Discovery of Regular Daily Ionospheric Scintillation

Abstract

The aim of this study was to find out whether, just like in March 2015, daily regular GPS positioning disturbances caused by ionospheric scintillations occurred in other months of the solar activity cycle 24. The GPS positioning 90-s kinematic solutions of selected 46 months covering 11 years were used to search for regular daily scintillation events. The hypothesis on predictable regular daily ionospheric scintillation was tested. Scintillation waves were discovered as a result of space weather impact with the sidereal day regularity. It leads to the conclusion that the radiation originates from the interplanetary medium. The enhancement of radiation waves by solar activity is similar to Pc1 waves. The regular daily ionospheric scintillation waves are recorded at any time of the day. In the years with low solar activity in 2010 and 2012, regular scintillation waves were not found. It cannot be claimed that the comparison of daily regular ionospheric scintillation cases over time with the mentioned Pc1 wave cases indicates any interrelation.

1. Introduction

Global Navigation Satellite Systems (GNSSs) have become so widely used that it is hard to imagine an industry in the national economy that does not use them. However, GNSS users and service providers that require high-precision positioning, navigation, and timing (PNT) understand that achieving high precision requires accounting for various nuances, including the impact of space weather. The aim of this study was to determine whether, as in March 2015, daily regular GPS positioning disturbances caused by ionospheric scintillations also occurred in other months of the solar activity cycle.

In this article, we discuss several topics related to GNSS positioning and navigation in the Introduction, and then delve into the daily ionospheric scintillation waves of interplanetary origin that cause positioning accuracy errors.

In [1], space weather was defined, and the primary phenomena that produce potential space weather impacting on GNSS applications in particular were described: the introduction of large gradients in the ionospheric total electron content (TEC), rapid variation in a signal’s amplitude and/or phase (scintillation), and/or a sudden increase in background noise.

Scintillation is caused by random density fluctuations of electrons and protons in the Earth’s ionosphere, the solar wind, or the interstellar medium [2]. Ionospheric scintillation, caused by irregularities in the ionosphere, can lead to rapid changes in signal amplitude and phase, particularly affecting GPS signals at high latitudes and during space weather events [3,4,5,6,7,8,9,10]. The presence of moderate to strong scintillation can double positioning errors and induce clustering effects on positioning solutions, complicating navigation tasks [4]. The positioning performance of GNSS is especially affected during severe geomagnetic storms, with studies indicating that the accuracy of precise point positioning (PPP) can be significantly reduced due to frequent cycle slips [5]. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry-Free (GF) cycle slip detection threshold during geomagnetic storms, indicating that GF is no longer applicable to high-precision services [11]. The correlation between geomagnetic storm indices and GNSS signal loss underscores the need for effective monitoring and forecasting to mitigate the impacts of space weather on navigation and positioning systems—a continuous concern for many users [6,12,13,14].

In combination with other instrumentation, scintillation monitoring is a powerful tool for investigating the formation and evolution of ionospheric structures such as spread-F and equatorial plasma bubbles [15]. Vasylyev et al. [2] described the history of research on the impacts of scintillation on various important services and remote sensing data products, with the theoretical modeling and computer simulation of these phenomena having attracted the attention of researchers for more than seventy years. Theoretical methods for modeling ionospheric scintillation phenomena have reached maturity over the last several decades. This progress is linked to the development of sophisticated numerical techniques based on the formalism of phase screens [2]. There are many models of ionospheric scintillation [16], which are used for scientific purposes as well as in services for scintillation prediction, determination of time periods and geographic areas with strong scintillation, and warning systems [2]. There are several scintillation models for GNSS applications, such as the Cornel model [17] and the multi-frequency GNSS scintillation model [18]. Given its practical importance, scintillation forecasting remains a key driver of modeling. Climatological models, such as WBMOD or GISM, can roughly predict scintillation levels [2]. Short-term prediction can be achieved by observing the time series of the model input, such as the F10:7 solar flux, and predicting future values, e.g., using exponential smoothing methods [19] and the BATS and TBATS models [20], to name a few. Machine learning and artificial neural networks also help to meet these demands [21,22]. Each year, more and more data are accumulated, enabling better insights into ionospheric conditions over solar cycles. The number of satellites and ground stations scanning the Earth’s ionosphere is also steadily increasing. The combination of data from multiple remote sensing sources, such as GNSS, SAR, and geostationary satellites, enhances understanding of the volumetric spatial variability of the ionosphere [2]. The development and deployment of high-resolution electron density monitoring instruments, such as EISCAT3D, global ultraviolet imagers (GUVIs), and special sensor ultraviolet spectrographic imagers (SSUSIs), are attractive for analyzing ionospheric irregularities with improved spatial and temporal resolution [2]. This, in turn, would facilitate a better understanding of the influence of ionospheric irregularities on radio-wave propagation in the disturbed ionosphere [2].

