Modeling of Ionospheric Responses to Atmospheric Acoustic and Gravity Waves Driven by the 2015 Nepal M w 7.8 Gorkha Earthquake

Near- and far-field ionospheric responses to atmospheric acoustic and gravity waves (AGWs) generated by surface displacements during the 2015 Nepal 7.8 Gorkha earthquake are simulated. Realistic surface displacements driven by the earthquake are calculated in three-dimensional forward seismic waves propagation simulation, based on kinematic slip model. They are used to excite AGWs at ground level in the direct numerical simulation of three-dimensional nonlinear compressible Navier-Stokes equations with neutral atmosphere model, which is coupled with a two-dimensional nonlinear multifluid electrodynamic ionospheric model. The importance of incorporating earthquake rupture kinematics for the simulation of realistic coseismic ionospheric disturbances (CIDs) is demonstrated and the possibility of describing faulting mechanisms and surface deformations based on ionospheric observations is discussed in details. Simulation results at the near-epicentral region are comparable with total electron content (TEC) observations in periods ( 3.3 and 6-10 min for acoustic and gravity waves, respectively), propagation velocities ( 0.92 km/s for acoustic waves) and amplitudes (up to 2 TECu). Simulated far-field CIDs correspond to long-period ( 4 mHz) Rayleigh waves (RWs), propagating with the same phase velocity of 4 km/s. The characteristics of modeled RW-related ionospheric disturbances differ from previously-reported observations based on TEC data; possible reasons for these differences are discussed

S-system theory applied to array-based GNSS ionospheric sensing

The GPS carrier-phase and code data have proven to be valuable sources of measuring the Earth’s ionospheric total electron content (TEC). With the development of new GNSSs with multi frequency data, many more ionosphere-sensing combinations of different precision can be formed as input of ionospheric modelling. We present the general way of interpreting such combinations through an application of S-system theory and address how their precision propagates into that of the unbiased TEC solution. Presenting the data relevant to TEC determination, we propose the usage of an array of GNSS antennas to improve the TEC precision and to expedite the rather long observational time-span required for high-precision TEC determination

Estimation and analysis of multi-GNSS differential code biases using a hardware signal simulator

In ionospheric modeling, the differential code biases (DCBs) are a non-negligible error source, which are routinely estimated by the different analysis centers of the International GNSS Service (IGS) as a by-product of their global ionospheric analysis. These are, however, estimated only for the IGS station receivers and for all the satellites of the different GNSS constellations. A technique is proposed for estimating the receiver and satellites DCBs in a global or regional network by first estimating the DCB of one receiver set as reference. This receiver DCB is then used as a ‘known’ parameter to constrain the global ionospheric solution, where the receiver and satellite DCBs are estimated for the entire network. This is in contrast to the constraint used by the IGS, which assumes that the involved satellites DCBs have a zero mean. The ‘known’ receiver DCB is obtained by simulating signals that are free of the ionospheric, tropospheric and other group delays using a hardware signal simulator. When applying the proposed technique for Global Positioning System legacy signals, mean offsets in the order of 3 ns for satellites and receivers were found to exist between the estimated DCBs and the IGS published DCBs. It was shown that these estimated DCBs are fairly stable in time, especially for the legacy signals. When the proposed technique is applied for the DCBs estimation using the newer Galileo signals, an agreement at the level of 1–2 ns was found between the estimated DCBs and the manufacturer’s measured DCBs, as published by the European Space Agency, for the three still operational Galileo in-orbit validation satellites

Redes neurais artificiais aplicadas na previsão do VTEC no Brasil

Uma forma de se prever o conteúdo total de elétrons na direção vertical (VTEC - Vertical Total Electron Content) usando a arquitetura de redes neurais artificiais (RNA) denominada de perceptrons de múltiplas camadas (MLP - MultipLayer Percetrons) é apresentada e avaliada nesta pesquisa. As entradas do modelo foram definidas como sendo a posição dos pontos ionosféricos (IPP - Ionospheric Pierce Point) e o tempo universal (TU), enquanto que a saída é o VTEC. As variações sazonais e de períodos mais longos são levadas em conta através da atualização do treinamento diariamente. Testes foram conduzidos sobre uma área que abrange o Brasil e sua vizinhança considerando períodos de alta e baixa atividade solar. As RNA foram treinadas utilizando informações dos mapas globais da ionosfera (GIM - Global Ionospheric Maps) produzidos pelo serviço internacional do GNSS (IGS - International GNSS Service) das 72 horas anteriores à época de início da previsão. As RNA treinadas foram utilizadas para prever o VTEC por 72 horas (VTEC RNA). Os VTEC RNA foram comparados com os VTEC contidos nos GIM (VTEC GIM). A raiz do erro médio quadrático (RMS) da diferença entre o VTEC GIM e o VTEC RNA variou de 1,4 a 10,7 unidades de TEC (TECU). O erro relativo mostra que a RNA proposta foi capaz de prever o VTEC com 70 a 85% de acerto

Figure 13, animation

The movie represents animation of simulated vertical fluid velocities for the meridional slice along the GCMT epicenter based on Simulation (3), as discussed in Section 3.2 of the manuscript. White rectangle at the center of the video shows the region for which surface vertical velocities were suppressed. The movie supplements Figure 13 of the manuscript

Figure 10, animation

The movie represents animation of simulated ion field-aligned velocities (left panel), electron density perturbations (middle panel) and ion temperature perturbations (right panel) for the meridional slice along the GCMT epicenter based on Simulation (1), as discussed in Section 3.2 of the manuscript. Electron density and ion temperature perturbations are shown in percentages scaled relative to the background state. The movie supplements Figure 10 of the manuscript

Figure 12, data

Simulation (2) output of vertical fluid velocity (m/s) shown on Figure 12 of the manuscript. The archive contains *.mat files and a script for data reading and plotting