Physical and chemical processes in the ionosphere are driven by
complex interactions with the solar radiation. The ionospheric plasma is in
particular sensitive to solar EUV and UV variations with a time delay between
one and two days. This delay is assumed to be related to thermospheric
transport processes from the lower ionosphere to the F region. In previous
analyses, the delay has been investigated using the F10.7 index. Here we
present preliminary results of the ionospheric delay based on a comprehensive
and reliable database consisting of GNSS TEC Maps and EUV spectral flux data.
We plan to specify the various dependencies from geographic/geomagnetic
location, altitude, season, local time, geophysical and solar radiation
conditions such as the solar activity level. The first results for
dependencies from seasons and wavelengths regions of the EUV are presented in
this paper. These results can provide more insight into ionospheric processes
and are of interest for applications dependent on reliable ionospheric
weather forecasts, e.g. GNSS error analyses, prediction and mitigation.
Introduction
The high variability of the ionospheric plasma has a strong
influence on the radio signal propagation and therefore an impact e.g. on all
applications based on Global Navigation Satellite Systems (GNSS), like
navigation support, traffic guidance systems, land survey and communication
services. Therefore, understanding the processes of the solar-ionosphere
interaction, as a crucial source of the high dynamics of the ionosphere, is
not only of interest for scientists, but also for engineers and service
operators. The ionospheric ionization depends on the solar radiation
intensity in combination with the recombination rate and transport processes
due to diurnal, seasonal and geographic variations. The solar EUV radiation
dominates the photoionization in the ionosphere and causes different
variations like the 27-day solar rotation cycle or seasonal changes. The
impact of the solar EUV radiation depends on the wavelength together with the
absorption and ionization cross-sections of the varying particle populations
at different heights, which cause a chain of reactions on different time
scales with impact on the plasma structure of the ionosphere.
The different ionospheric layers are characterized by the density
distribution of the different atom and molecule species. The understanding of
the influence of solar radiation on the ionosphere and a potential delay in
plasma production is essential for the development of realistic ionospheric
models, having the potential to allow a precise prediction of the ionosphere
with high spatial and temporal resolution. In the past investigations of the
ionospheric delay were frequently based on the F10.7 index as proxy for the
EUV variability . The observational results were further
investigated by modeling the thermospheric response to the solar radiation
variation using a one-dimensional numerical model between 100 and 250 km
height. The simulation revealed a delayed density variation of atomic oxygen
of about two days at 180 km height due to photodissociation.
have introduced several simplifying assumptions such as
a fixed thermosphere temperature profile, limitation to O and O2
thermospheric constituents and a fixed downward flow of atomic oxygen at the
lower boundary. Here it is expected that thermosphere-ionosphere coupling
plays a significant role for the delayed response of the ionospheric
ionization.
The reason why F10.7 has been used as a proxy in the earlier analysis was due
to a lack of direct EUV measurement in the relevant spectrum range. A delay
between one and two days was confirmed by others using F10.7 and EUV proxies
to describe the solar radiation correlation with the total electron content
(TEC) . The delay was also measured by using
different indices for the solar radiation. Studies by
have indicated that the delay appears even in the plasmaspheric electron
density derived from cross-spectral analyses of ULF wave measurements
recorded at ground magnetometer stations. Thus, the delayed response of
ionosphere/plasmasphere ionization to mid-term solar irradiance changes
(variation within days, e.g. induced by the solar rotation) has a fundamental
character which requires more exploration. In order to analyse the radiation
effect on the ionospheric plasma, the TEC can be used as indicator for the
ionization of the F region and ionosphere in general. Variations in the EUV
radiation (like the 27-day cycle or seasonal changes) cause variations of
TEC. To improve future ionosphere modelling a more detailed explanation of
the delay with higher temporal and spectral resolution of the data and also
consideration of different locations is needed.
In this paper we will give more insight by comparing the ionospheric delay to
ionization, photodissociation and recombination processes respectively, as
well as short term and long term seasonal and solar changes.
