# Continuous Solar Observations from the Ground – Assessing Duty Cycle from
GONG Observations

Kiran Jain, Sushant C. Tripathy, Frank Hill & Alexei A. Pevtsov  National
Solar Observatory, 3665 Discovery Dr., Boulder, CO 80303, USA<EMAIL_ADDRESS>
###### Abstract

Continuous observations play an important role in the studies of solar
variability. While such observations can be achieved from space with almost
100% duty cycle, it is difficult to accomplish very high duty cycle from the
ground. In this context, we assess the duty cycle that has been achieved from
the ground by analyzing the observations of a six station network of identical
instruments, Global Oscillation Network Group (GONG). We provide a detailed
analysis of the duty cycle using GONG observations spanning over 18 years. We
also discuss duty cycle of individual sites and point out various factors that
may impact individual site or network duty cycle. The mean duty cycle of the
network is 93%, however it reduces by about 5% after all images pass through
the stringent quality-control checks. The standard deviations in monthly and
yearly duty cycle values are found to be 1.9% and 2.2%, respectively. These
results provide a baseline that can be used in the planning of future ground-
based networks.

††: Pub. Astron. Soc. Pacific

Keywords: Sun – helioseismology – methods: data analysis – methods:
observational – methods: statistical

##  1 Introduction

Solar observations are now available for a few centuries, however most of the
records prior to the 20th century are sparse. The coverage has significantly
increased in last few decades, primarily due to the advancement in observing
capabilities from both space and ground. While observations from space are
almost continuous with small disruptions, the ground-based observations are
adversely affected by the diurnal cycle. There are several research areas in
solar physics where uninterrupted observations for a long time are essential
in order to achieve reliable results. Helioseismology is among one of these
areas where solar oscillations below the surface are used to study phenomena
occurring in the interior. These oscillations with a prominent period of
5-minutes were discovered in early 1960s (?, ?) and the physical mechanism
explaining these observations was suggested about a decade later (?, ?). It
was soon realized that the continuous observations at high cadence for a
longer time would be needed to reduce side lobes in the temporal power
spectra, but this was not possible from a single ground station due to the
diurnal cycle. There were two possibilities for continuous observations from
ground: either from the South Pole but for a limited time during the Austral
Summer (?) or build a network of identical instruments at geographically
separated locations around the Earth. This led to the inception of several
networks for helioseismic observations; some are still operational even after
more than 25 years and other ceased operations after a few years. Observations
from these networks allowed the measurement of solar oscillations from low to
high degrees where degree is the number of waves around the solar
circumference.

Some of the short-lived networks were the International Research on the
Interior of the Sun (?, IRIS) and the Taiwanese Oscillations Network (?, TON).
Two still operating networks are the Birmingham Solar Oscillation Network (?,
BISON) since 1975 and the Global Oscillation Network Group (?, GONG) since
1995. While BiSON provides observations in solar-disk-integrated light best
suitable for low spherical harmonic degree helioseismic modes, which travel
deep into the core, GONG observations resolve acoustic modes covering a wider
range from core to the near-surface layers. In addition, several observational
campaigns were held at the South Pole from the early 1980s to mid-1990s (?,
?), however continuous observations were obtained for a maximum period of
about a week. Occasional observation campaigns at the South Pole are still
continuing with specific science goals (?, ?) but continuous observations were
acquired only for a few days. All of these observations played a crucial role
in advancing the research in helioseismology and confirmed the solar
variability below the surface as observed above the surface, first reported by
? (?). Furthermore, observations from the current or past space missions also
complemented the ground-based observations (?).

In this article, we assess the temporal coverage from the ground-based
observatories using observations of the GONG network. Section 2 presents a
brief history of GONG. Data used to compute duty cycle is described in Section
3. Assessment of the duty cycle of individual sites and the network as a whole
are discussed in Sections 4 and 5, respectively. We also provide statistical
analysis of the simultaneous observations between different sites in 6.
Section 7 demonstrates the scientific need of high duty cycle, especially in
helioseismic studies. Finally, the summary is presented in Section 8.

##  2 A Brief History of GONG: From Helioseismology to Space Weather

Table 1:  Locations of GONG sites and operation start dates.

  * Site | Latitude | Longitude | Elevation
---|---|---|---
Name | Identifier |  |  | [meters]
Learmonth, Australia | LE | S 022∘ 13′ 06.6′′ | E 114∘ 06′ 09.8′′ | 15
Udaipur, India | UD | N 024∘ 36′ 53.8′′ | E 073∘ 40′ 10.9′′ | 677
El Teide, Spain | TD | N 028∘ 18′ 03.0′′ | W 016∘ 30′ 43.0′′ | 2425
Cerro Tololo, Chile | CT | S 030∘ 10′ 04.2′′ | W 070∘ 48′ 19.7′′ | 2190
Big Bear, USA | BB | N 034∘ 15′ 37.2′′ | W 116∘ 55′ 17.1′′ | 2063
Mauna Loa, USA | ML | N 019∘ 32′ 10.1′′ | W 155∘ 34′ 33.3′′ | 3471

Figure 1: Locations of GONG sites around the world.

One of the first tasks in establishing a network of observatories is to
identify the number and locations of the sites suitable for substantially
reducing the observed diurnal cycle. For this, a systematic study of potential
sites for the GONG network started with estimating the performance of several
hypothetical ground-based network composed of two to six existing observing
sites distributed in both longitude and latitude. Results suggested that a
network of six sites was needed to achieve $>$ 90 % annual mean duty cycle
(?). This initiated a coordinated site survey with identical instruments (?)
placed at 15 sites around the globe, divided into six longitudinal bands to
measure the cloud-cover percentage and transparency of the sky. Based on the
detailed analysis of the collected data (?) and the overall performance of
each site (?), six sites were finally selected in early 1991. Each selected
site represents one of the six longitudinal bands that allows the network to
make 24-hour-a-day observations of the Sun year round. These sites are shown
in Figure 1 and their locations are given in Table 1.