The approach for regional ionosphere modeling, based on undifferenced multi-GNSS carrier-phase data for TEC estimation and thin-plate splines for TEC interpolation, is described in [23].

Sun X. et al. [24] evaluated the International GNSS Service (IGS) 10-day continuous observations with eight combinations of BeiDou (BDS-3) and Galileo frequencies. The dual-frequency ionospheric-free (DFIF) Precise Point Positioning (PPP) in static and kinematic modes was implemented using the open-source Nett_Diff software v1.17 developed by the GNSS Analysis Center of the Shanghai Astronomical Observatory [24]. The best results were achieved with equal accuracy for BDS-3 only, B1C/B3I, and Galileo only, E1/E5 [24].

Space weather events such as the May 2024 Mother’s Day Superstorm induce hazardous perturbations in the thermosphere–ionosphere–magnetosphere system [25,26]. Filjar et al. [27] demonstrated that, in the case of a short-term fast-developing geomagnetic storm, a machine learning-based environment-aware GNSS ionospheric correction model for sub-equatorial regions may provide a substantial improvement over a global model.

Analyzing unique events such as the May 2024 Mother’s Day Superstorm offers valuable insight into the evolution of extreme geospace dynamics [5,25,26,28]. Several authors have studied the ionospheric response and the impact on GPS positioning accuracy during tropical cyclones [29,30] and volcanic eruptions [31].

An extended dataset of Loss-of-Lock (LoL) events recorded by the Swarm constellation from December 2013 to December 2020—the longest ever used—includes the corresponding occurrence as a function of latitude, local time, season, and solar activity [32]. In addition, the analysis aims to find a relationship between LoL occurrence and the defined values of the following two ionospheric indices: the rate of change in the electron density index (RODI) and the rate of change in the total electron content (TEC) index (ROTI). This research was performed both to characterize the background conditions of the ionosphere for such events and to aid in understanding whether these events can be forecasted, which would be crucial for mitigating space weather effects [32].

During the course of this study, it was revealed that disturbances caused by daily regular scintillations recur at intervals close to the length of a sidereal day. Given this, it can be concluded that the radiation causing scintillations originates in the interplanetary medium. The scientific literature also indicates that the origin of Pc1 wave radiation is in the interplanetary medium.

Fransia et al.’s [33] results show that there is a correspondence between Pc1 pulsation activity and the occurrence of ionospheric irregularities, as clearly shown in the analysis of ROT fluctuations; second, signatures of EMIC waves, driven by increased solar wind pressure, can also be observed at polar latitudes in both hemispheres simultaneously due to their propagation in the ionospheric waveguide. Such waves, generated just inside the magnetopause, propagate through the magnetosphere and transmit as Alfven waves along geomagnetic field lines up to the auroral ionosphere. They can precipitate magnetospheric energetic electrons into the atmosphere, causing electron/ion density variations as measured by TEC fluctuations. Fransia et al. [33] believe that the results of their work, although very interesting, should be substantiated by further investigations based on multiple events and additional data (satellite and/or riometer data). Third, these waves propagate along geomagnetic field lines and can reach the Earth’s ionosphere, where they influence ionospheric conductivity and electron density. Pc1 waves can modulate ionospheric total electron content (TEC), leading to irregularities and affecting radio signals [33]. Geomagnetic measurements from ground and space represent the main ingredient in modeling and understanding the Earth’s magnetic field [34].

Space weather issues related to PNT are relevant worldwide [28,35]. The Global Positioning System (GPS) plays an important role in navigation and positioning services. When GPS signals propagate through a complex space environment, they are susceptible to ionospheric scintillation interference. As one of the largest interference sources in GPS navigation and positioning, ionospheric scintillation causes signal intensity decline and carrier phase fluctuations, making signal acquisition for the GPS receiver challenging [36].

