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Ionospheric foF2 forecast over Europe based on an autoregressive modeling technique driven by solar wind parameters
Author(s) -
Tsagouri I.,
Koutroumbas K.,
Belehaki A.
Publication year - 2009
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2008rs004112
Subject(s) - autoregressive model , ionosphere , meteorology , storm , solar wind , vector autoregression , environmental science , range (aeronautics) , time series , computer science , econometrics , geology , mathematics , geography , geophysics , engineering , physics , plasma , quantum mechanics , aerospace engineering , machine learning
The development of a new ionospheric forecasting algorithm, called the Solar Wind driven autoregression model for Ionospheric short‐term Forecast (SWIF) is presented. SWIF combines historical and real‐time ionospheric observations with solar wind parameters obtained in real time at the L1 point. This is achieved through the cooperation of an autoregression forecasting algorithm, called Time Series AutoRegressive (TSAR), with the empirical Storm Time Ionospheric Model that formulates the ionospheric storm time response based on solar wind input. The evaluation of SWIF's predictions was principally focused on its performance during selected storm time intervals over Europe. The results demonstrate significant improvement of SWIF's prediction capability over its predecessor, TSAR, significant improvement over climatology and evidence of SWIF's efficiency compared to other forecasting methods. Moreover, the evaluation of SWIF's output over long time periods, that include a wide range of geophysical conditions, suggests that SWIF can be used for prediction up to 24 h ahead.