The GONG instrument is based on measuring the line-of-sight Doppler shifts of
the photospheric spectral line Ni i 676.8 nm. The key element of this
instrument is a Michelson interferometer, the details of which are given by
(?). Deployment of the GONG instruments started in January 1995 with the TD
site and the full network was completed in October of the same year after UD,
the last site, became operational. Start dates of the operations at each site
are also given in Table 1. Identical instruments and identical settings at all
six sites operating in automated mode with minimal human intervention is the
key to the network’s success. The science-grade data of the network are
available since 7 May 1995. At the beginning, the observations were carried
out with low-resolution cameras that had rectangular pixels (aspect ratio of
1.28:1) and was oriented in such a way that the longer dimension was aligned
with the solar axis of rotation. The resulting images were 204 $\times$ 239
pixels across with a pixel resolution of $\approx$10 $\times$ 8 arcseconds.

After observing successfully for the initial three years using low-resolution
cameras (?), the capabilities of the GONG network were expanded in 2001 by
upgrading its cameras to 1024 $\times$ 1024 with square pixels (?). Until
2001, the GONG, commonly known as GONG classic, data usage was primarily
limited to global helioseismic studies. The upgrade provided data that is
suitable for local helioseismic studies, e.g. internal conditions below small
regions on the surface, hemispheric and latitudinal distributions of
subsurface flows, mapping of the farside for the detection of active regions,
etc. The upgraded network is commonly referred to as GONG+. This also served
as the starting point to expand GONG’s capabilities to space weather. In
subsequent years, the GONG observations were extended to one-minute cadence
full-disk magnetograms in 2006 (?, ?) and 2048 $\times$ 2048 pixels images of
the H$\alpha$ spectral line at a wavelength of 656.28 nm in mid-2010 (?). Note
that all H$\alpha$ observations are taken at a cadence of one minute at each
site but with the acquisition time offset between adjacent sites so that a new
H$\alpha$ image is, in principle, available every 20 seconds from the network.
Various GONG data products are available at https://nso.edu/data/nisp-data/,
and their applications in space-weather related studies and predictions are
described by (?).

##  3 Data

Table 2:  Details of the network data used in this study.

  * Data Type | Count | Duty Cycle | Details
---|---|---|---
Period covered | 18 years |  |
| (2002 – 2019) |  |
Total Number of days | 6574 |  |
Total Minutes covered | 9.47 $\times$ 106 |  |
Total Observations from all sites | 1.35 $\times$ 107 |  |
Total Minutes with observations | 8.36 $\times$ 106 | 88.33 % |
Total Minutes with no observations | 1.11 $\times$ 106 | 11.67 % |
Total minutes with independent observations | 4.12 $\times$ 106 | 43.49 % | Table 3
Total minutes with simultaneous observations | 4.24 $\times$ 106 | 44.84 % | Table 4

We compute the duty cycle of individual sites or the network from fully
calibrated images that have successfully passed through stringent quality
checks as described in the Automated Image Rejection (?, AIR), and consist of,
for example, active tracking, image stability, signal/noise level in
individual images (?, ?), temporal variations between images, and the
correction for orientation for the best position-angle estimation (?). As
mentioned in Section 2, GONG has many different observables out of which we
choose observations of Doppler velocities to determine the duty cycle. While
the magnetograms and the Dopplergrams are part of the same observing sequence,
the data products go through different processing pipelines and thus have
different image rejection criteria as part of the quality control. H$\alpha$
observations have been taken since 2010 using a different instrument setup and
may provide a slightly different duty cycle.

The duty cycle is defined as the fraction of 24-hour day for which the
observations are available. In addition, the network duty cycle is determined
by combining all six site images for each minute in the same 24-hour period.
There are several factors that significantly influence the observations at
individual sites and, finally, the network duty cycle. These factors include,
but are not limited to, severe weather conditions, instrument problems,
downtime due to preventive maintenance and upgrades, etc. Although the network
has been operational for more than 25 years, we consider only a period of 18
years from January 2002 to December 2019 for assessing the duty cycle, i.e.
the period after the completion of camera upgrade. This period allows us to
analyze consistent data set without any forced major disruptions. A total
number of 6574 days, i.e. 9.47$\times$106 minutes, are covered in this study.
Since there are overlapping observations between different sites and GONG
provides observations at a cadence of one minute, the analysis has been
carried out using more than 13 million images collected at GONG sites over the
period of 18 years. Tables 2 summarizes the details of the network input data
and Table 3 provides the details of each site’s data. In the next section, we
describe the performance of individual sites starting from the East. Results
for the full network are discussed in Section 5.

Table 3:  Details of individual site data used in this study.

  * Sites | Contribution to total | Contribution to network duty cycle
---|---|---
| observations | Total | Independent | Overlapping
LE | 18.6 % | 26.6 % | 12.9 % | 13.7 %
UD | 11.2 % | 15.9 % | 5.1 % | 10.8 %
TD | 17.8 % | 25.7 % | 8.7 % | 17.0 %
CT | 21.1 % | 30.1 % | 8.0 % | 22.1 %
BB | 16.5 % | 23.6 % | 4.7 % | 18.9 %
ML | 14.8 % | 21.2 % | 4.1 % | 17.1 %

##  4 Individual Site Duty Cycle

###  4.1 Learmonth, Australia (LE)

The Learmonth Solar Observatory (LSO) situated on the western shore of the
Exmouth Gulf on the North West Cape of Australia houses one of GONG’s
instruments. The observatory is co-administered by the Bureau of Meteorology,
Australian Government, and the US Air Force. This GONG site became operational
in April 1995. We display the variation of daily duty cycle in Figure 2, as an
example, for the year 2009. Here different panels represent different calendar
months. In addition to the seasonal variation in duty cycle, there are some
random gaps, which signify no observations for entire days. Longer gaps (e.g.
from the last week of April to mid-May in Figure 2) generally arise due to
instrument downtime or preventive maintenance periods while shorter gaps
correspond to severe weather that may last from one to a few days. The maximum
daily observations were obtained for 11.47 hours which gives a duty cycle of
47.8 %. In total, LE provided observations for 5621 days out of 6574 days
(i.e. 85.5 % days). Various milestones achieved by individual sites are given
in Table 4.