The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information [37]. The significant research centers for geomagnetism, space physics, and space weather in Europe are the ESA space weather network and the GFZ Helmholtz Centre for Geosciences in Potsdam, Germany. Studies have also been conducted in Latvia on the impact of space weather on GNSS measurements [38]. The second section of this study provides a brief overview of the study’s raw data, focusing on March 2015 data that clearly illustrate the presence of regular daily ionospheric scintillation waves. Further analysis of datasets and their reduction formulas is presented. The third section presents the results of the data analysis and reflects the characteristics of the obtained results. The fourth section discusses the results obtained and compares them with geomagnetic measurements of the Pc1 waves mentioned in the literature. Additional information about the example, including a summary of six searches of daily regular scintillation waves for June 2014, is provided in Appendix A, and a list of daily regular ionospheric scintillation events is provided in Supplementary Materials.

2. Data and Methods

In this study, the hypothesis regarding the possible regular daily ionospheric scintillation disturbances affecting GPS positioning observations was tested in 46 selected months of the 24th solar activity cycle (2007–2017).

GPS observation data from Latvian Continuously Operating GPS Station (CORS) networks were used for double-difference (DD) post-processing in the Bernese GNSS Software v5.2, with an elevation cut-off angle of 15˚ for 90-s (30 s sampling data) intervals of kinematic post-processing to identify disturbed results. Information on ionospheric TEC levels was obtained from publicly available datasets. The programs RNXSMT (Clean RINEX Data, Smooth Code Observations) and MAUPRP (automatic phase pre-processing, cycle slip detection and correction, outlier detection, and Ambiguity List update) in the Bernese GNSS Software 5.2 were used for cycle slip detection. The MAUPRP program was also used to repair cycle slips. The outputs from both programs were used to find detected cycle slips for each station and baseline. The FES2004 ocean tidal model was used, along with the correction of the solid Earth tide effect. The Dry Global Mapping Function (DRY-GMF) was used for tropospheric delay modeling. The maximum size of accepted cycle slip corrections was 10. The solutions were achieved in subgroups of 4–5 Latvian CORS, using the same IGS/EPN reference stations. The resulting post-processed kinematic solution data were used for further analysis with the authors’ software programs [39].

2.1. Ionospheric Scintillation Waves

Regular daily disturbances were observed at Latvian CORSs in March 2015 [39]. A graphical image of the information subset of the ionospheric scintillation wave is depicted in Figure 1.

The first row presents information on the number of scintillation events, the date and time, and a list of Latvian CORS DOMEs where ionospheric scintillation causing a 3D positioning discrepancy ≥ 10 cm occurred simultaneously. The second row represents information on the next event, which occurred within the next 90 s. The various subsets of stations represent the area of the station network impacted by different sizes of electron clouds. Therefore, the information subset of each row is called a cloud, and the whole sequence of uninterrupted 90 sec events of ionospheric scintillation is called a wave (evil wave [28]). Over the course of a day, wave counts may vary depending on solar activity.

A subset of any wave $w_k$ is described in Formula (1) as a union of subsets of scintillation clouds with their date and time and GPS position discrepancy data.

$$\begin{array}{cc}{w}_k:=& \cup \{\mathrm{‹}{k}_i,D_k,{t_{k_i},s}_{k_{ij}}{,d}_{k_{i}j},r_{k_{i}j},w_k^{\prime }\mathrm{›}:j∊\mathrm{‹}{D}_k,t_{k_i}\mathrm{›},n_{k_i}∊\mathrm{‹}{D}_k,t_{k_i}\mathrm{›},1\le \mathrm{i}\\ & \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\le n_k,1\le \mathrm{j}\le {|S}_{k_i}\left|\right\}\hfill \end{array}$$

(1)

where $k_i$ is the number of recorded scintillation waves on date $D_k$ and $k_i\mathrm{r}\mathrm{e}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{s}$ cloud’s time $t_{k_i}$; $s_{k_{i}j}$ is a subset of station DOME names in each row’s subsets $S_{k_i}$ with cardinality ${|S}_{k_i}|$, where simultaneously fixed scintillations occur. $S_{k_i}$ subsets are called clouds, similarly to electron clouds, in the impacted area of the station subset of cardinality j. The 3D positioning discrepancy subset is represented by $d_{k_{i}j}$, and a subset of the Rate of the Total Electron Content Index (ROTI) is denoted by $r_{k_{i}j}$. Later in this article, the tuple $w_k^{\prime }$ of Modified Julian Days (MJD) denoted by Formula (2), where day and time are converted to MJD, is described. The cardinality of subset $w_k$ is $n_k$, and the count of CORSs in each $k_i$—cloud is ${|S}_{k_i}|$. The number of waves per day varies, and the number of clouds in each wave also varies. Therefore, based on a data-driven approach, the actual spatiotemporally matched structure of the electron cloud can be inferred from these patterns on the ground [40].

