these relationships. Based on this analysis, a statistical model for predicting the probability for geomagnetic storm level was developed, which uses remote solar observations of CMEs and flares for the model input. We tested the model against the independent list of events in the ascending phase of the solar cycle 24. This work has received funding from the European Commission FP7 Project COMESEP (263252).

space weather ; coronal mass ejections ; geomagnetic storms

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