Figure 2: Daily duty cycle for Learmonth (LE) site for each month in the year
2009. The dashed (red) line represents the mean value over the entire period
(see Table 4) whereas the dotted (black) line shows a duty cycle value of 50
%.

Figure 3: Monthly duty cycle for Learmonth (LE) site for selected years.
Yearly means for 2005, 2009, 2013, and 2017 are 26 %, 27 %, 27 %, and 26 %,
respectively. The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.  Table 4: Various performance figures achieved by individual sites.

  * Criterion | Frequency | LE | UD | TD | CT | BB | ML
---|---|---|---|---|---|---|---
Total coverage [Days] | All | 5621 | 3949 | 5280 | 5516 | 5532 | 5536
Mean Duty Cycle [%] | All | 26.6 | 16.0 | 25.4 | 30.2 | 23.6 | 21.2
Median Duty Cycle [%] | Monthly | 28.2 | 18.4 | 26.5 | 31.1 | 25.5 | 21.4
| Yearly | 26.5 | 16.2 | 25.7 | 31.1 | 23.8 | 21.1
Maximum Duty Cycle [%] | Daily | 47.8 | 46.7 | 49.4 | 50.3 | 48.5 | 47.7
| Monthly | 34.2 | 27.0 | 39.4 | 43.6 | 36.3 | 26.1
| Yearly | 31.1 | 21.4 | 30.2 | 33.1 | 25.6 | 29.5
Minimum Duty Cycle [%] | Daily | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0
| Monthly | 17.8 | 4.0 | 16.1 | 18.0 | 12.6 | 18.1
| Yearly | 23.1 | 9.9 | 20.5 | 26.4 | 19.8 | 14.1
Standard Deviation [%] | Monthly | 6.0 | 9.7 | 8.3 | 9.7 | 7.3 | 2.6
| Yearly | 2.0 | 3.0 | 2.6 | 2.2 | 1.4 | 3.3

Figure 4: Monthly variation in duty cycle averaged over the entire period for
all six GONG sites. Horizontal dotted (black) and dashed (red) lines represent
50 % and the mean value for individual sites over the entire period (see Table
4), respectively.

Figure 5: Yearly variation in duty cycle for all six GONG sites. Horizontal
dotted (black) and dashed (red) lines represent 50 % and the mean value for
individual sites over the entire period (see Table 4), respectively.

To examine the trends in LE observations, we display in Figure 3 the monthly
variation in duty cycle for four selected years. A systematic seasonal
variation is seen in all four years with a dip in the duty cycle in April –
May, except for 2017, followed by an increasing trend. The higher duty cycle
is achieved in later months every year with the beginning of Summer in the
southern hemisphere due to the availability of sunshine over a larger fraction
of the day. The overall trends in monthly and yearly variations averaged over
the entire period are presented in Figures 4 and 5, respectively. We have
included other GONG sites in the same figure for comparison. As mentioned
above, the trend in the mean monthly values display strong seasonal variation
with a standard deviation of 6.0 %. When we consider yearly variation (see
Figure 5), LE’s contribution to the GONG network has been stable throughout
the entire period and, as a result, the standard deviation reduces to about
2.0 %.

From Table 3, we find that the observations taken at LE contribute about 18.6
% to the total observations and 26.6 % to the network duty cycle. As mentioned
earlier, all sites provide independent as well as overlapping observations and
LE’s contribution to the network duty cycle is almost equally split between
these two categories. It is further noted that LE’s independent contribution
is the largest among all sites.

###  4.2 Udaipur, India (UD)

The Udaipur Solar Observatory (USO) is situated in a semi-arid highland region
of Western India and houses the GONG’s instrument in the Asian band. USO is
administered by the Physical Research Laboratory (Ahmedabad), a unit of
Department of Space, Government of India. The Udaipur site experiences strong
weather pattern with an extended period of monsoon lasting for two – three
months every year and the observations are stopped during this period. As a
result, UD’s contribution to the total observations is the lowest at 11.2 %.
As presented in Table 3, UD provides only 5.1 % independent contribution to
the network duty cycle while the total contribution is 15.9 %. Two-thirds of
UD observations have overlap with other sites and the details are given in
Section 5.

To evaluate the daily variation throughout the year, we show daily duty cycle
for the year 2009 in Figure 6 and the monthly duty cycle for four selected
years in Figure 7. Compared to LE, Udaipur observations have many longer gaps
in 2009. The UD site was down from early July to mid-September due to the
monsoon and the other two gaps, in late January and early October, are due to
system failures and occurrence of preventive maintenance. The downtime due to
the monsoon is seen in all monthly average plots presented in Figure 7. The
duty cycle again decreases in December – January due to hazy conditions and
less sunshine hours available during Winter months. The maximum observations
are normally obtained from February to May. During Spring/Summer time, the
Udaipur site observed for more than 11 hours and the maximum daily duty cycle
during 2002 – 2019 reached 46.7 %.

Figure 6: Daily duty cycle for Udaipur (UD) site for each month in the year
2009. The dashed (red) line represents the mean value over the entire period
(see Table 4) whereas the dotted (black) line shows a duty cycle value of 50
%.

Figure 7: Monthly duty cycle for Udaipur (UD) site for selected years. Yearly
means for 2005, 2009, 2013 and 2017 are 16 %, 19 %, 16 %, and 17 %,
respectively. The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.