2.2. Graphical Representation of the Registered Ionospheric Wave Sets over the Month

The occurrence of monthly ionospheric scintillation waves in March 2015 is depicted in Figure 2. There are visible types (in the form of peaks) of waves depicted in Figure 1, which clearly repeat daily. The largest peak occurred on 17 March 2015, during the geomagnetic storm on St Patrick’s Day.

More complicated scintillation events in August 2007 are depicted in Figure 3. There are several scintillation waves in some days and no scintillation in some other days.

Figure 4 depicts scintillation events in July 2016. On the surface, it is hard to say whether scintillation waves recurred daily during the month.

The total number of waves is 6718, and the number of clouds is 84,827, as listed in

Table 1. Monthly ionospheric wave impact with a minimum of 2 clouds.

#	Year	Month	Clouds	Waves	#	Year	Month	Clouds	Waves
1	2007	FEB	1569	120	24	2012	OCT	837	88
2	2007	MAY	4359	308	25	2013	MAY	1587	201
3	2007	JUN	3501	272	26	2013	OCT	3772	152
4	2007	AUG	5830	491	27	2013	NOV	935	114
5	2008	MAR	726	69	28	2013	DEC	1155	136
6	2008	JUN	1600	140	29	2014	FEB	1268	99
7	2008	SEP	1986	193	30	2014	JUN	3393	295
8	2008	OCT	1328	107	31	2014	OCT	1241	117
9	2009	JUL	2473	216	32	2014	DEC	1837	126
10	2009	AUG	1413	126	33	2015	MAR	1584	119
11	2009	OCT	1304	107	34	2015	MAY	1749	170
12	2009	DEC	2056	115	35	2015	JUN	2499	154
13	2010	JAN	255	20	36	2015	OCT	880	118
14	2010	FEB	1123	66	37	2015	DEC	988	119
15	2010	APR	449	59	38	2016	FEB	1166	104
16	2010	NOV	1263	45	39	2016	APR	1401	105
17	2011	MAR	747	48	40	2016	MAY	2541	175
18	2011	AUG	1943	248	41	2016	JUL	3445	339
19	2011	SEP	1477	134	42	2017	APR	1980	120
20	2011	NOV	523	77	43	2017	MAY	2894	136
21	2012	JAN	366	35	44	2017	JUL	6442	214
22	2012	MAR	461	56	45	2017	SEP	1181	126
23	2012	JUL	2155	227	46	2017	OCT	1145	112

.

It is necessary to select an algorithm to detect the presence of regular waves in these monthly data subsets. Based on the example of March 2015, daily regular waves are repeated with a 4.5-min lag on a time scale graduated in 1.5-min increments. For better transparency, the count of clouds and waves is depicted in Figure 5.

2.3. The Classification of the Characteristic Elements of the Waves of Ionospheric Scintillation Clouds

Figure 6 once again shows a scintillation k–wave and sketches its details, which are used in the subsequent analysis.

Additionally, for the elements of each wave with index k, the information subset of the tuples of Modified Julian Days (MJD) is introduced, where MJD for the peak cloud is denoted by $p_k$, beginning cloud is denoted by $b_k$, end cloud is denoted by $e_k$, middle cloud is denoted by $g_k$, wavelength is denoted by $l_k$, and station count in peak cloud is denoted by $\left|S_{k_i}\right|$. k is the No. of waves in month, $c_1$ is the recorded No. of the first wave’s cloud, and $n_k\mathrm{i}\mathrm{s}$ count of clouds in a wave. Initially, it was planned to use the differences between the element values of adjacent waves (denoted by o); however, in practice, this turned out to be insignificant.

$$\begin{array}{cc}{W}^{\prime }:=& {\displaystyle \bigcup _{k=1}^{\left|W^{\prime }\right|}}\{(k,{c_1,b}_k,e_k,p_k,g_k,l_k,o_k^b,o_k^e{o_k^p,o_k^g,n}_k,max|S_{k_i}|)∊w_k,n_{k_i}\\ & \hspace{1em}\hspace{1em} \hspace{1em}∊w_k,1\le \mathrm{i}\le n_k\}\hfill \end{array}$$

(2)

An example from such a subset of tuples is shown in

Table 2. An example of the subset of tuples of wave information (P is $max\left|S_{k_i}\right|)$.

