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Ann. Geophys., 26, 47-57, 2008
www.ann-geophys.net/26/47/2008/
© European Geosciences Union 2008
Solar control of ambient ionization of the ionosphere near the crest of the equatorial anomaly in the Indian zone
S. K. Chakraborty and R. Hajra
Department of Physics, Raja Peary Mohan College, Uttarpara, Hooghly, Pin.712258, India
Abstract. Long-term (1978–1990) total electron content (TEC) data have been analyzed to
show the dependence of ambient ionization on EUV radiation from the Sun. TEC
observations were made at Calcutta (22.58° N, 88.38° E geographic,
dip: 32° N), situated virtually below the northern crest of the equatorial
ionization anomaly. Day-to-day changes in TEC at different local times do
not show any significant correlation with F10.7 solar flux. A good
correlation is, however, observed between the F10.7 solar flux and the monthly mean
TEC when both are considered on a long-term basis, i.e. either in the
ascending (1986–1990) or in the descending (1979–1985) phase. In the early
morning hours the correlation coefficient maximizes around the 08:00–10:00 h IST
interval. The flux independent nature of diurnal TEC is evident around the
noon time hours of only a few months in the descending phase for F10.7 values
greater than 150 unit. Variation of TEC for the whole time period (1979–1990)
also exhibits a prominent hysteresis effect. The remarkable feature of the hysteresis effect is
its local time dependence, leading to a temporal
flip-over. Solar flux-normalized TEC values show a clear seasonal dependence
with asymmetrical variations in the two equinoxes. The amplitudes of the
equinoctial peaks reveal a prominent local time dependence. A further
normalization leads to a typical local time variation of TEC. Based on solar
flux, seasonal and local time dependent features of TEC, an empirical formula
has been developed to represent the TEC variation in the early morning
hours. It yields a quantitative estimate of the solar flux dependent nature of
the TEC variation. The formula has been validated using the available TEC data and
data from the neural network.
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