foF2 vs solar indices for the Rome station: Looking for the best general relation which is able to describe the anomalous minimum between cycles 23 and 24

Perna, Luigi; Pezzopane, Michael

doi:10.1016/j.jastp.2016.08.003

Cited by 44 publications

(26 citation statements)

References 91 publications

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“…Observations during the extended solar minimum of 2008-2009 showed complex behavior as they disagreed with the previously established understanding that vertical E × B drifts have solar activity dependance (e.g., Fejer et al, 1991;Richmond, 1973) over the equatorial latitudes. Figure 3a to 2010, which may be directly related to the complexity of the ionospheric variability during this extended solar minimum period (Chen et al, 2011;Ezquer et al, 2014;Perna & Pezzopane, 2016;Solomon et al, 2013). In Figure 3b ISR vertical E × B drift changes are shown at 1200 LT during 2008-2014.…”

Section: Relationship Between C/nofs Vertical Ion Plasma Drift and Mamentioning

confidence: 93%

“…Figure a shows the JULIA vertical E × B drift variability from 2001 to 2015 at 1200 LT. The solar flux F10.7 is superimposed on Figure a and generally shows little correlation from 2008 to 2010, which may be directly related to the complexity of the ionospheric variability during this extended solar minimum period (Chen et al, ; Ezquer et al, ; Perna & Pezzopane, ; Solomon et al, ). In Figure b ISR vertical E × B drift changes are shown at 1200 LT during 2008–2014.…”

Section: Relationship Between C/nofs Vertical Ion Plasma Drift and Mamentioning

confidence: 99%

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Long‐Term Estimation of Diurnal Vertical E × B Drift Velocities Using C/NOFS and Ground‐Based Magnetometer Observations

et al. 2018

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We report on the development of a new mathematical expression to estimate local daytime (0700–1700 LT) vertical E × B drift in low latitudes using a combination of ground‐based magnetometer measurements and Communications and Navigation Outage Forecasting System (C/NOFS) satellite observations. The expression was developed over Jicamarca (11.8°S, 77.2°W; 0.8°N geomagnetic) and validated with Jicamarca Unattended Long‐Term studies of the Ionosphere and Atmosphere (JULIA) mode and incoherent scatter radar (ISR) measurements during the period 2008–2014. The obtained correlation coefficient (R) values computed using observed and derived vertical E × B drift velocities are 0.79 and 0.84 for ISR and JULIA, respectively when data are available during 2008–2014. Storm‐time comparison between observed and derived vertical E × B drift velocities agreed well with R of 0.92 and 0.87 during 5–8 August 2011 and 8–11 March 2012 geomagnetic storm periods for ISR and JULIA observations, respectively. Overall, we found that the developed expression is applicable in estimating vertical E × B drift response during quiet and geomagnetic storm periods. Based on these findings, we suggest that it is possible to develop accurate daytime global vertical E × B drift model over the equatorial latitude regions using inexpensive magnetometer observations and available satellite data.

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Section: Relationship Between C/nofs Vertical Ion Plasma Drift and Mamentioning

confidence: 93%

Section: Relationship Between C/nofs Vertical Ion Plasma Drift and Mamentioning

confidence: 99%

Long‐Term Estimation of Diurnal Vertical E × B Drift Velocities Using C/NOFS and Ground‐Based Magnetometer Observations

et al. 2018

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“…The removal of magnetic storms seems to reduce the influence of negative ionospheric storms (hence reduce negative and increase positive hysteresis), in consistence with the geomagnetic explanation; but the positive hysteresis at low latitudes remains unexplained and needs other mechanisms functioning at quiet time. In particular, the F 10.7 index has been found to show a kind of saturation for very low solar activity (Araujo-Pradere et al, 2011; Chen et al, 2011;Perna & Pezzopane, 2016). In particular, the F 10.7 index has been found to show a kind of saturation for very low solar activity (Araujo-Pradere et al, 2011; Chen et al, 2011;Perna & Pezzopane, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Many insights have been gained based on this kind of methods on the topics of both hysteresis (e.g., Elias, 2014;Kane, 1992;Ozguc et al, 2008;Perna & Pezzopane, 2016) and long-term trends (e.g., Laštovička et al, 2016;Pham Thi Thu et al, 2016). In previous studies, yearly mean (or running) average methods are usually employed to smooth out the short-term, cyclic variations.…”

