Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations

Details Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations

Oct 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006jgra..11110303y&link_type=abstract

Journal of Geophysical Research, Volume 111, Issue A10, CiteID A10303

Other

11

Ionosphere: Ionospheric Dynamics, Ionosphere: Ionosphere/Atmosphere Interactions (0335), Ionosphere: General Or Miscellaneous, Space Weather: Solar Effects

Scientific paper

An artificial neural network (ANN) method is first used for deriving long-term trends of the F2-layer critical frequency (foF2) at 19 ionospheric stations in the Asia/Pacific sector. It is found that the ANN method can eliminate the geomagnetic activity effect on foF2 more effectively than usual regression methods. Of the selected 19 stations, there are significant long-term trends corresponding to a confidence level >=90% at 14 stations and 12 of these stations present negative trends. An average trend of -0.05% per year in the selected area can be obtained if the 12 stations with significant negative long-term trends be considered. No pronounced diurnal and latitudinal effects in trends and no uniform pattern of seasonal variation in most stations are detected. The long-term trends for low latitude and equatorial stations differ from other stations suggest that some special dynamical processes may take effects in the equatorial anomaly region. Many factors which can influence ionosphere, such as the greenhouse effect, solar and geomagnetic activity, and neutral background gas, might contribute to the trend.

Affiliated with

Also associated with

No associations

Rating

Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Applying artificial neural network to derive long-term foF2 trends in the Asia/Pacific sector from ionosonde observations will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-908084