$\mathit{k}$	${\mathit{c}}_1$	b	e	p	$\mathit{g}$	l	${\mathit{o}}^{\mathit{b}}$	${\mathit{o}}^{\mathit{e}}$	${\mathit{o}}^{\mathit{p}}$	${\mathit{o}}^{\mathit{g}}$	n	P
50 WAVE	516	57,092.991667	57,092.998958	PK 57,092.992708	MED 57,092.995313	DIFF 0.007291	0.002084	0.009375	0.003125	0.005730	PK 8	3
51 WAVE	525	57,093.534375	57,093.581250	PK 57,093.534375	MED 57,093.557813	DIFF 0.046875	0.542708	0.582292	0.541667	0.562500	PK 3	2
52 WAVE	534	57,093.817708	57,093.894792	PK 57,093.817708	MED 57,093.856250	DIFF 0.077084	0.283333	0.313542	0.283333	0.298437	PK 2	3
53 WAVE	540	57,093.902083	57,093.906250	PK 57,093.903125	MED 57,093.904166	DIFF 0.004167	0.084375	0.011458	0.085417	0.047916	PK 4	4
54 WAVE	551	57,093.938542	57,093.952083	PK 57,093.941667	MED 57,093.945312	DIFF 0.013541	0.036459	0.045833	0.038542	0.041146	PK 14	15
55 WAVE	569	57,093.986458	57,093.997917	PK 57,093.990625	MED 57,093.992188	DIFF 0.011459	0.047916	0.045834	0.048958	0.046875	PK 12	4
56 WAVE	608	57,094.932292	57,094.951042	PK 57,094.938542	MED 57,094.941667	DIFF 0.018750	0.945834	0.953125	0.947917	0.949479	PK 19	27
57 WAVE	649	57,094.975000	57,094.976042	PK 57,094.975000	MED 57,094.975521	DIFF 0.001042	0.042708	0.025000	0.036458	0.033854	PK 2	2

for 11–13 March 2015.

2.4. Characteristics of Regular Daily Wave Elements in March 2015

The duration of the wave is called the length of the wave in this article. The length of waves varies in size depending on the annual season and solar activity level. In March 2015, the regularity of waves is clearly visible (Figure 2). However, regular daily waves are unclear in Figure 3 or in Figure 4.

With a 4.5-min lag in adjacent days of March 2015, each of the 1.5-min (90 s) clouds shifts. However, the time lag for each of these proper clouds (peak, med, and others) varies with changes in the daily wave configuration. Importantly, the times of regular daily repetition are variable.

Table 3. Regular daily wave characterization parameters (min) in March 2015 (p − b = peak − beg; m − p = med − peak; p − e = peak − end).

Date	MJD	Shift	Peak	Med	Beg	Length	p − b	m − p	p − e
2	57,083.969792		−0.1	−4	−10.2	42	24	−3	18
3	57,084.966667	−4.5	−0.5	−5.5	−9.5	37.5	22.5	−3.7	15
4	57,085.963542	−4.5	−0.9	−1	−2.7	33	15	1.5	18
5	57,086.961458	−3.0	0.2	−1	−0.4	28.5	13.5	0.8	15
6	57,087.959375	−3.0	1.3	0.5	1.9	27	12	1.5	15
7	57,088.954167	−7.5	−2.1	4.3	7.1	24	3	9	21
8	57,089.954167	0.0	2.1	1.3	4.9	22.5	9	2.2	13.5
9	57,090.948958	−7.5	−1.3	1.3	7.2	18	3	6	15
10	57,091.946875	−3.0	−0.2	1.3	5	22.5	6	5.3	16.5
11	57,092.944792	−3.0	0.9	1.4	5.8	21	6	4.5	15
12	57,093.941667	−4.5	0.5	1.4	6.5	19.5	4.5	5.2	15
13	57,094.938542	−4.5	0.1	−0.1	1.3	27	9	4.5	18
14	57,095.934375	−6.0	−1.8	−1.6	3.6	19.5	4.5	5.3	15
15	57,096.933333	−1.5	0.8	−0.1	4.4	21	6	4.5	15
16	57,097.930208	−4.5	0.4	−0.1	3.7	22.5	6	5.3	16.5
17	57,098.928125	−3.0	1.6	29.9	−22.6	135	33	34.5	102
18	57,099.923958	−6.0	−0.3	−6.1	−6.8	31.5	15	0.8	16.5
19	57,100.922917	−1.5	2.3	−3	−1.5	27	12	1.5	15
20	57,101.917708	−7.5	−1.1	−5.3	−3.7	27	10.5	3	16.5
21	57,102.914583	−4.5	−1.5	−3	0.1	24	6	6	18
22	57,103.912500	−3.0	−0.4	−1.5	3.8	19.5	3	6.8	16.5
23	57,104.910417	−3.0	0.7	−4.5	0.1	21	7.5	3	13.5
24	57,105.906250	−6.0	−1.2	−2.2	2.4	21	3	7.5	18
25	57,106.905208	−1.5	1.5	−2.2	1.7	22.5	6	5.3	16.5
26	57,107.901042	−6.0	−0.4	−4.4	−0.5	22.5	6	5.2	16.5
27	57,108.898958	−3.0	0.7	−3.7	1.7	19.5	4.5	5.3	15
28	57,109.895833	−4.5	0.3	−4.4	1	19.5	4.5	5.3	15
29	57,110.892708	−4.5	−0.1	−5.2	−1.2	22.5	6	5.3	16.5
30	57,111.889583	−4.5	−0.5	21.9	−3.4	81	7.5	33	73.5
31	57,112.886458	−4.5	−0.9	−4.4	0.3	21	3	7.5	18

