Investigating the Accuracy of IRI Model for the Ionospheric TEC parameter during Strong, Severe and Great Geomagnetic Storms from 2000-2013
Keywords:
TEC, Geomagnetic storm, IRI modelAbstract
Several efforts have been made to study the behavior of Total Electron Content (TEC) with many types of geomagnetic storm, the purpose of this research is to study the disturbances of the ionosphere through the TEC parameter during strong, severe and great geomagnetic storms and the validity of International Reference Ionosphere IRI model during these kinds of storms. TEC data selected for years 2000-2013 (descending solar cycle 23 to ascending cycle 24), as available from koyota Japan wdc. To find out the type of geomagnetic storms the Disturbance storm time (Dst) index was selected for the years (2000-2013) from the same website. Data from UK WDC have been taken for the solar indices sunspots number (SSN), radio flux (F10.7) and ionosphere index parameter (IG12). The predicted TEC are calculated from IRI model. From data analysis, it is found that there are (132) events happened in the tested years for the strong, severe and great geomagnetic storms, a largest number of solar storms appeared in years 2000 to 2005 at solar maximum from solar cycle 23 and the number of storms increases with increasing the SSN. In general, there is a good proportionality between disturbance storm time index (Dst) and the total electron contents, the values of TEC in daytime greater than nighttime, but there is anomaly when the storm continued for several hours from the day, there is a highly a broad increasing in TEC started from sunrise to sunset. Also two peaks or more appeared when two types of storms occurred remaining for one event or the storm remains for more than one day. Finally there is approximately sharp peak at noon, when the storm started in early morning. Concerning the validity of the IRI model during strong, great, and severe geomagnetic storm shows that there is a weak correlation between the observed and predicted TEC values, so that the model must be corrected during major storms.