Research Article

Ionospheric forecasts for the European region for space weather applications

National Observatory of Athens, Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, I. Metaxa & Vas. Pavlou St., 15236 Penteli, Greece

^* Corresponding author: tsagouri@noa.gr

Received: 17 September 2014
Accepted: 9 March 2015

Abstract

This paper discusses recent advances in the implementation and validation of the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF) that is running in the European Digital upper Atmosphere Server (DIAS) to release ionospheric forecasting products for the European region. The upgraded implementation plan expands SWIF’s capabilities in the high latitude ionosphere while the extensive validation tests in the two solar cycles 23 and 24 allow the comprehensive analysis of the model’s performance in all terms. Focusing on disturbed conditions, the results demonstrate that SWIF’s alert detection algorithm forecasts the occurrence of ionospheric storm time disturbances with probability of detection up to 98% under intense geomagnetic storm conditions and up to 63% when storms of moderate intensity are also considered. The forecasts show relative improvement over climatology of about 30% in middle-to-low and high latitudes and 40% in middle-to-high latitudes. This indicates that SWIF is able to capture on average more than one third (35%) of the storm-associated ionospheric disturbances. Regarding the accuracy, the averaged mean relative error during storm conditions usually ranges around 20% in middle-to-low and high latitudes and 24% in the middle-to-high latitudes. Our analysis shows clearly that SWIF alert criteria were designed to effectively anticipate the ionospheric storm time effects that occurred under specific interplanetary conditions, e.g., cloud Interplanetary Coronal Mass Ejections (ICMEs) and/or associated sheaths. The results provide valuable input in advancing our ability in predicting the space weather effects in the ionosphere for future developments, and further work is proposed to enhance the model forecasting efficiency to support operational applications.