Section: Introductionmentioning

confidence: 99%

The Use of Monthly Mean Average for Investigating the Presence of Hysteresis and Long‐Term Trends in Ionospheric NmF2

et al. 2020

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Yearly averaging is a basic preprocessing of ionospheric data to smooth out the short-term, cyclic variations in the observations. However, we find that the use of yearly (running) average method probably leads to a biased evaluated relation between the ionospheric parameters (e.g., F2 peak electron density, NmF2) and the solar extreme ultraviolet (EUV) irradiance. The bias is essentially related to the annual/seasonal variations of the ionosphere, which means that the ionospheric sensitivity to the solar EUV irradiance varies with season and the same solar input can produce different ionization levels in different months/seasons. The yearly method simply averages the observations of different seasons, leading to a biased yearly NmF2-EUV relation, which will further affect the estimation of the ionospheric hysteresis (different NmF2-EUV relation in the solar rising and declining phase). Also, the extraction of long-term trends in the ionosphere will be more uncertain if using a regression approach based on the biased NmF2-EUV relation. In conclusion, we suggest to do the analysis month by month (using monthly average method) to properly deal with the annual/seasonal variations of the ionosphere, and to obtain accurate estimation of the hysteresis and long-term trends. Though both the yearly and monthly methods were used in precedent works, the bias introduced by the yearly average method has not been fully recognized prior to this study.

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“…It was realized by Mayaud () that during most magnetically quiet conditions, CEJ events were from minimum solstice months. Our period of study included one of the anomalous and prolonged solar minimum conditions that were extremely quiet (e.g., Ezquer et al, ; Perna & Pezzopane, ), and as a result the variability analyses relating ionospheric and solar activity behavior were very complicated (e.g., Chen et al, ; Solomon et al, ). Geomagnetic field data recorded in 2009 (mean annual sunpot number R z =3.1) over South American, Philippine, and African sectors were used by Rabiu et al () to investigate longitudinal variation of EEJ and CEJ during quiet conditions ( K p ≤3).…”

Section: Methodsmentioning

confidence: 99%

An Empirical Model of Vertical Plasma Drift Over the African Sector

Dubazane

Habarulema

2018

Space Weather

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Daytime vertical E × B drift can be derived from difference of H‐component magnetic field measurements (ΔH) using a pair of low‐latitude and equatorial latitude magnetometer stations. Knowledge of E × B drift is of utmost importance in space weather‐related predictions since it can significantly affect ionospheric density structures and dynamics. For the first time, we developed a quantitative relationship between equatorial electrojet (ΔH) and vertical E × B drift over the African region using magnetometer data and E × B drift observations from the Communication and Navigation Outage Forecasting System (C/NOFS) satellite during local daytime (0700–1800 LT) from 2008 to 2013 when magnetometer data were available. Additionally, we have constructed vertical E × B drift models over the African sector based on C/NOFS vertical E × B drift data and relevant physical and geophysical inputs. Validating using data not included in model development, our model estimated C/NOFS vertical E × B drift with correlation coefficient values of 0.85 and 0.56 during quiet and disturbed conditions, respectively. The daily pattern of the E × B drift velocity is consistent between the developed model based on C/NOFS data and the climatological Scherliess‐Fejer model. However, the Scherliess‐Fejer model generally overestimated C/NOFS vertical E × B drift (in most cases) with a correlation coefficient of 0.54 during quiet conditions.

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foF2 vs solar indices for the Rome station: Looking for the best general relation which is able to describe the anomalous minimum between cycles 23 and 24

Cited by 44 publications

References 91 publications

Long‐Term Estimation of Diurnal Vertical E × B Drift Velocities Using C/NOFS and Ground‐Based Magnetometer Observations

Long‐Term Estimation of Diurnal Vertical E × B Drift Velocities Using C/NOFS and Ground‐Based Magnetometer Observations

The Use of Monthly Mean Average for Investigating the Presence of Hysteresis and Long‐Term Trends in Ionospheric NmF2

An Empirical Model of Vertical Plasma Drift Over the African Sector

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