The average monthly and yearly duty-cycle trends, as shown in Figures 4b and
5b, clearly demonstrate the effect of the monsoon on the duty cycle. The
seasonal variation in mean duty cycle for UD is more significant than for LE.
As a result, the standard deviations, as summarized in Table 4, are also
higher for both monthly and yearly averages. In addition, the mean duty cycle
is notably lower. Total observations from UD are available for 60.1 % or 3949
days during the period of 18 years, the lowest among all sites. The lower
coverage from UD was a combination of the monsoon season and several other
long periods of system downtime, for example, a fire broke in the GONG
instrument shelter in August 2010 and the site remained non-operational until
the beginning of 2011.

###  4.3 El Teide, Canary Islands (TD)

The El Teide site is located on the island of Tenerife, in the Canary Islands
off the west coast of Africa, hosted by the Instituto Astrofísica de Canarias
(IAC). The IAC is administered by a public consortium which includes the
Universidad de la Laguna. Similar to other sites, observations from TD are
also affected by strong seasonal variations. Sometimes it is hit by intense
dust storm, known as the Calima, blowing off the Sahara Desert. During the
Calima, strong winds are accompanied by a orange haze that significantly
reduces the sky transparency and the observations, if any, are discarded. Even
after being affected by the several months of poor weather conditions in
Winter and Summer, TD provides a good coverage of about 80.3 % of the days.

The daily duty cycles for TD are presented in Figure 8 for 2009. It is evident
that daily observations from TD are consistent for several months in Summer,
thus providing good coverage to the GONG network. This is also apparent from
Figure 9 where the duty cycle from April to September for all four years is
consistently higher than other months. From Table 3, one can notice that TD’s
contribution to the network duty cycle is about 25 % which is similar to LE.
The independent contribution from the Teide site is 8.7 % in the overall duty
cycle. This site along with LE becomes notably more important when the UD
instrument is down for the long monsoon season or any other down-time. During
a clear day in Summer, good observations for almost 12 hours can be obtained
from this site.

Similar to other sites, a strong weather-dependent variation in the TD duty
cycle is apparent in Figures 4c where we show the monthly duty cycle averaged
over the entire period considered in this study. It is noted that the duty
cycle drops to almost one half in the Winter months and the standard deviation
in the monthly mean duty cycle is moderately large with a value of 8.3 %.
However, the yearly variation is relatively small (Figure 5c) with a standard
deviation of only 2.6 % (Table 4).

Figure 8: Daily duty cycle for the El Teide (TD) site for each month in 2009.
The dashed (red) line represents the mean value over the entire period (see
Table 4) whereas the dotted (black) line shows a duty cycle value of 50 %.

Figure 9: Monthly duty cycle for the Teide (TD) site for selected years.
Yearly means for 2005, 2009, 2013, and 2017 are 25 %, 22 %, 30 %, and 27 %,
respectively. The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.

###  4.4 Cerro Tololo, Chile (CT)

Figure 10: Daily duty cycle for Cerro Tololo (CT) site for each month in 2009.
The dashed (red) line represents the mean value over the entire period (see
Table 4) whereas the dotted (black) line shows a duty cycle value of 50 %.

Figure 11: Monthly duty cycle for Cerro Tololo (CT) site for selected years.
Yearly means for 2005, 2009, 2013, and 2017 are 29 %, 33 %, 31 %, and 30 %,
respectively.The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.

The second GONG site in the southern hemisphere is located on the mountain
Cerro Tololo in northern Chile. The instrument is located on the grounds of
the Cerro Tololo Interamerican Observatory (CTIO), a division of NSF’s
National Optical-Infrared Astronomy Research Laboratory (NSF’s NOIRLab)
headquartered in Tucson, USA. Due to excellent sky conditions, CTIO is one of
the main sites for astronomical investigations of the southern skies. This is
reflected in the number of observations from the individual sites summarized
in Table 3. CT’s contribution to GONG’s cumulative observations is the highest
and has 5516 observing days, that is about 84 % of the total days. Despite a
significantly large number of observations and about 30 % contribution to the
network duty cycle, the independent contribution from CT is only 8.0 % while
overlapping observations cover the remaining 22.1 %, as evident from Table 3.
This large fraction of overlapping observations are due to the proximity of
adjacent sites BB and TD. Moreover, CT has the maximum overlap with the
neighboring sites’ observations.

Daily variation in duty cycles for the year 2009 is presented in Figure 10,
which exhibits stability for several months. Maximum duty cycle of about 50 %
is achievable for several months, i.e. January, February, November, and
December. From the monthly variation for four sample years (Figure 11), it is
clear that the number of observations from CT reaches its lowest value in the
month of June. This strong seasonal variations is also noticeable in Figure 4d
and the standard deviation in the monthly duty cycle is about 9.7 %. Aside
from the seasonal variation, the yearly duty cycles from CT remain reasonably
high as shown in Figure 5d and the values remain between 28 % – 33 % with a
standard deviation of 2.2 %, as given in Table 4.

###  4.5 Big Bear, USA (BB)

Figure 12: Daily duty cycle for Big Bear (BB) site for each month in the year
2009. The dashed (red) line represents the mean value over the entire period
(see Table 4) whereas the dotted (black) line shows a duty cycle value of 50
%.

Figure 13: Monthly duty cycle for Big Bear (BB)) site for selected years.
Yearly means for 2005, 2009, 2013, and 2017 are 24 %, 23 %, 27 %, and 24 %,
respectively. The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.

The only GONG site in the United States mainland is located in the southwest
on the premises of Big Bear Solar Observatory (BBSO), managed by the New
Jersey Institute of Technology (NJIT). The instrument is placed on the north
shore of an artificial lake in the San Bernardino Mountains in California.
Similar to other sites, BB also has strong seasonal variation in the duty
cycle that peaks around June and reaches its lowest value in December –
January. From the daily duty-cycle histograms, as shown in Figure 12, it is
evident that the observations are sparse in the Winter months. Although the
observations from BB are consistent in Summer months, there are a few years
when the duty cycle is relatively low and 2009 is among those years. As
illustrated in Figure 13, the monthly duty-cycles for four years clearly show
the variation from one year to another. The overall trend is more pronounced
in Figure 4e where the average monthly duty cycle is shown. A gradual increase
in the duty cycle is observed after February with a maximum in June, a slow
decrease during July to September and finally, a sharp decline is noticed
until December.

