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

Abstract

[1] 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 À0.05% 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.

Long-term trends in <I>fo</I>F2: their estimating and origin

“…Laštovička (jla@ufa.cas.cz) of the F2 layer. Unfortunately, the results of different authors differ substantially, even as to sign and possible origin of long-term change in foF2 (see, e.g., brief review in Laštovička et al, 2006b;Yue et al, 2006). The determination and/or estimation of long-term trends in foF2 are difficult due to the overlapping effect of the 11-year solar cycle, which is many times larger than the effect of long-term trends over the 11 year period.…”

“…The results obtained by applying the Yue et al (2006) artificial neural network-based approach with their way of correcting for solar and geomagnetic activity influences to these test data are shown below. Six combinations of daily values of R (sunspot number), F10.7, E10.7 and geomagnetic activity index A p are used in order to remove and/or suppress the solar cycle and geomagnetic influences on trend determination: (1) F10.7 and A p ; (2) R and A p ; (3) E10.7 and A p ; (4) F10.7 and 11-year running mean A p ; (5) R and 11-year running mean A p ; (6) E10.7 and 11-year running mean A p .…”

“…Two solar cycles with good quality data enable determine trend in yearly values, but such interval is too short to determine reliably seasonal (month-tomonth) variation of trends. Yue et al (2006) developed another promising method of trend determination based on artificial neural network and applied it successfully to data from the Asia/Pacific sector. The main purpose of our short paper is to apply Yue et al (2006) method to the test Juliusruh data set to help to clarify differences between different methods and reliability of different methods and to test if the application of this modern statistical method leads to changes in trend results.…”

“…Yue et al (2006) developed another promising method of trend determination based on artificial neural network and applied it successfully to data from the Asia/Pacific sector. The main purpose of our short paper is to apply Yue et al (2006) method to the test Juliusruh data set to help to clarify differences between different methods and reliability of different methods and to test if the application of this modern statistical method leads to changes in trend results. Another purpose of the paper is to contribute to the determination of the role of greenhouse gases on long-term trends in foF2.…”

Different long-term trends of the oxygen red 630.0 nm line nightglow intensity as the result of lowering the ionosphere F2 layer

“…Long-term variations in the upper atmosphere/ionosphere parameters may be related to environmental (anthropogenic/natural) changes in the troposphere (Roble and Dickinson, 1989;Rishbeth, 1990;Rish-Correspondence to: G. G. Didebulidze (didebulidze@genao.org) beth and Roble, 1992;Bremer, 1998;Danilov and Mikhailov, 1999;Bremer and Berger, 2002;Bencze, 2005;Yue et al, 2006). The main cause of global warming in the troposphere is thought to be an increase in the density of the greenhouse gases (Roble and Dickinson, 1989;Rishbeth, 1990;Rishbeth and Roble, 1992).…”