COST 296 Mitigation of Ionospheric Effects on Radio Systems (MIERS) Preface

ISSN 1593-5213International audienc

An updating of the SIRM model

The SIRM model proposed by Zolesi et al. (1993, 1996) is an ionospheric regional model for predicting the vertical-sounding characteristics that has been frequently used in developing ionospheric web prediction services (Zolesi and Cander, 2014). Recently the model and its outputs were implemented in the framework of two European projects: DIAS (DIgital upper Atmosphere Server; http://www.iono.noa.gr/DIAS/) (Belehaki et al., 2005, 2015) and ESPAS (Near-Earth Space Data Infrastructure for e-Science; http://www.espas-fp7.eu/) (Belehaki et al., 2016). In this paper an updated version of the SIRM model, called SIRMPol, is described and corresponding outputs in terms of the F2-layer critical frequency (foF2) are compared with values recorded at the mid-latitude station of Rome (41.8°N, 12.5°E), for extremely high (year 1958) and low (years 2008 and 2009) solar activity. The main novelties introduced in the SIRMPol model are: (1) an extension of the Rome ionosonde input dataset that, besides data from 1957 to 1987, includes also data from 1988 to 2007; (2) the use of second order polynomial regressions, instead of linear ones, to fit the relation foF2 vs. solar activity index R12 ; (3) the use of polynomial relations, instead of linear ones, to fit the relations A0 vs. R12 , An vs. R 12 and Yn vs. R12 , where A0 , An and Yn are the coefficients of the Fourier analysis performed by the SIRM model to reproduce the values calculated by using relations in (2). The obtained results show that SIRMPol outputs are better than those of the SIRM model. As the SIRMPol model represents only a partial updating of the SIRM model based on inputs from only Rome ionosonde data, it can be considered a particular case of a single-station model. Nevertheless, the development of the SIRMPol model allowed getting some useful guidelines for a future complete and more accurate updating of the SIRM model, of which both DIAS and ESPAS could benefit.Published1249-12602A. Fisica dell'alta atmosferaJCR Journa

Neural Networks and Cascade Modeling Technique in System Identification

The use of the Middle East Technical University Neural Network and Cascade Modeling (METU-NN-C) technique in system identification to forecast complex nonlinear processes has been examined. Special cascade models based on Hammerstein system modeling have been developed. The total electron content (TEC) data evaluated from GPS measurements are vital in telecommunications and satellite navigation systems. Using the model, forecast of the TEC data in 10 minute intervals 1 hour ahead, during disturbed conditions have been made. In performance analysis an operation has been performed on a new validation data set by producing the forecast values. Forecast of GPS-TEC values have been achieved with high sensitivity and accuracy before, during and after the disturbed conditions. The performance results of the cascade modeling of the near Earth space process have been discussed in terms of system identification