Data
Nowadays, data of the solar spectrum in the EUV wavelength are available for
more than one decade from the Solar EUV Experiment (SEE) onboard the
Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite
, and from the Solar Dynamics Observatory (SDO)
experiment. The SDO is a mission launched by the NASA in
2010 that has several instruments onboard: Extreme Ultraviolet Variability
Experiment (EVE), Helioseismic and Magnetic Imager (HMI) and Atmospheric
Imaging Assembly (AIA). The EVE instrument measures the solar EUV radiation
in a wavelength region from 0.1 to 105 nm with a spectral resolution of
0.1 nm; it has a temporal resolution of 20 s and achieves an accuracy of
25 % with the inflight calibration . A high temporal
resolution is available for the Geostationary Operational Environmental
Satellites (GOES) data , but these are only available for
several data bands in the EUV range. The available wavelength regions are
from 5 to 15 nm, 25 to 35 nm and 115 to 130 nm. The GOES data have a
temporal resolution of 1 min . A specific experiment to
measure EUV spectral solar radiance calibrated in flight is Solar
Auto-Calibrating EUV/UV Spectrophotometers (SolACES)
as a part of the ESA
SOLAR ISS mission. Generally, the time resolution of the available data is
1 day, but due to ISS maneuvers there are repeated gaps. Therefore, SolACES
data are not useful for time series analyses, but can be applied to calibrate
regular observations from TIMED/SEE or SDO/EVE.
The SDO/EVE and GOES data are best suited for the analysis in this paper,
because of the high temporal and spectral resolution and length of the
missions. The integrated EUV of a wavelength region in EVE and the
corresponding GOES band show a good correlation, as can be seen in
Fig. . The other EUV data are not considered because of the
mentioned deficiencies.
Comparison of GOES A and integrated EVE data for the wavelengths
from 9 to 20 nm.
In the analysis, the EUV data will be compared with TEC values extracted from
TEC maps provided by the International GNSS Service (IGS) TEC maps
. TEC is the preferred parameter to
investigate the solar dependence of ionospheric ionization, because TEC is an
integral measurement of the electron density and therefore not as sensitive
to vertical redistribution of plasma as for example the peak electron
density. Furthermore, the good coverage and 24/7 operationality of ground
based GNSS reference stations used for TEC calculation allow a permanent,
high resolution and global access to the TEC parameter. The correlation with
the solar EUV radiation can be calculated at single locations or regions. The
time periods of the analysis are chosen to avoid big data gaps and to
guarantee a minimal impact of small remaining gaps on the calculation of the
delay.
Correlation between F10.7 and TEC
The delay between one to two days of solar EUV radiation and ionospheric
parameters has been shown with the solar index F10.7 by .
and confirmed the delay with the
EUV-TEC, which is calculated from satellite-born EUV measurements and
represents the ionospheric variability better than the conventional F10.7
does. Since EUV-TEC does not account certain effects (e.g. secondary
ionization), the same delay was also calculated with EVE fluxes by
.
In Fig. we could reproduce the results via the calculation of the
cross-correlation between F10.7 and TEC. In the left plot in Fig.
the normalized (feature scaling) F10.7 and TEC data are shown. In the
calculation of the TEC map correlation data from the grid point
50∘ N and 10∘ E are used, since this position is in a
region with a high number of ground stations and thus the data strongly rely
on real measurements. In addition the TEC data have been resampled to a
resolution of 1 day because the F10.7 data are not available in a higher
resolution. The resulting cross-correlation of F10.7 and TEC is shown in the
middle plot of Fig. . In the right plot of Fig. the peak
of the correlation is given, indicating a delay of about one day between
F10.7 and TEC.
Comparison of normalized F10.7 and TEC (for grid point
50∘ N, 10∘ E), cross-correlation of F10.7 and TEC, peak of
the cross-correlation of F10.7 and TEC. The middle plot shows, that the peak
and delay can be estimated from the entire cross-correlation for the given
time period and resolution without making assumptions about an expected
value.
Further calculations show that the estimated delay varies between one and two
days for the analyzed years (2011: 1 day, 2012: 1 day, 2013: 1 day, 2014:
2 days and 2015: 2 days). Due to the daily resolution of F10.7 the
calculation of the delay for different regions does not show any significant
variations and a more precise calculation of the delay is not possible here.