We note that the Big Bear site has been very stable throughout the operations
of GONG and the year-to-year variation is the least among all six GONG sites
(See Figure 5e). Therefore, the standard deviation in yearly values is also a
minimum, i.e. only 1.4 %, while the standard deviation in monthly values is
7.3 %. Table 4 shows that the observations collected at BB spanned over 5532
days that is about 84.1 % of the total observing days. The BB contribution to
all GONG observations is around 16.5 % with three-quarters of them have
overlap with other sites as described in Table 3. Nevertheless, the
independent coverage of 4.7 % in total duty cycle makes this site equally
important with other GONG sites.

###  4.6 Mauna Loa, USA (ML)

The sixth GONG site is located at the north side of the Mauna Loa volcano at
the Mauna Loa Observatory (MLO), on the island of Hawai’i (Big Island). MLO is
an atmospheric baseline station of the Global Monitoring Laboratory (GML) of
the National Oceanic and Atmospheric Administration (NOAA). Local staff
support is provided by the Mauna Loa Solar Observatory operated by the High
Altitude Observatory, Boulder, Colorado. Unlike other GONG sites, Mauna Loa is
minimally affected by strong seasonal variations. This is clearly visible in
the daily duty-cycle plot for a year displayed in Figure 14. Although we do
see variations from month to month, these are not as strong as in other sites
and the daily values remain in a similar range throughout the year. Further,
to evaluate the existence of this pattern in all years, we plot monthly values
for four individual years in Figure 15. This shows no distinct trend in the
monthly variation within a year, however there are random fluctuations, but
these do not indicate any strong seasonal pattern. On averaging monthly values
over the entire period presented in Figure 4f, a mild pattern emerges with
minor dips around both equinoxes. Finally, we find that the standard deviation
in monthly values for ML is only about 2.6 % which is the lowest among all
sites ( Table 4). However, the standard deviation in yearly values displayed
in Figure 5f is higher than the monthly value and also the highest among all
sites indicating that ML experienced the highest year-to-year variation in the
duty cycle.

In addition, the number of observing days at ML at 84.2 % level is comparable
with CT and BB, but the contribution to the total network observations is the
second lowest (see Table 3). It is worth pointing out that the independent
observations from ML cover merely 4.1 % which is the lowest among all six
sites; more than three-quarters of ML observations overlap with other sites.

Figure 14: Daily duty cycle for Mauna Loa (ML) site for each month in 2009.
The dashed (red) line represents the mean value over the entire period (see
Table 4) whereas the dotted (black) line shows a duty cycle value of 50 %.

Figure 15: Monthly duty cycle for Mauna Loa (ML) site for selected years.
Yearly means for 2005, 2009, 2013, and 2017 are 20 %, 20 %, 22 %, and 20 %,
respectively. The dashed (pink) line represents the mean value over the entire
period (see Table 4) whereas the dotted (black) line shows a duty cycle value
of 50 %.

##  5 Network Duty Cycle

Figure 16: Daily duty cycle of the GONG network for 2009. The dashed (red)
line represents the mean value over the entire period (see Table 2) whereas
the dotted (black) line shows a duty cycle value of 50 %.

Figure 17: Mean monthly duty cycle of the GONG network for four selected
years. Yearly means for 2005, 2009, 2013, and 2017 are 88 %, 89 %, 90 %, and
88 %, respectively. The dashed (pink) line represents the mean value over the
entire period (see Table 2) whereas the dotted (black) line shows a duty cycle
value of 50 %.

Figure 18: (Left) Monthly and (Right) yearly variations in the network duty
cycles. The dashed (red) line represents the mean value over the entire period
(see Table 2) whereas the dotted (black) line shows a duty cycle value of 50
%.

Section 4 clearly demonstrates that all sites except ML are affected by strong
seasonal patterns. While sites in the northern hemisphere provide good
coverage during Summer months (humps in June – July for BB and TD in Figure
4), this trend reverses in the southern hemisphere (dips in June – July for LE
and CT). Further, due to the low latitude location, the seasonal patterns are
less apparent in ML observations. However, the network observations after
integrating over those from individual sites (?) display a consistent coverage
throughout the year with significantly high duty cycle. Figure 16 presents
network daily duty cycle for a year exhibiting minimal weather-related
patterns and the daily value reaches $>$ 90 % in most cases. We do notice some
days with duty cycles dipping down to 50 % or lower but these are rare. To
better understand the variation, if any, we compute mean monthly duty cycles
for the network for four years and present them in Figure 17. We do not see
any distinct pattern in all of these years, which strongly supports the
concept of a ground-based network for uninterrupted observations with
consistent duty cycles.

Finally, we present monthly and yearly duty cycles averaged over the entire
period in Figure 18. We notice a mild trend in the monthly duty cycle (left
panel of Figure 18) throughout the year reaching its lowest value in July (84
%) and the highest in December/January (91 %). We also notice a mild variation
in the yearly values. The yearly mean is relatively low in the first two years
and then remains around the mean over the entire period for rest of the years.
The low standard deviations of 1.9 % and 2.2 % in monthly and yearly values,
respectively exhibit excellent stability in the network duty cycle, even after
more than 25 years of its operations. In addition, Figure 5 also indicates
that there are no long-term changes in the duty cycles of any of the six
network sites though the contribution from each site is affected by the local
weather as well as the instrument down time. The lack of long-term variations
in duty cycles further implies that there are no significant changes in
climate in any of the GONG network sites. These findings clearly demonstrate
that a ground-based network can reliably study long term solar variability.