shows how the parameters of daily waves are changing. The shift time is provided in minutes for the peak cloud. Uncertainties (dpeak) in time minutes (min) are obtained as a result of the linear approximation of daily peak times. Similarly, linear approximation is performed for the median (dmed) and beginning (dbeg). The length of the wave in minutes means the duration of the GPS positioning discrepancies. Time differences for the peak minus beginning (peak − beg), median minus peak (med − peak), and end–peak (end − peak) characterize the changing shape of the waves.

The root mean square error (RMS) of linear approximation is 1.12 min for the time of occurrence of the peak cloud (peak), 7.75 min for the cloud in the middle of the time sequence (further median—med), and 6.15 min for the time sequence beginning of the wave (further beginning—beg). The linear approximation errors of the daily time are depicted in Figure 7. The peak’s daily repetition is most regular. The monthly mean value of the length of wave (Length) is 30.0 min, and the mean value without outliers is 24.4 min (Figure 7). The mean time difference from the beginning to the peak is 9.05 min, that from the peak to the middle of the wave is 5.95 min, and that from the peak to the end of the wave is 20.95 min. This means that, on average, the peak of the ionospheric wave is closer to the beginning, but the section from the peak to the end is 2 times as stretched. Daily regular waves repeated with a –4.11 min lag for the peak, –3.76 min for the wave median, and –3.78 min for the beginning of the wave.

Variation in the length of regular waves is depicted in Figure 8. It is later shown in this study that the wavelength changes with solar activity. This is also shown in Figure 8, where the longest wavelength occurs on the day of the geomagnetic storm, March 17. The geomagnetic storm appears in the Latvian CORS observations from 14:27 UT to 18:18 UT, while the regular daily scintillation wave is from 21:43:30 UT to 23:58:30 UT.

Since the time lag of the peak indicates the most stable regularity, it is helpful to conduct a search for the next regular daily wave in relation to the forecast of the next day of the peak time. In order to search for the peak on the next day, it was necessary to consider both the fluctuation of the lag time and the discrepancy between the gradation of the measurement time, 1.5 min (see Figure 1), and the regular predicted daily time lag, –4.11 min.

When the study began, it was thought that there might also be regular waves of GPS interference in a few other months. However, the example from March 2015 sparked interest in exploring the regularity of positioning disorders in other months.

2.5. Search for Regular Daily Waves

The monthly set of regular daily scintillation waves is

$$W:=\cup \left\{w_k:\exists p_k\exists p_k^{\prime }\mathrm{a}\mathrm{b}\mathrm{s}(p_k-p_k^{\prime })\le c^{\prime },p_k,p_k^{\prime }∊w_k,1\le \mathrm{k}\le 31\right\}$$

(3)

$$p_k^{\prime }=p_{k-1}+\mathrm{const}$$

(4)

where $c^{\prime }$ is a threshold indicating uncertainty in the constant for the daily regularity of the peak cloud $p_k$ occurrence time. For the initial search test, sets of CORS GPS measurement errors for the months of October and December 2014 were selected. A cloud of the largest cardinality of the first wave of the first day of the month was chosen as the starting point. The search process confirmed the daily variation in the timing of the wave beginnings, peaks, and midpoints. The results of the experimentation will be reflected in the next section of the article.