Therefore we apply in the next section the same correlation process to the
higher temporal resolution of the nowadays available EUV data.
Correlation between EUV and TEC
For the calculation of the cross-correlation between EUV and TEC also the TEC
data used were adjusted to a temporal resolution of 1 h. A band-stop filter
using fast Fourier transform from 23 to 25 h is applied to remove the daily
variability. The EVE data are integrated over 3 to 100 nm to get a single
data series representing the EUV radiation in the correlation.
Comparison of normalized integrated EUV (EVE from 3 to 100 nm) and
TEC (for grid point 50∘ N, 10∘ E) for summer,
cross-correlation of integrated EUV and TEC, peak of the cross-correlation of
integrated EUV and TEC. The middle plot shows, that the peak and delay can be
estimated from the entire cross-correlation for the given time period and
resolution without making assumptions about an expected value.
Figure shows the comparison and cross-correlation for the
integrated EUV and the TEC values from the grid point 50∘ N and
10∘ E. The left plot in Fig. shows a delay of about 16
hours for a chosen time period during the summer season. A cross-correlation
for a shorter time period is compared to the earlier results, because there
are no gaps in the data and therefore no interpolations had to be applied.
For a rough comparison of the correlations between F10.7 and TEC and also EUV
and TEC this approach is sufficient. The calculated delay is in agreement
with the rough delay of one day estimated with the F10.7 index around this
time period. Therefore it is possible to reproduce earlier results with much
higher precision by using recent EUV measurements. The variation of the delay
between one and two days estimated with the F10.7 index and a similar
behavior for the estimation with EUV, as shown in Fig. , is
discussed in the further analysis.
Running correlation coefficients (window of 90 days) of EUV (GOES E
from 115 to 130 nm) and TEC (for grid point 50∘ N, 10∘ E).
The red line and grey shading are the mean and standard deviation for each
month of the year. Summer and winter months are shaded in yellow and blue.
Delay of EUV (GOES E from 115 to 130 nm) and TEC (for grid point
50∘ N, 10∘ E). The blue dots represent delays which are
related to negative correlations. Summer and winter months are shaded in
yellow and blue.
Analysis of ionospheric delay
In order to analyse a possible seasonal effect on the delay, which might be
caused by a seasonal dependence of ionospheric dynamics and
photodissociation, the analysis needs to be further improved and consider
more than a single time period. Figures and show a time
series of the correlation coefficient and the delay for a period of 5 years,
where each point is calculated for a time window of 90 days. This time window
is applied, because it is long enough to result in reliable
cross-correlations for the delay estimation and because it is short enough to
allow capturing changes in the delay over time. Here we used EUV data from
GOES E for the comparison with TEC data at the grid point 50∘ N and
10∘ E because they cover the longest time period of continuous EUV
data measurement.
Super epoch analysis of the correlation coefficient and delay from
2011 to 2015 with EUV (GOES E from 115 to 130 nm) and TEC (for grid point
50∘ N, 10∘ E). In the upper plot the black line and grey
shading are the mean and standard deviation of the correlation coeffecient
for each day. In the lower plot the black dots and grey shading are the mean
and standard deviation of the delay for each day; the red line is the mean of
the delay for each month.
The correlation of EUV and TEC in Fig. shows an increase in summer
and a decrease in winter, although the correlation is slightly different in
each summer and winter. The annual difference might be caused by changes in
the EUV radiation due to the solar cycle or peculiarities of the
thermospheric dynamics in different years. In some winter periods the
correlation reaches negative values and the corresponding delays have been
highlighted in Fig. in order to show that the delay's deviation is
increased. The delay itself, as visible in Fig. , is grouped in two
peaks below 24 and 48 h. This clustering is caused by the one day variation
of the TEC data, which induces a similar variation in the cross-correlation
results. By comparing both groups we find that the values around 48 h have
many gaps and belong mostly to a negative correlation. In addition the number
of cross-correlation maxima around 24 h outnumbers the results around 48 h
by a factor of 3. We believe that the second band at 48 h is caused by the
maximum of the cross-correlation which sometimes originates from a pronounced
second peak, which cannot be removed completely with the above mentioned
band-stop filter. Therefore, we expect that values which belong in the group
around 24 h represent the actual delay and we disregard all other values in
the further analysis.
