Inverse Modeling Using a Novel Scintillation Model Sigma to Characterize High Latitude Irregularities During a Storm and a Substorm
Abstract
We have developed a high fidelity ``Satellitebeacon Ionosphericscintillation Global Model of the upper Atmosphere" (SIGMA) which is a full 3D EM wave propagation model to simulate GPS scintillations globally. We demonstrate in this work that the results from this model can form a basic framework on the use of inverse method to understand the physics of high latitude irregularities using GPS scintillations. We are using SIGMA and an inverse method to understand the physics of the irregularities observed with GPS receivers from six different interhemispheric high latitude stations during a geomagnetic storm on 9 March 2012, and from Autonomous Adaptive LowPower Instrument Platform (AALPIP) Antarctic stations during a substorm on 9 January 2014. We utilize ancillary observations from SuperDARN, ISRs, riometers etc. to obtain some of the input parameters of SIGMA. Further, we implement a uniformgrid SIGMA simulation or a nonlinear optimization of the model to obtain the rest of the unknowns that give us the best fit with data. The input parameters of SIGMA thus derived represent the physical and propagation parameters related to the physics of the irregularity that produced those GNSS scintillations. Some of our findings from this investigation include that the spectral indices and outer scales for ionospheric heights of 350~km are higher than those at 120~km. The best fits we obtained from our inverse method mostly agree with the observations except for some cases, which might be because the spectral model we use is insufficient to describe irregularity physics. We need more auxiliary data in order to facilitate the possibility of accomplishing a unique solution to the inverse problem.
 Publication:

AGU Fall Meeting Abstracts
 Pub Date:
 December 2014
 Bibcode:
 2014AGUFMSA13A3968D
 Keywords:

 2407 Auroral ionosphere;
 2415 Equatorial ionosphere;
 2439 Ionospheric irregularities;
 2447 Modeling and forecasting