On the day-to-day variation of foF2 and M(3000)F2

Kouris, S.S.; Fotiadis, D.N.; Xenos, Thomas D.

doi:10.1016/s0273-1177(98)00116-1

Cited by 36 publications

(51 citation statements)

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“…This could be related to an enhancement in the foF2 variability during the spring and autumn months. (Kouris et al, 1998(Kouris et al, , 1999.…”

Section: Resultsmentioning

confidence: 99%

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Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics

Xenos

2002

Nonlin. Processes Geophys.

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Abstract. In this work, Neural-Network-based single-station hourly daily foF2 and M(3000)F2 modelling of 15 European ionospheric stations is investigated. The data used are neural networks and hourly daily values from the period 1964-1988 for training the neural networks and from the period 1989-1994 for checking the prediction accuracy. Two types of models are presented for the F2-layer critical frequency prediction and two for the propagation factor M(3000)F2. The first foF2 model employs the E-layer local noon calculated daily critical frequency (foE 12 ) and the local noon F2-layer critical frequency of the previous day. The second foF2 model, which introduces a new regional mapping technique, employs the Juliusruh neural network model and uses the Elayer local noon calculated daily critical frequency (foE 12 ), and the previous day F2-layer critical frequency measured at Juliusruh at noon. The first M(3000)F2 model employs the E-layer local noon calculated daily critical frequency (foE 12 ), its ±3 h deviations and the local noon cosine of the solar zenith angle (cos χ 12 ). The second model, which introduces a new M(3000)F2 mapping technique, employs Juliusruh neural network model and uses the E-layer local noon calculated daily critical frequency (foE 12 ), and the previous day F2-layer critical frequency measured at Juliusruh at noon.

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“…This could be related to an enhancement in the foF2 variability during the spring and autumn months. (Kouris et al, 1998(Kouris et al, , 1999.…”

Section: Resultsmentioning

confidence: 99%

“…This could be due to the fact that the variability of M(3000)F2 is much lower than that of foF2. (Kouris et al, 1998(Kouris et al, , 1999 The performance of the M(3000)F2 mapping models are presented in Fig. 6.…”

Section: Resultsmentioning

confidence: 99%

Neural-network-based prediction techniques for single station modeling and regional mapping of the foF2 and M(3000)F2 ionospheric characteristics

Xenos

2002

Nonlin. Processes Geophys.

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“…Geophysicae 16, 1039 -1042(1998) © EGS -Springer-Verlag 1998 Letter to the Editor Abstract. Daily values of the ionospheric characteristics fo F2 and M(3000)F2 for a given hour and month are correlated with the corresponding daily values of sunspot number using measured data collected at seven European locations.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, the diurnal and seasonal trends in the correlation coefficients are found to be in the same sense whether dealing with daily or monthly median values, and this fact prompts us to attribute the observed differences to the influences of other effects than ionisation production in dominating the day-to-day variability of these two ionospheric characteristics. For example, at middle latitudes ionospheric disturbances can depress or enhance foF2 by more than 50% in some particular days (Kouris et al, 1998) such that a multiple regression of two variables, for instance R and the geomagnetic Ap index as advocated for the monthly median values by Kane (1992), might substantially improve the daily correlation. If so, this suggests that formulations based also on magnetic activity could be valuable in improving shortterm ionospheric forecasts.…”

Section: Introductionmentioning

confidence: 99%

Letter to the Editor: Solar-cycle variation of the daily foF2 and M(3000)F2

1998

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solar or ionospheric indices. One step towards optimising that adjustment is to examine the extent to which daily values depend on daily solar activity. The present investigations therefore consider the correlation with daily sunspot number R, whereas monthly median values are known to correlate best with 12-month running mean sunspot number R 12 . Data and analysesIn particular, Student's t-test and Fisher's F-test have been applied to determine the significance of the higher-order polynomial coefficients and at what points truncation should be introduced. ResultsFor foF2 these tests show that a 3 is significant at the 95% level in about 40% of the regressions examined, being negative in winter and the equinoxes, and positive in summer. Moreover, this same general pattern of variation is experienced at all stations considered. As an example, Table 1 for Slough shows the months and hours for which the term a 3 is significant and for these whether it is positive or negative. Retention of a 2 results in substantial improvement, being significantly different from zero in 85% of cases (Table 2). a 2 is negative in all seasons and exhibits this same pattern at all stations, i.e. tending towards saturation at the higher R. By contrast, in the case of M(3000)F2, b 3Ann. Geophysicae 16, 1039 -1042(1998) © EGS -Springer-Verlag 1998 Letter to the Editor Abstract. Daily values of the ionospheric characteristics fo F2 and M(3000)F2 for a given hour and month are correlated with the corresponding daily values of sunspot number using measured data collected at seven European locations. The significance of applying different-order polynomials is considered and the times are confirmed when the higher-order terms are important. Mean correlation coefficients for combined data sets over all hours, months and stations are determined, together with the standard errors of estimates. Comparisons are made with corresponding figures for monthly median values derived from the same data sets.