As can be seen in Fig. , the delay decreases for a few hours in
winter and increases again during summer. There also seems to be an overall
trend which lets the delay decrease slightly in the middle of the chosen time
period. To allow a better comparison of the seasonal variation, the results
of Fig. are used to calculate the super epoch plots in
Fig. , which shows the mean trend of the correlation and delay
through the seasons.
Correlation and delay of EUV (EVE) and TEC (for grid point
50∘ N, 10∘ E) for a summer and winter period. Red dots are
delays which are related to negative correlations. Green line: Start of
N2 ionization, cyan line: start of O ionization, blue line: start
of O2 ionization.
Comparing the seasonal values in the years from 2011 to 2015 the correlation
in the upper plot of Fig. reaches its maximum in the month June
and have their minimum in February. The mean value of the correlation varies
from ≈-0.05 in Winter to ≈0.35 in Summer. The increase and
decrease of the delay (Fig. lower plot) seems to be slower with a
maximum shifted to October and a minimum shifted from January to February.
This could be an indication that atmospheric processes might have an
additional influence on the ionospheric delay. The mean value of the delay
varies between 17 to 18.5 h for winter and summer respectivly. The slower
and delayed increase of the delay shown in the lower plot of Fig.
compared to the correlation coefficient in the upper plot of Fig.
indicates that there are processes involved which are not directly related to
the EUV radiation. These effects (e.g. transport processes or coupling with
the lower atmosphere) must be stronger in winter and cause the decreased
correlation between EUV and TEC. We expect that seasonal changes of
thermospheric winds might be a candidate of such additional process
responsible for the smaller seasonal variations in the delay.
To further analyse the variation between different EUV spectral bands we
compare the whole wavelength spectrum available from EVE data in
Fig. , instead of using the integrated EUV data.
For a detailed view of the spectral bands the ionization potentials of
N2, O and O2 are added in Fig. , which absorb most
of the EUV radiation at different spectral bands in the F region of the
ionosphere. A detailed view of the ionization cross sections is shown in
Fig. . The correlation and delay are again much stronger in summer
than in winter for every investigated wavelength. There is also a profile of
the correlation and delay that is similar in every of the chosen time periods
with a maximum at ≈62 nm. The wavelengths at which the different
ionizations begin do not show any noticeable variations. Therefore the delay
to 24 h is similar for most wavelengths, but has a slight increase around
the above-mentioned maximum. The contribution of the ionizations of each
species to the delay cannot be estimated with the given results because most
of the EUV variability at different wavelengths is correlated. This causes a
very similar behaviour for the whole EUV spectrum.
Ionization cross sections of N2, O and O2. Green
line: start of N2 ionization, cyan line: start of O ionization, blue
line: start of O2 ionization (Fennelly et al., 1992).
As seen in Fig. , the ionization profiles of N2, O and
O2 have their maximums similar to the trend of the delay through the
wavelength spectrum, but no reliable correlation can be estimated with the
given data.
Results and conclusion
With the analysis of EUV and TEC data the earlier results from
or were confirmed and calculated with
more precision. The calculated delay is about ≈17 h on an average
during a year. The difference to 24 h indicates that besides the seasonal
and daily variation other effects in the ionosphere contribute to the delay.
