Four primary plasma instability processes have been proposed in the literature to explain the generation of phase scintillation associated with polar cap plasma patches. These are the gradient drift, current convective, and Kelvin‐Helmholtz instabilities and a small‐scale “turbulence” process. In this paper the range of possible values of the linear growth rates for each of these processes is explored using Dynamics Explorer 2 satellite observations. It is found that the inertial turbulence instability is the dominant process, followed by inertial gradient drift, collisional turbulence, and collisional shortwave current convective instabilities. The other processes, such as Kelvin‐Helmholtz, collisional gradient drift, and inertial shortwave current convective instabilities, very rarely (<1% of the time) give rise to a growth rate exceeding 1/60, that is deemed to be significant (in publications) to give rise to GPS scintillation.

[1] A model is developed of the gradient drift instability growth rate in the north polar cap ionosphere, utilizing a novel approach employing an ionospheric imaging algorithm. The growth rate values calculated by this model are in turn used to estimate how the amplitudes of actual gradient drift waves vary over time as the plasma drifts and the growth rates change with time. Ionospheric imaging is again used in order to determine plasma drift velocities. The final output from the model is in turn used to assess the linear correlation between the scintillation indices S 4 and s f recorded by several GPS L1 band scintillation receivers stationed in the north polar cap and mean gradient drift wave amplitudes. Four separate magnetic storm periods, totaling 13 days, are analyzed in this way. The results show weak but significant linear correlations between the mean wave amplitudes calculated and the observed scintillation indices at F layer altitudes.Citation: Burston, R., I. Astin, C. Mitchell, L. Alfonsi, T. Pedersen, and S. Skone (2009), Correlation between scintillation indices and gradient drift wave amplitudes in the northern polar ionosphere,

[1] A model is presented of the growth rate of turbulently generated irregularities in the electron concentration of northern polar cap plasma patches. The turbulence is generated by the short-term fluctuations in the electric field imposed on the polar cap ionosphere by electric field mapping from the magnetosphere. The model uses an ionospheric imaging algorithm to specify the state of the ionosphere throughout. The growth rates are used to estimate mean amplitudes for the irregularities, and these mean amplitudes are compared with observations of the scintillation indices S 4 and s by calculating the linear correlation coefficients between them. The scintillation data are recorded by GPS L1 band receivers stationed at high northern latitudes. A total of 13 days are analyzed, covering four separate magnetic storm periods. These results are compared with those from a similar model of the gradient drift instability (GDI) growth rate. Overall, the results show better correlation between the GDI process and the scintillation indices than for the turbulence process and the scintillation indices. Two storms, however, show approximately equally good correlations for both processes, indicating that there might be times when the turbulence process of irregularity formation on plasma patches may be the controlling one.

Abstract.A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from groundbased Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.

Biomass is a spaceborn polarimetric P-band (435 MHz) synthetic aperture radar (SAR) in a dawn-dusk low Earth orbit. Its principal objective is to measure biomass content and change in all the Earth's forests. The ionosphere introduces Faraday rotation on every pulse emitted by low-frequency SAR and scintillations when the pulse traverses a region of plasma irregularities, consequently impacting the quality of the imaging. Some of these effects are due to Total Electron Content (TEC) and its gradients along the propagation path. Therefore, an accurate assessment of the ionospheric morphology and dynamics is necessary to properly understand the impact on image quality, especially in the equatorial and tropical regions. To this scope, we have conducted an in-depth investigation of the significant noise budget introduced by the two crests of the Equatorial Ionospheric Anomaly (EIA) over Brazil and South-East Asia. The work is characterized by a novel approach to conceive a SAR-oriented ionospheric assessment, aimed at detecting and identifying spatial and temporal TEC gradients, including scintillation effects and Traveling Ionospheric Disturbances, by means of Global Navigation Satellite Systems (GNSS) ground-based monitoring stations. The novelty of this approach resides in the customization of the information about the impact of the ionosphere on SAR imaging as derived by local dense networks of ground instruments operating during the passes of Biomass spacecraft. The results identify the EIA crests as the regions hosting the bulk of irregularities potentially causing degradation on SAR imaging. Interesting insights about the local characteristics of low-latitudes ionosphere are also highlighted.