Geoscience Reference
In-Depth Information
ent ionospheric measurements. GAIM reconstructs 3-dimensional electron density
distribution from the height of 90km up to the geosynchronous altitude (35,000km)
in a continuous basis (Scherliess et al.
2004
).
The optimization techniques which is incorporated intoGAIMinclude theKalman
Filter and four dimensional variational (4DVAR) approaches. Currently different data
types are being examinedwithGAIM, these data types include line of sight TECmea-
surements made from ground-based GPS receiver networks and space-borne GPS
receivers, ionosondes, and satellite UV limb scans. To validate the model, different
independent data sources were used. These sources are namely VTECmeasurements
from satellite ocean altimeter radar (such as those onboard TOPEX and Jason-1),
ionosonde and incoherent scatter radars (JPL
2011
). An updated version of the GAIM
model became operational at the Air Force Weather Agency (AFWA) on February,
2008. The new version of GAIM assimilates ultraviolet (UV) observations from
Defense Meteorological Satellite Program (DMSP) sensors, including the Special
Sensor Ultraviolet Limb Imager (SSULI), which has been developed by the U.S.
Naval Research Laboratory (NRL) Space Science Division (NRL
2008
).
4.1.5 MIDAS Model
The Multi-Instrument Data Analysis System (MIDAS) was designed and developed
at the University of Bath in 2001. The analysis algorithm makes use of GPS dual-
frequency observations to produce four-dimensional images of electron concentra-
tion over large geographical regions or even over the globe (Mitchell and Cannon
2002
). Different types of measurements that can be put into the MIDAS are the
satellite to ground measurements, satellite to satellite observations, measurements
from sea-reflecting radars, electron-concentration profiles from inverted ionograms,
and in-situ measurements of ionized concentration from LEO satellites. The MIDAS
algorithm reconstructs the free electron density as a piecewise constant 3D distribu-
tion, starting from collections of slant TEC data along ray paths crossing the region of
interest (Mitchell and Spencer
2003
). The essential ingredient of the MIDAS inver-
sion is the use of Empirical Orthogonal Functions (Sirovich and Everson
1992
), along
which the solution of the inverse problem is assumed to be linearly decomposable
(Materassi
2003
). MIDAS produces four-dimensional electron density maps which
can be used to correct the phase distortions and polarization changes by Faraday
rotation in the ionosphere. MIDAS also has a ray tracer which allows accurate deter-
mination of the refracting ray paths and hence the apparent sky location of a radio
source.
4.2 Eliminating TEC
TEC is a very complicated quantity. It depends on many parameters such as sunspot
activity, seasonal and diurnal variations, the line of signal propagation, and the posi-
