Annales Geophysicae · 2008 · 33 citations · 22 references
BusinessEarth SciencesAtmospheric ScienceForecastingTime Series AnalysisForecast VerificationShort-term ForecastingTime Series ForecastingTime Series ModelingTime Series PredictionAtmospheric Parameter EstimationAutoregressive ModelsAutoregressive Time Series ModelsIonospheric Parameter PredictionIonospheric StormsNeural Network ModelingNeural Time Series ForecastingStatistical Time Series Models
Abstract. A new method for ionospheric predictions based on time series autoregressive models (AR) that was recently developed to serve the needs of the European Digital Upper Atmosphere Server (DIAS) for short term forecast of the foF2 parameter over Europe (up to the next 24 h) is described. Its performance for various steps ahead is compared with the outcome of neural network predictors for both storm and quiet periods in two DIAS locations, Athens and Pruhonice. The results indicate that the proposed method provides robust short term forecasts of the foF2 for the middle latitude ionosphere.
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