Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9179
Authors: Perrone, L. 
Mikhailov, A. 
Title: A method for foF2 short-term (1–24 h) forecast using both historical and real-time foF2 observations over European stations: EUROMAP model
Issue Date: 10-Apr-2014
Series/Report no.: /49(2014)
DOI: 10.1002/2014RS005373
URI: http://hdl.handle.net/2122/9179
Keywords: forecast
Subject Classification01. Atmosphere::01.02. Ionosphere::01.02.03. Forecasts 
Abstract: A method for foF2 short-term forecast over Europe has been developed and implemented in the EUROMAP model. The input-driving parameters are 3 h ap indices (converted to ap(τ)), effective ionospheric T index, and real-time foF2 observations. The method includes local (for each station) regression storm models to describe strong negative disturbances under ap(τ)>30 and training models to describe foF2 variations under ap(τ) ≤ 30. The derived model was tested in two regimes: descriptive when observed 3 h ap indices were used and real forecast when predicted daily Ap were used instead of 3 h ap indices—. In the case of strong negative disturbances the EUROMAP model demonstrates on average the improvement over the lnternational Reference Ionosphere STORM-time correction model (IRI(STORM)) model: 40% in winter, 24% in summer, and 39% in equinox. The average improvement over climatology is 41% in winter, 59% in summer, and 55% in equinox. In the majority of cases this difference is statistically significant. In the case of strong positive disturbances, higher-latitude stations also manifest a significant difference between the twomodels but this difference is insignificant at lower latitude stations. The substitution of 3 h ap input indices for the predicted daily Ap ones decreases the foF2 prediction accuracy in the case of negative disturbances but practically has no effect with positive disturbances. In both cases the proposed method manifests better accuracy than the IRI(STORM) model provides. The obtained results show a real opportunity to provide foF2 forecast with the (1–24 h) lead time on the basis of predicted Ap indices
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