Semiannual and annual variations in the height of the ionospheric F2-peak

Rishbeth, H.; Sedgemore-Schulthess, K. J. F.; Ulich, Thomas

doi:10.1007/s005850050889

Cited by 7 publications

(13 citation statements)

References 4 publications

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“…At low latitudes, especially in the EIA area, f o F 2 in winter is seen to be larger than that in summer during the daytime, which is consistent with the time‐sequence development of the north‐south (summer‐winter) asymmetry denoted by Lin et al []. Meanwhile, h m F 2 ( M (3000) F 2 ) are quite high (very small) in June, considerable (large) in March and September, and low (great) in December, which presents the superposition of the annual and semiannual variations [ Millward et al , ; Zou et al , ; Rishbeth et al , ].…”

Section: Resultssupporting

confidence: 77%

Modeling Chinese ionospheric layer parameters based on EOF analysis

Wan

Xiong

et al. 2015

Space Weather

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Using 24-ionosonde observations in and around China during the 20th solar cycle, an assimilative model is constructed to map the ionospheric layer parameters (f o F 2 , h m F 2 , M(3000)F 2 , and f o E) over China based on empirical orthogonal function (EOF) analysis. First, we decompose the background maps from the International Reference Ionosphere model 2007 (IRI-07) into different EOF modes. The obtained EOF modes consist of two factors: the EOF patterns and the corresponding EOF amplitudes. These two factors individually reflect the spatial distributions (e.g., the latitudinal dependence such as the equatorial ionization anomaly structure and the longitude structure with east-west difference) and temporal variations on different time scales (e.g., solar cycle, annual, semiannual, and diurnal variations) of the layer parameters. Then, the EOF patterns and long-term observations of ionosondes are assimilated to get the observed EOF amplitudes, which are further used to construct the Chinese Ionospheric Maps (CIMs) of the layer parameters. In contrast with the IRI-07 model, the mapped CIMs successfully capture the inherent temporal and spatial variations of the ionospheric layer parameters. Finally, comparison of the modeled (EOF and IRI-07 model) and observed values reveals that the EOF model reproduces the observation with smaller root-mean-square errors and higher linear correlation coefficients. In addition, IRI discrepancy at the low latitude especially for f o F 2 is effectively removed by EOF model.

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Section: Resultssupporting

confidence: 77%

Modeling Chinese ionospheric layer parameters based on EOF analysis

Wan

Xiong

et al. 2015

Space Weather

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“…By far the dominant effect is the variability of solar activity, which has, on average, a period of 11 years. Thereafter, annual (seasonal) and semiannual cycles are seen in the time series [ Rishbeth et al , ]. The underlying trend, whether it is due to human‐caused factors or due to Sun‐Earth interaction, is of interest to us.…”

Section: Methodsmentioning

confidence: 99%

Time‐varying ionosonde trend: Case study of Sodankylä h_mF₂ data 1957–2014

2015

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We discuss trend analysis of nonstationary ionosonde hmF2 time series measured at Sodankylä Geophysical Observatory (67.4∘N, 26.7∘E), Finland, 1957–2014. We model the hmF2 with a dynamic regression time series model with the following components: a slowly varying background level, seasonal variations, and solar effects. We analyze the time series with a dynamic linear state space model. Such an approach allows model components to vary in time, allowing us to study the dynamic stochastic nature of the underlying long‐term trend. This feature is lacking in most time series models used in atmospheric and environmental long‐term trend analyses. Our objective is to understand the long‐term hmF2 trend with respect to increased levels of carbon dioxide and methane in the atmosphere. Based on model estimates, this phenomenon is predicted to cool the thermosphere and leads to decrease of the altitude of the so‐called F2 layer peak. After accounting for the effects of solar activity variations on the data, we see that the estimated trend shows an almost 30 km decrease of the F2 layer peak during the observation period. The decrease of the peak during 1990–2010 is significantly greater than during earlier observation period.

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“…In order to study foF2 annual and semi-annual variations in high solar activity year, we select the data in 1958, the highest solar activity year in current. Since we are interested in foF2 semi-annual and annual variations, we express foF2 at given local time as three terms, similar with Rishbeth et al (2000c) and Yu et al (2002), which represent average value, annual component and semi-annual component respectively.…”

Section: Analysis Methodsmentioning

confidence: 99%

Annual and Semi-annual Variations of the Observed foF2 in a High Solar Activity Year

Li¹,

Yu²

2003

Terr. Atmos. Ocean. Sci.

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By the Fourier series expanding method, the observed F2 layer critical frequencies (foF2) globally over 70 stations in a high solar activity year of 1958, are used to analyze the annual and semi-annual variations of foF2, and the world wide distribution features of their amplitude and phase in daytime and nighttime are studied in detail. The results for foF2 annual and semi-annual variation are summarized as follows. The midnight (2:00 LT) foF2 annual variations are noticeable in both hemispheres at mid-high latitudes, and their amplitudes are slightly larger in far pole regions than in near pole regions. Generally, at most stations, the midnight foF2 reach the maximum in summer, and no winter anomaly can be discerned. While in daytime (14:00 LT), there are pronounced annual variations with large amplitude in both hemispheres at mid-high latitudes. After carefully study ing their phases, we find that these annual variations usually peak in winter, which indicate all the variations are classic winter anomaly. However, the winter anomaly is very weak in the equatorial zone and not even perceiv able in South America. Moreover, the amplitude of daytime foF2 semi-an nual variation is generally small in near pole regions and large in far poles region of both hemispheres. Compared with their annual component, the semi-annual variations in the tropical region are significant. Their phase distributions reveal that the semi-annual variation usually peaks iil March and April. In order to explain the results mentioned above, we studied the atomid molecular ratio [0/N2] and confirmed that the noon foF2 annual variations prevailing in mid-high latitudes are caused largely by the annual variation of [0/N2]. As the noon foF2 semi-annual variations pronounced in far pole

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Semiannual and annual variations in the height of the ionospheric F2-peak

Cited by 7 publications

References 4 publications

Modeling Chinese ionospheric layer parameters based on EOF analysis

Modeling Chinese ionospheric layer parameters based on EOF analysis

Time‐varying ionosonde trend: Case study of Sodankylä h_mF₂ data 1957–2014

Annual and Semi-annual Variations of the Observed foF2 in a High Solar Activity Year

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Semiannual and annual variations in the height of the ionospheric F2-peak

Cited by 7 publications

References 4 publications

Modeling Chinese ionospheric layer parameters based on EOF analysis

Modeling Chinese ionospheric layer parameters based on EOF analysis

Time‐varying ionosonde trend: Case study of Sodankylä hmF2 data 1957–2014

Annual and Semi-annual Variations of the Observed foF2 in a High Solar Activity Year

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Time‐varying ionosonde trend: Case study of Sodankylä h_mF₂ data 1957–2014