Local ionospheric modelling of the electron density profiles retrieved from FORMOSAT-3/COSMIC using artificial neural network
Electrondensity is one of the significant parameters for monitoring and describing the ionosphere.The ionosphere is a consequential source of errors for the GPS signals that traverse through the ionosphere on their way to the ground-based receivers because there is a high concentration of free electrons and ionsreleased by the ionizingaction of solar X-ray and ultraviolet radiation on atmospheric formers.Radio Occultation(RO) is one of the most modern satellite techniques to study on vertical profiles of neutral density, temperature, pressure and water vapor in the stratosphere and troposphere and ionospheric electron density profiles with high vertical resolutions.Since the RO technique using the GPS signals was employed for the first time by the Global Positioning System Meteorology (GPS/MET), the low-earth-orbit-based GPS RO technique has been proven as a successful method in exploring the earth’s lower atmosphere and ionosphere.
Abel transformation is the basic hypothesis made in the retrieval of radio-occulted ionospheric parameters.The Abel inversion is a powerful tool to retrieve high-resolution vertical profiles of electron density from GPS radio occultation collected by satellites into Low Earth Orbit(LEO).
COSMIC satellite records measurements during the whole day and is not limited to the specific times and special atmospheric conditions.It should be noted that the GPS radio occultation techniques provide continuous and useful ionospheric layers information and are not obtained from the point wise measurements by other satellites.
Also, COSMIC satellite records the altitude for the measurements of the electron density profile. COSMIC satellite provides more than1000 electron density profiles per day with approximately global coverage and also parts of them cover IRAN country.In this approach, the LEO-GPS line of sight is occulted by the Earth’s limb with the setting(or rising) motion of the LEO satellite. The GPS-LEO radio connection successively records the atmospheric layers at different altitudes. The ionosphere is highly variable in space and time. Thus, for modelling the electrondensity profile, it must be considered the time changes(diurnaland seasonal) and location changes(geographical position of station). In this research, the input space includes the day number (seasonal variation), hour (diurnal variation), latitude, longitude, height and F10.7 index (measure of the solar activity). The output of the model is the ionospheric electron density profile(Ne).The COSMIC observations and IRI-2007-based data of electron density profiles were also analyzed during the solar minimum period. In this research,we used a feedforward Artificial Neural Network (ANN) with 55 neurons in hidden layer for modelling profiles of electron density of COSMIC satellite.Performance of the ANN models was evaluated using correlation coefficient (R=92%),R-Squared(0.83). It was found that the ANN model could be applied successfully in estimating the electron density profiles retrieved from FORMOSAT-3/COSMIC.The comparison of the IRI model electron density profile with COSMIC RO measurements during each month of the year 2007 over IRAN region is performed.The electron density profile from all the three International Reference Ionosphere (IRI) models, namely IRI-Neq,IRI-2001, and IRI-01-Corr are used.
The results showed that the results of the IRI2007 model electron density is not satisfactory over IRAN and ANN model electron density profile is in very good agreement with COSMIC RO measurements.
It was concluded that IRI_NEQ model is more appropriate thanthe other two models.
The results showed that the differences between the modeled profile electron density and theobserved profile electron density are very lower than the differences between the IRI-2007 models.Maximum changes occurred in January and December months at analtitudeof ~450 km and minimum changes was recorded in November month at heightof 250 Km and in April month at height of 450 Km . Also, the differences decreased in the summer at higher altitudes and in winter at lower altitudes.
فصلنامه اطلاعات جغرافیایی (سپهر), Volume:26 Issue: 101, 2017
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