The mean duty cycle over 18 years is 88.3 % and the median value is 92.0 %.
Figure 19 displays the duty cycle corresponding to each day for the entire
analysis period; smaller bars representing days where the duty cycle is lower
than the median values. It is also observed that most days with lower duty
cycles are around mid-year which is also apparent in monthly mean values
displayed in Figure 18. Distribution of the daily values for bin-size of 2 %
is given in Figure 20. We find that only 82 days out of 6574 days covered in
this study have the duty cycle less than 50 %. Moreover, almost 13 % of the
total days, i.e. 865 days, have a duty cycle of 99 % or greater of which 106
days have no gaps, i.e., 100 % duty cycle. In general, the frequency of
occurrence gradually decreases with decreasing duty cycle except when values
lie between 97 % – 98 % where we notice a slight increase.

Figure 19: Daily duty cycle of GONG network for the entire period covered in
this study, i.e. 2002 – 2019. Smaller bars represent days with duty cycle
lower than the median value, i.e. 92 %.

Figure 20: Histogram of daily duty cycle of the fully calibrated images for
the entire period.

Figure 21: Distribution of daily duty cycle computed from all the images taken
at sites. Please note that these images are partially calibrated (see text for
explanation).

It is important to mention that the above analysis is based on the fully
calibrated  images where each and every individual site image is passed
through the pre-defined quality control checks (?). During this process, some
images are rejected every day affecting the site as well as network duty
cycles. To estimate the impact of the rejection criteria on the network duty
cycle, we display the distribution of the duty cycle computed from the
partially calibrated site images in Figure 21. Note that these partially
calibrated images do not pass through all quality checks and may be counted
for initial observations. A comparison between the duty cycles for partially
and fully calibrated data reveals that the mean duty cycle obtained from the
partially calibrated images is higher with the mean value of 93.5 % and the
median value of 97 %. This is in excellent agreement with the GONG site-survey
study where (?) suggested an achievable duty cycle value of 93.3 %. This also
indicates that the quality control matrices reduce the duty cycle by about 5 %
on an average, however there are large number of days when the duty cycle is
not affected in this process, primarily due to multiple-site observations for
a given minute. Compared to fully calibrated data where only 13 % of the days
have duty cycle 99 % or more, the partially calibrated data have about 40 % of
the days with duty cycle in this range. This reduction in duty cycle is also
seen in another six-site network, BiSON, where the duty cycle of 82 % reduces
to 78 % when fully calibrated images were used (?).

###  5.1 Comparison with Previous Results

Figure 22: Dependence of the duty cycle on the number of sites. Filled circles
represent values for each number of stations with different GONG-site
combinations. Open circles are for the annual mean duty cycle computed using
hypothetical networks as presented by (?). Solid line represents fit to the
data with a exponential function described in the text.

Based on the hypothetical networks, ? (?) reported that two-, three-, four-,
and six-site networks might achieve 60 %, 75 %, 82 %, and 93 % annual mean
duty cycle. To check the feasibility of these duty cycle values, we also
computed duty cycles for all possible speculative networks using GONG’s fully
calibrated site images and the results are displayed in Figure 22. The maximum
duty cycles for two-, three-, four-, five-, and six-site combinations are 57
%, 70 %, 79 %, 84 %, and 88 %, respectively. These values are smaller by a few
percent than those reported in ? (?). These are shown by the open symbols. It
should be noted that the duty-cycle values obtained in this study are also
affected by the downtime of each site due to instrument repairs and preventive
maintenance trips which were not considered in the simulated analysis.
However, as discussed earlier, the use of fully calibrated images reduces the
duty cycle by about 5 % and thus the values obtained in this study are
comparable with those reported by (?).

To study the impact of number of sites on the duty cycle, we finally fit duty
cycle values obtained for different site combinations to a function. The best
fit is found for an exponential function of the form of $f(n)$ = -1.20
e${}^{-0.22n}+1.20$, where $f$ is the duty cycle, and $n$ is the number of
sites (Figure 22). It clearly displays that the duty cycle increases
exponentially with the increasing number of sites, however the locations of
the observing sites play a critical role in achieving desired duty cycle. We
also find that adding another site to the six-site network will improve the
duty cycle notably.

##  6 Simultaneous Observations from Multiple Sites

Table 5:  Multi-site simultaneous contribution to the network duty cycle.

  * Simultaneous | Count | Duty Cycle | Details
---|---|---|---
Observations Type | [Minutes] |  |
Total | 4.24 $\times$ 106 | 44.84 % |
2 sites | 3.37 $\times$ 106 | 35.60 % | Table 6
3 sites | 8.20 $\times$ 105 | 8.66 % | Table 7
4 sites | 5.45 $\times$ 104 | 0.58 % | Table 8

In a well-organized network, it is important that simultaneous observations
are carried out at adjacent sites. This will not only minimize interruptions
in observations but also helps in a better data calibration. In ideal
conditions, the GONG network should be providing overlapping observations all
of the time. However as presented in Table 2, almost 11 % of the period
considered in this study was not covered by any site and the rest of the
coverage was equally divided among single-site and overlapping observations.
Table 5 illustrates the details of simultaneous observations from two, three,
and four sites. As expected, the maximum simultaneous coverage comes from two-
site overlap. This is in agreement with the site-survey study by (?) where
they also achieved a maximum overlap of 44.8 % between two sites. Further, the
single-site observations were limited to 28.9 %, which is lower than this
study, and three- and four-site simultaneous observations contributed to 18.5
% and 1.7 % of the total observing time.

###  6.1 Simultaneous Observations from Two Sites

Table 6:  Simultaneous coverage from two sites in the network.