The final test was based on the assumption that the predicted regular wave of the next day partially overlaps the regular wave of the current day in terms of time in minutes and seconds, unless one of the regular wavelengths of the comparable adjacent days is less than a certain time limit.

$$\begin{array}{cc}{W}_{tested}:=& \cup \left\{\right(w_i,w_j):(\left(t_{j_1}^{\prime }<t_{i_l}\right)\wedge \left(t_{j_l}^{\prime }>t_{i_1}\right)),t_{i1},t_{il}∊D_i,\\ & \hspace{1em}\hspace{1em}{w}_j{,t}_{j_1}^{\prime },t_{j_l}^{\prime }∊D_{i+1},1\le \mathrm{i}\le \left|w_i\right|,1\le \mathrm{j}\le \left|w_j\right|\}\hfill \end{array}$$

(5)

$$t_j^{\prime }=t_i+\mathrm{const}$$

(6)

where $t_{i_1}$ indicates the time of the first cloud of the wave; $t_{i_l}$ indicates that of the last cloud of the wave; $\left|w_i\right|$ indicates cardinality, representing the number of clouds in the current day; and $t_{j_1}^{\prime }$ and $t_{j_l}^{\prime }$ indicate the wave found for the next day, correspondingly. Const indicates a daily lag in time changes in the regular waves that occurred in adjacent days. The value of Const is discussed in the Discussion section (Section 4).

2.6. ROTI@ground and Positioning Errors

In conclusion to this analysis, for a few months, the average values of ROTI@ground and the affected CORS average positioning errors were calculated to gain insight into what impact these regular daily scintillation waves had on positioning accuracy.

The sample of discrepancies in GPS positioning for all the stations of one cloud (Northing—dN, Easting—dE, height—dh, 3D error, azimuth—Az, and rate of change in total electron content index—ROTI, with a time resolution of 8 min) is shown in

Table 4. Example of discrepancies and ROTI for each cloud’s station.

#	Date	Time UT	DOME	#	dN	dE	dh	3D	Az	ROTI
258	2015 MAR 5	22:58:30 UT	IRBE	1211	0.017	0.004	0.105	0.106	13.2	0.0394
			OJAR	1214	−0.005	−0.007	−0.110	0.110	−125.5	0.0403
			DAU1	1210	0.024	0.003	0.115	0.118	7.1	0.0384
			KREI	1212	−0.029	−0.002	−0.150	0.153	−176.1	0.0403
			VANG	1215	0.043	0.006	0.154	0.160	7.9	0.0403
			MAZS	1213	0.059	−0.001	0.219	0.227	−1.0	0.0407
			ALUK	1209	−0.076	0.009	−0.236	0.248	173.2	0.0403

. Both the results of ROTI@ground and the average positioning error were computed for each daily regular wave. The results will be discussed in the following sections, and the input data sample for each station at a fixed time is shown in Table 4.

3. Results

By analogy with March 2015, the search for regular waves in other months began with the first wave of the first day of the month. The results were disappointing. Then, the search began with the largest wave of the first day, and then with the highest peak of the first day. The number of waves on the first day of a few months was unexpectedly large, and it was necessary to change the initial search parameters in the software program. Several search parameters were improved step by step and, as a result, the search was conducted six times with both different and matching results.

Doubts and disbelief about the regularity of scintillation led to its existence being repeatedly tested. The search conditions were not fundamentally different. The differences were only the change in the choice of the starting point cloud or in the choice of the regular daily repetition time shift constant (Const). By changing the Const value, the number of identified waves per month changed. In the search process with a different Const value, a peak cloud was always the starting point. However, in search of the subsequent day’s wave, a different cloud of the same wave was used instead of the peak cloud because of its fluctuating time. In months with shorter wavelengths, the search process collapsed, as the accepted time Const shifted beyond the limits of the next wave. In Figure 9, the fluctuation in the time of peak cloud and the subsequent variation in Const value are depicted. The dashed line shows the mean Const that is close to the length of the sidereal day.

The results of the experiments were summarized and are shown in Figure 10 and

Table A1. Summary of six searches of daily regular scintillation waves for June 2014. For example, the regular cloud on 2 June 2014 was identified in columns 1, 3, 4, and 5. There were 26 stations in a cloud.

