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

Abstract

Abstract. This paper deals with two problems, methods of foF2 trend determination and origin of trends in foF2, both being controversial in current literature. We found that various regression-based methods and artificial neural networkbased method of Yue et al. (2006) provided comparable results within uncertainties caused mainly by various ways of removing/suppressing the dominant solar cycle effect. The role of geomagnetic activity in the observed trends in foF2 was probably substantial and might be still even rather dominant in the last quarter of the 20th century.

A long-term trend in the F2-layer critical frequency as observed at Alma-Ata ionosonde station

“…One exception to this conclusion is the somewhat higher foF2 trend (−0.0075 MHz y −1 ) than that found using the regression method and including an F10.7 correction (−0.0038 MHz y −1 ). Table 1 shows that the higher foF2 trend is close to those calculated by Danilov (2002Danilov ( , 2003, Lastovicka et al (2006Lastovicka et al ( , 2008b, Khaitov et al (2012), and Ghabahou et al (2013), whereas the weaker foF2 trend more closely matches that calculated by Mielich and Bremer (2013). Here, we can only note that twice removing the solar element of variations in foF2 (using the regression method and the 11-year running mean) provides a weaker foF2 trend than that obtained using only the 11-year smoothing.…”

Global pattern of trends in the upper atmosphere and ionosphere: Recent progress

“…A Chilean team analyzed data with regression-and wavelet-based methods and got quite different results of opposite sign. As shown by Laštovička et al (2008b), there was however an error in the wavelet-based method. Also Chinese neural network-based method (Yue et al, 2006) gave results comparable with regression-based results (Laštovička et al, 2008b).…”

“…However, practical application of such an approach has to be made with care, as the trend has a non-infinite period limited by the length of the dataset or the length of the interval of a stable linear trend existence. This is probably the reason why the trial of Chilean team to use in foF2 trend analysis such approach (Laštovička et al, 2006b) was unsuccessful (Laštovička et al, 2008b). …”

An attempt to study long-term variation of sporadic E layers using neural networks

“…NN has been used to solve many problems associated with ionosphere (Altiany et al, 1997;Williscroft and Poole, 1996). Recently, Yue et al (2006) and Laštovička et al (2008) successfully applied artificial neural network to derive long-term foF2 trends.…”