In the overall trend of the delay in Fig. a small decrease in the
middle of the five years can be seen. This might be the influence of the
solar cycle which shows the same trend for this time period. Similar to the
seasonal variation an increase of the EUV radiation and therefore of the
ionization could cause a stronger correlation and delay. Further analysis for
longer time periods that include solar maximums and minimums are necessary to
show if such an effect exist. The higher resolution of the delay calculation
also showed the seasonal variation of the correlation and delay. The seasonal
variation was calculated using GOES and SDO/EVE data. The decrease of the
correlation in winter shows that the influence of the ionization is slightly
reduced, which allows other effects to have a visible impact on the delay,
causing the resulting seasonal variation in the delay. The shift between the
maximum of the EUV-TEC correlation compared to the maximum of the ionospheric
delay in late summer also indicates the influence of other effects. The
coupling with the seasonal variations of thermospheric winds might be a
candidate having some influence on the delay. This needs to be investigated
in more detail in a future work. Furthermore, the analysis of EUV and other
ionospheric parameters besides TEC (e.g. hmF2 or NmF2) needs to be done in
future work, to investigate if a similar or different delay could give more
insight into the physics of the seasonal variation. The possible effects of
the ionization of different species were analysed by correlating the whole
EUV spectrum with the TEC data. It could be shown that the different
wavelengths bands of EUV have no major impact on the correlation and delay.
The whole EUV spectrum is contributing in the same order of magnitude to the
delay.
In future analysis also the UV data should be added, which allows to take the
photodissociation into account which happens around ≈150 nm. Such
an analysis could confirm the influence on the delay by atomic and molecular
oxygen mentioned in and . The results
indicate that photodissociation processes might be primarily cause for
differences in the delay. The coupling with thermospheric winds
is expected to have some influence too, but needs to be
investigated in more detail in future. Although ionization processes are
crucial for the genesis of the ionosphere, their influence on the ionospheric
delay has to be analyzed for the different ionospheric reactions in the F
region. Another major point of interest for future analysis is the dependence
of the delay from longitude and latitude. In this paper all calculations used
the same location and focused on the dependence on wavelengths and time.
Data availability
IGS TEC maps has been provided by NASA through
(ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/). F10.7 data has been
provided by NASA through (ftp://spdf.gsfc.nasa.gov/pub/data/omni/).
EVE data has been provided by LASP through
(http://lasp.colorado.edu/eve/data_access/evewebdata/products/level3/, Tapping, 2013).
GOES data has been provided by NOAA through
(https://satdat.ngdc.noaa.gov/sem/goes/data/euvs/).
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Kleinheubacher
Berichte 2017”. It is a result of the Kleinheubacher Tagung 2017,
Miltenberg, Germany, 25–27 September 2017.
Acknowledgements
IGS TEC maps and F10.7 data have been provided by NASA. GOES data has been
provided by NOAA. EVE data has been provided by LASP. The study has been
supported by Deutsche Forschungsgemeinschaft (DFG) through grants no. BE 5789/2-1 and JA 836/33-1. Edited by: Ralph
Latteck Reviewed by: Ljiljana R. Cander and one anonymous
referee
ReferencesChen, Y., Liu, L., Le, H., and Wan, W.: How does ionospheric TEC vary if
solar EUV irradiance continuously decreases?, Earth Planet. Space,
66, 52 pp.,
10.1186/1880-5981-66-52, 2014.Fennelly, J. A. and Torr, D. G.: Photoionization and photoabsorption cross
sections of O, N2, O2, and N for aeronomic calculations,
Atom. Data Nucl. Data Tables, 51, 321–363, 10.1016/0092-640X(92)90004-2,
1992.
Hernández-Pajares, M.: IGS Ionosphere WG Status Report: Performance of
IGS Ionosphere TEC Maps Position Paper, 263–275,
2004.Hernández-Pajares, M., Juan, J. M., Sanz, J., Orus, R., Garcia-Rigo, A.,
Feltens, J., Komjathy, A., Schaer, S. C., and Krankowski, A.: The IGS VTEC
maps: a reliable source of ionospheric information since 1998, J. Geodesy,
83, 263–275, 10.1007/s00190-008-0266-1, 2009.Jacobi, C., Jakowski, N., Schmidtke, G., and Woods, T.: Delayed response of
the global total electron content to solar EUV variations, Adv. Radio Sci.,
14, 175–180, 10.5194/ars-14-175-2016, 2016.Jakowski, N., Fichtelmann, B., and Jungstand, A.: Solar activity control of
ionospheric and thermospheric processs, J. Atmos. Terr. Phys., 53,
1125–1130, 10.1016/0021-9169(91)90061-B, 1991.
