  In this paper, we propose a Bayesian framework to create two dimensional
ionospheric images of high spatio-temporal resolution to monitor ionospheric
irregularities as measured by the S4 index. Here, we recast the standard
Bayesian recursive filtering for a linear Gaussian state-space model, also
referred to as the Kalman filter, first by augmenting the (pierce point)
observation model with connectivity information stemming from the insight and
assumptions/standard modeling about the spatial distribution of the
scintillation activity on the ionospheric shell at 350 km altitude. Thus, we
achieve to handle the limited spatio-temporal observations. Then, by
introducing a set of Kalman filters running in parallel, we mitigate the
uncertainty related to a tuning parameter of the proposed augmented model. The
output images are a weighted average of the state estimates of the individual
filters. We demonstrate our approach by rendering two dimensional real-time
ionospheric images of S4 amplitude scintillation at 350 km over South America
with temporal resolution of one minute. Furthermore, we employ extra S4 data
that was not used in producing these ionospheric images, to check and verify
the ability of our images to predict this extra data in particular ionospheric
pierce points. Our results show that in areas with a network of ground
receivers with a relatively good coverage (e.g. within a couple of kilometers
distance) the produced images can provide reliable real-time results. Our
proposed algorithmic framework can be readily used to visualize real-time
ionospheric images taking as inputs the available scintillation data provided
from freely available web-servers.

Atmospheric and Oceanic Physics;Machine Learning