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“…Earlier catalog of disturbed periods has been based on the selection of 3 h or longer for the mean relative deviation of NmF2 greater than ±40% which corresponds to the requirement for foF2 deviation to exceed the monthly median by ±20% (Kouris et al, 1998). Such an analysis could be performed on a monthly basis upon the end of each month when the monthly median could be obtained.…”

Section: Ionospheric Disturbed Periodsmentioning

confidence: 99%

Ionospheric weather: cloning missed foF2 observations for derivation of variability index

2008

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Abstract.A techique for filling the gaps of the missing F2-layer critical frequency is proposed and applied for the derivation of the ionospheric weather index, characterizing the degree of disturbance at each particular station. A dailyhourly analysis of ionosonde observations of foF2 for 16 stations at latitude range 37 • to 70 • N, longitudes of 10 • W to 150 • E, is performed during the solar minimum, 2006. Missed ionosonde observations are reconstructed by cloning data of another station. The process of gap filling considers hourly values of the F peak density NmF2 (deduced from foF2), normalized to the respective median, and assumes that this ratio remains the same for the parent and cloned data. It is shown that the correlation coefficient between cloned fcF2 and observed foF2 is greater than 0.75 for the positive and negative ionospheric disturbed days during a year at solar minimum, independent of the distance between the stations in high and middle latitudes. The quiet reference is determined as a running daily-hourly median for 27 days, preceding the day of observation calibrated for a seasonal trend with ITU-R foF2 predictions. The hourly deviation DNmF2 is defined as the logarithm of ratio of NmF2/NmF2med. A segmented logarithmic scale of the ionospheric weather index, W , is introduced, so that W =±1 refers to the quiet state, W =±2 to a moderate disturbance, W =±3 to the ionospheric storm, and W =±4 to the extreme or anomalous conditions. The catalog of the ionospheric disturbances for W exceeding ±2 at least during 3 consecutive hours is produced and presented online at the SRC and IZMIRAN web pages. It is found that the moderate disturbance is a prevailing state of the ionospheric weather for all stations. The stormy conditions comprise 1 to 20% of the times which occur more frequently at high latitudes, by night, during equinox and winter.

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On the day-to-day variation of foF2 and M(3000)F2

Cited by 36 publications

References 1 publication

Neural-network-based prediction techniques for single station modeling and regional mapping of the <I>fo</I>F2 and M(3000)F2 ionospheric characteristics

Neural-network-based prediction techniques for single station modeling and regional mapping of the <I>fo</I>F2 and M(3000)F2 ionospheric characteristics

<i>Letter to the Editor:</i> Solar-cycle variation of the daily <i>fo</i>F2 and M(3000)F2

Ionospheric weather: cloning missed foF2 observations for derivation of variability index

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On the day-to-day variation of foF2 and M(3000)F2

Cited by 36 publications

References 1 publication

Neural-network-based prediction techniques for single station modeling and regional mapping of the &lt;I&gt;fo&lt;/I&gt;F2 and M(3000)F2 ionospheric characteristics

Neural-network-based prediction techniques for single station modeling and regional mapping of the &lt;I&gt;fo&lt;/I&gt;F2 and M(3000)F2 ionospheric characteristics

&lt;i&gt;Letter to the Editor:&lt;/i&gt; Solar-cycle variation of the daily &lt;i&gt;fo&lt;/i&gt;F2 and M(3000)F2

Ionospheric weather: cloning missed foF2 observations for derivation of variability index

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Neural-network-based prediction techniques for single station modeling and regional mapping of the <I>fo</I>F2 and M(3000)F2 ionospheric characteristics

Neural-network-based prediction techniques for single station modeling and regional mapping of the <I>fo</I>F2 and M(3000)F2 ionospheric characteristics

<i>Letter to the Editor:</i> Solar-cycle variation of the daily <i>fo</i>F2 and M(3000)F2