Kutiev, I., Tsagouri, I., Perrone, L., Pancheva, D., Mukhtarov, P.,
Mikhailov, A., Lastovicka, J., Jakowski, N., Buresova, D., Blanch, E.,
Andonov, B., Altadill, D., Magdaleno, S., Parisi, M., and Torta, J. M.: Solar
activity impact on the Earth's upper atmosphere, J. Space Weather Space,
3, A06, 10.1051/swsc/2013028, 2013.Machol, J., Viereck, R., and Jones, A.: GOES EUVS Measurements, v2.
https://www.ngdc.noaa.gov/stp/satellite/goes/doc/GOES_NOP_EUV_readme.pdf
(last access: 22 November 2017), 2014.
Nikutowski, B., Brunner, R., Jacobi, C., Knecht, S., Ehrhardt, C., and
Schmidtke, G.: EUV measurements by the auto-calibrating SolACES spectrometers
onboard the International Space Station (ISS), 7 pp., 2010.Oinats, A. V., Ratovsky, K. G., and Kotovich G. V.: Influence of the 27-day
solar flux variations on the ionosphere parameters measured at Irkutsk in
2003–2005, Adv. Space Res., 42, 639–644, 10.1016/j.asr.2008.02.009,
2008.Schmidtke, G., Brunner, R., Eberhard, D., Halford., B., Klocke, U., Knothe,
M., Konz, W., Riedel, W.-J., and Wolf, H.: SOL-ACES: Auto-calibrating EUV/UV
spectrometers for measurements onboard the International Space Station, Adv.
Space Res., 37, 273–282, 10.1016/j.asr.2005.01.112, 2006.Schmidtke, G., Nikutowski, B., Jacobi, C., Brunner, R., Erhardt, C., Knecht,
S., Scherle, J., and Schlagenhauf, J.: Solar EUV irradiance measurements by
the Auto-Calibrating EUV Spectrometers (SolACES) aboard the International
Space Station (ISS), Sol. Phys., 289, 1863–1883,
10.1007/s11207-013-0430-5, 2014.Tapping, K. F.: The 10.7 cm solar radio flux (F10.7), Space Weather, 11,
394–406, 10.1002/swe.20064, 2013.Unglaub, C., Jacobi, Ch., Schmidtke, G., Nikutowski, B., and Brunner, R.:
EUV-TEC proxy to describe ionospheric variability using satellite-borne solar
EUV measurements: first results, Adv. Space Res., 47, 1578–1584,
10.1016/j.asr.2010.12.014, 2011.Vellante, M., Förster, M., Pezzopane, M., Jakowski, N., Long Zhang, T.,
Villante, U., De Lauretis, M., Zolesi, B., and Magnes, W.: Monitoring the
Dynamics of the Ionosphere-Plasmasphere System by Ground-Based ULF Wave
Observations, Earth Moon Planets, 104, 25–27, 10.1007/s11038-008-9246-y,
2009.Woods, T. N., Eparvier, F. G., Bailey, S. M., Chamberlin, P. C., Lean, J.,
Rottman, G. J., Solomon, S. C., Tobiska, W. K., and Woodraska, D. L.: Solar
EUV Experiment (SEE): Mission overview and first results, J. Geophys. Res.,
110, A01312, 10.1029/2004JA010765, 2005.Woods, T. N., Eparvier, F. G., Hock, R., Jones, A. R., Woodraska, D., Judge,
D., Didkovsky, L., Lean, J., Mariska, J., Warren, H., McMullin, D.,
Chamberlin, P., Berthiaume, G., Bailey, S., Fuller-Rowell, T., Sojka, J.,
Tobiska, W. K., and Viereck, R.: Extreme Ultraviolet Variability Experiment
(EVE) on the Solar Dynamics Observatory (SDO): Overview of Science
Objectives, Instrument Design, Data Products, and Model Developments Solar
Physics, 275, 115–143,
10.1007/s11207-009-9487-6, 2012.