  * Site 1 | Site 2 | Total Coverage |  | Simultaneous Coverage
---|---|---|---|---
|  | [Minutes] | Duty Cycle |  | [Minutes] | Duty Cycle
LE | UD | 3.2 $\times$ 106 | 34.4 % |  | 6.9 $\times$ 105 | 7.3 %
| TD | 4.8 $\times$ 106 | 50.5 % |  | 9.7 $\times$ 104 | 1.0 %
| CT | 5.4 $\times$ 106 | 56.8 % |  | 211 |  $<$0.1 %
| BB | 4.6 $\times$ 106 | 49.0 % |  | 6.9 $\times$ 104 | 0.7 %
| ML | 4.1 $\times$ 106 | 43.8 % |  | 3.1 $\times$ 105 | 3.3 %
UD | TD | 3.6 $\times$ 106 | 38.4 % |  | 2.2 $\times$ 105 | 2.3 %
| CT | 4.3 $\times$ 106 | 45.9 % |  | 1.0$\times$ 104 | 0.1 %
| BB | 3.7 $\times$ 106 | 39.5 % |  | 34 |  $<$0.1 %
| ML | 3.5 $\times$ 106 | 36.7 % |  | 1.7 $\times$ 104 | 0.1 %
TD | CT | 4.2 $\times$ 106 | 44.8 % |  | 6.9 $\times$ 105 | 7.3 %
| BB | 4.2 $\times$ 106 | 44.0 % |  | 1.4 $\times$ 105 | 1.5 %
| ML | 4.3 $\times$ 106 | 45.2 % |  | 1.6 $\times$ 104 | 0.1 %
CT | BB | 4.0 $\times$ 106 | 42.4 % |  | 4.0 $\times$ 105 | 4.2 %
| ML | 4.1 $\times$ 106 | 43.5 % |  | 2.8 $\times$ 105 | 3.0 %
BB | ML | 3.3 $\times$ 106 | 34.9 % |  | 4.7 $\times$ 105 | 4.4 %

Table 5 shows that the two-site simultaneous observations provide coverage of
about 35.6 % over the entire period. We present in Table 6 the details of the
two-site observations and their coverage. In addition, every site has some
fraction of overlapping observations with adjacent sites. It can be easily
seen that two-site observations provide coverage between 34 % – 56 %, however
the simultaneous coverage ranges between $<$0.1 % and 7.3 %. It is obvious
that the simultaneous coverage depends on the locations of the sites in the
network. The maximum overlap is observed between two pairs of sites, i.e., LE
and UD, and TD and CT, with a contribution of 7.3 % to the total duty cycle.
However, LE and UD have more overlapping observations, i.e., 21.6 % of their
total coverage compared to TD and CT with an overlapping observation of 16.4
%.

###  6.2 Simultaneous Observations from Three Sites

Table 7:  Simultaneous coverage from three sites in the network.

  * Site 1 | Site 2 | Site 3 | Total Coverage |  | Simultaneous Coverage
---|---|---|---|---|---
|  |  | [Minutes] | Duty Cycle |  | [Minutes] | Duty Cycle
LE | UD | TD | 5.3 $\times$ 106 | 55.9 % |  | 5.4 $\times$ 104 | 0.6 %
LE | UD | ML | 4.9 $\times$ 106 | 51.4 % |  | 2.7 $\times$ 104 | 0.3 %
LE | CT | BB | 6.4 $\times$ 106 | 67.8 % |  | 2.0 $\times$ 102 |  $<$0.1 %
LE | CT | ML | 6.3 $\times$ 106 | 66.0 % |  | 3.9 $\times$ 102 |  $<$0.1 %
LE | BB | ML | 5.4 $\times$ 106 | 56.7 % |  | 4.4 $\times$ 104 | 0.5 %
UD | TD | CT | 5.5 $\times$ 106 | 57.7 % |  | 1.2 $\times$ 104 | 0.1 %
TD | CT | BB | 5.2 $\times$ 106 | 55.1 % |  | 2.4 $\times$ 105 | 2.5 %
TD | CT | ML | 5.5 $\times$ 106 | 57.6 % |  | 2.5 $\times$ 104 | 0.3 %
TD | BB | ML | 5.2 $\times$ 106 | 54.9 % |  | 3.9 $\times$ 104 | 0.4 %
CT | BB | ML | 4.8 $\times$ 106 | 50.4 % |  | 3.8 $\times$ 105 | 4.0 %

Compared to two-site observations, three-site simultaneous coverage is less
frequent and the contribution is about 8.66 % in the total duty cycle. Table 7
presents the coverage between three sites where we have included only those
sites that observed simultaneously at any point in the entire period. We find
only two three-site combinations that observed simultaneously for $>$1 % in
the total coverage. Among these, the maximum overlap is obtained between CT,
BB, and ML.

###  6.3 Simultaneous Observations from Four Sites

For a tiny fraction in network duty cycle, i.e. $<$1 % , four GONG sites
observed simultaneously. As presented in Table 8, the maximum contribution
came from TD, CT, BB, and ML. These sites covered 63 % of the available time
together in 6574 days of which only 0.57 %, i.e. 54,423 min observed
simultaneously. There are two other site combinations in this category,
however their contribution to the network duty cycle is less than 0.01 %.

Table 8:  Simultaneous coverage from four sites in the network.

  * Sites | Total Coverage |  | Simultaneous Coverage
---|---|---|---
| [Minutes] | Duty Cycle |  | [Minutes] | Duty Cycle
LE, CT, BB, ML | 6.8 $\times$ 106 | 72.3 % |  | 328 |  $<$0.01 %
LE, UD, BB, ML | 6.1 $\times$ 106 | 64.3 % |  | 14 |  $<$0.01 %
TD, CT, BB, ML | 6.0 $\times$ 106 | 63.0 % |  | 54423 | 0.57 %

##  7 Significance of High Duty Cycle

GONG’s uninterrupted full-disk observations have been widely used in numerous
science projects as well as space-weather operations/monitoring where duty
cycle plays a pivotal role. In particular, helioseismic studies have been
greatly benefited from these long span of observations. For example, the
continuous data with consistently high duty cycle have allowed the study of
temporal variability in global (?, ?, ?) as well as local oscillation mode
characteristics (?, ?) and their connection with the 11-year cyclic behaviour
of the surface magnetic activity. In addition, these data have also revealed
other periodicities in helioseismic data, e.g., the quasi-biennial
oscillations (QBO) in oscillation frequencies (?). Further, long consistent
helioseismic data are also crucial in constraining the fundamental stellar
properties, particularly in asteroseismology where most stellar observations
cover relatively short time intervals (?).

Another benefit of Doppler observations with high duty cycle is the reliable
inferences of subsurface flows which are crucial in understanding the plasma
motion below the surface and the solar dynamo. Several studies have been
carried out where torsional oscillation patterns (?, ?), subsurface zonal and
meridional flows (?, ?, ?), and deep meridional flows (?) have been reliably
inferred. To emphasize the importance of high-duty cycle in helioseismic
studies, we compare horizontal flows (Figure 23) computed utilizing the
technique of ring diagrams (?) where 1664 full-disk Doppler images taken at 1
min cadence are used. In the figure, arrows show the flow magnitude and the
direction, and the background depict the uncertainties in these estimates. The
left panel shows the horizontal flows with a 100% duty cycle while the middle
and right panels display the flows measured with reduced duty cycles of 79%
and 66%, respectively. Lower duty cycles are computed by truncating
observations from 100 % duty cycle data. From a comparison of these three
panels, we notice that the flow magnitudes as well as the errors increase with
the decrease of the duty cycles. Thus, the reliable flow measurements require
a higher duty cycle.

Figure 23: Effect of duty cycle on the subsurface flow measurements computed
using 1664 min GONG Dopplergrams with (Left) 100 %, (Middle) 79 %,and 66 %
(Right) duty cycles. Arrows show the flow magnitude and the direction, and the
background depict the uncertainties in these estimates.

GONG has also become an important data source for uninterrupted high-cadence
magnetic-field and H$\alpha$ observations, primarily due to significantly high
duty cycle. These are being used in various space-weather forecast systems (?,
?) and the studies related to flare and filament eruptions (?). In addition,
maps of the invisible-side (farside) of the Sun, computed from GONG
Dopplergrams, identifying active regions before they rotate towards Earth are
another important data product (?, ?). An example of consequences of low-duty
cycle in farside mapping is illustrated in Figure 24. Here we display farside
maps for three different duty cycles using 24-h GONG observations for June 21,
2014. Top panel shows the map from 100 % duty cycle as observed and other two
panels display maps for lower duty cycles by artificially introducing gaps in
the observations. It is clearly visible that reducing duty cycle reduces
signal to noise and also increases random noise. It is also important to note
that the uninterrupted synoptic observations are crucial in advancing our
knowledge of the phenomena related to solar activity whose origin is still not
fully understood (?).

Figure 24: Effect of duty cycle on the farside helioseismic maps computed
using 24 h GONG Dopplergrams with (top) 100 %, (Middle) 90 %, and (Bottom) 70
% duty cycles. Vertical lines show the boundaries between farside and nearside
(direct observations) hemispheres. Regions of high magnetic field, i.e.active
regions, are shown by the darker regions on both sides. Identified active
regions on the farside are marked with circles. It is clearly visible that the
low duty cycle increases noise in the farside maps and also reduces the signal
to noise.

##  8 Summary

Continuous observations of the Sun play an important role in the studies of
long-term as well as short-term solar variability. In this context, GONG
comprising of six ground-based observatories has been providing consistent and
continuous observations spanning over more than two solar cycles with a stable
temporal coverage. In this paper, we perform a detailed analysis of all the
six sites and assess the duty cycle that has been achieved by individual as
well as combined sites.

The study reveals that the maximum observations have been recorded at two
sites located in the southern hemisphere and the lowest number of observations
were obtained at the UD site, predominantly owing to the yearly site closure
during the Indian monsoon. Further, there is significant overlap between
observations from different sites, which is important for better calibration
and for creating a seamless, long time series by merging observations from
various sites. The mean and median duty cycle of the network is 88 % and 92 %
, respectively,

Our study further demonstrates that strong seasonal trends are present at all
sites except the Mauna Loa (ML) site, probably due to its geographical
location. However, these trends disappear in the network when the observations
from all sites are combined/merged. Although the down time of each site varies
from year to year, we do not find long-term change in climate at any of the
network sites that may affect the duty cycle.

It should be noted that the ground-based networks are less expensive as
compared to space missions and have the ease of repairing/upgrading any
instrument though the image quality might be affected by the atmospheric
seeing. The detailed analysis presented here demonstrates that a significantly
high duty cycle values can be achieved from a well-designed network of ground-
based observatories and can provide an important platform for long-term
synoptic observations.

The authors would like to thank Jack Harvey, John Leibacher, and Charles
Lindsey for critically reading the manuscript and their suggestions. K. Jain
thanks Valentín Martínez Pillet for many useful discussions. GONG has received
support from hundreds of people at the GONG headquarters at the National Solar
Observatory and around the world, and their dedication is pivotal for
maintaining the GONG operations for such a long time. The authors would like
to thank all those who are, or have been, associated with GONG from its
planning to the present. This work utilizes data from the National Solar
Observatory Integrated Synoptic Program, which is operated by the Association
of Universities for Research in Astronomy, under a cooperative agreement with
the National Science Foundation and with additional financial support from the
National Oceanic and Atmospheric Administration, the National Aeronautics and
Space Administration, and the United States Air Force. The GONG network of
instruments is hosted by the Big Bear Solar Observatory, High Altitude
Observatory, Learmonth Solar Observatory, Udaipur Solar Observatory, Instituto
de Astrofísica de Canarias, and the Cerro Tololo Interamerican Observatory.

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