#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
135	56,809.751042	2014 JUN	1	18:1:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
136	56,809.752083	2014 JUN	1	18:3:0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
137	56,809.753125	2014 JUN	1	18:4:30	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
138	56,809.754167	2014 JUN	1	18:6:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
139	56,809.755208	2014 JUN	1	18:7:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
140	56,809.756250	2014 JUN	1	18:9:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
141	56,809.757292	2014 JUN	1	18:10:30	22	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
142	56,809.758333	2014 JUN	1	18:12:0	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
143	56,809.844792	2014 JUN	1	20:16:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time lag
135	56,809.751042	2014 JUN	2	18:1:30								0.0	0.0	0.0	0.0	0.0	0.0
241	56,810.743750	2014 JUN	2	17:51:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
242	56,810.744792	2014 JUN	2	17:52:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
243	56,810.745833	2014 JUN	2	17:54:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
244	56,810.746875	2014 JUN	2	17:55:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
245	56,810.747917	2014 JUN	2	17:57:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
246	56,810.748958	2014 JUN	2	17:58:29	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
247	56,810.750000	2014 JUN	2	18:0:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
248	56,810.751042	2014 JUN	2	18:1:30	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
249	56,810.752083	2014 JUN	2	18:3:0	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
250	56,810.753125	2014 JUN	2	18:4:30	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
251	56,810.754167	2014 JUN	2	18:6:0	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
252	56,810.755208	2014 JUN	2	18:7:29	26	26	0	26	26	26	0	−4.5	0.0	−4.5	0.2	−4.5	0.0
253	56,810.756250	2014 JUN	2	18:9:0	26	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
254	56,810.757292	2014 JUN	2	18:10:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
565	56,811.740625	2014 JUN	3	17:46:29	2							0.0	0.0	0.0	0.0	0.0	0.0
566	56,811.741667	2014 JUN	3	17:48:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
567	56,811.743750	2014 JUN	3	17:51:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
568	56,811.744792	2014 JUN	3	17:52:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
569	56,811.745833	2014 JUN	3	17:54:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
570	56,811.746875	2014 JUN	3	17:55:29	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
571	56,811.747917	2014 JUN	3	17:57:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
572	56,811.748958	2014 JUN	3	17:58:29	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
573	56,811.750000	2014 JUN	3	18:0:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
574	56,811.751042	2014 JUN	3	18:1:30	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
575	56,811.752083	2014 JUN	3	18:3:0	11	11	0	11	0	11	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
576	56,811.753125	2014 JUN	3	18:4:30	22	0	0	0	22	0	0	0.0	0.0	0.0	1.3	0.0	0.0
577	56,811.754167	2014 JUN	3	18:6:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
725	56,812.740625	2014 JUN	4	17:46:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
726	56,812.741667	2014 JUN	4	17:48:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
727	56,812.742708	2014 JUN	4	17:49:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
728	56,812.743750	2014 JUN	4	17:51:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
729	56,812.744792	2014 JUN	4	17:52:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
730	56,812.745833	2014 JUN	4	17:54:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
731	56,812.746875	2014 JUN	4	17:55:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
732	56,812.747917	2014 JUN	4	17:57:0	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
733	56,812.748958	2014 JUN	4	17:58:29	18	18	0	18	18	18	0	−4.5	0.0	−4.5	−1.7	−4.5	0.0
734	56,812.75000	2014 JUN	4	18:0:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
735	56,812.751042	2014 JUN	4	18:1:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
736	56,812.752083	2014 JUN	4	18:3:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
737	56,812.753125	2014 JUN	4	18:4:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
738	56,812.756250	2014 JUN	4	18:9:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
739	56,812.757292	2014 JUN	4	18:10:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
740	56,812.758333	2014 JUN	4	18:12:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
741	56,812.773958	2014 JUN	4	18:34:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
917	56,813.734375	2014 JUN	5	17:37:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
918	56,813.735417	2014 JUN	5	17:39:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
919	56,813.736458	2014 JUN	5	17:40:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
920	56,813.737500	2014 JUN	5	17:42:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
921	56,813.738542	2014 JUN	5	17:43:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
922	56,813.739583	2014 JUN	5	17:45:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
923	56,813.739583	2014 JUN	5	17:46:29	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
924	56,813.741667	2014 JUN	5	17:48:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
925	56,813.742708	2014 JUN	5	17:49:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
926	56,813.743750	2014 JUN	5	17:51:0	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
927	56,813.744792	2014 JUN	5	17:52:30	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
928	56,813.745833	2014 JUN	5	17:54:0	16	16	0	16	0	16	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
929	56,813.746875	2014 JUN	5	17:55:29	22	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
930	56,813.747917	2014 JUN	5	17:58:29	27	0	0	0	27	0	0	0.0	0.0	0.0	2.8	0.0	0.0
931	56,813.748958	2014 JUN	5	17:58:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
932	56,813.750000	2014 JUN	5	18:0:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
933	56,813.751042	2014 JUN	5	18:1:30	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
934	56,813.752083	2014 JUN	5	18:3:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
935	56,813.753125	2014 JUN	5	18:4:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
936	56,813.754167	2014 JUN	5	18:6:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
937	56,813.755208	2014 JUN	5	18:7:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
938	56,813.756250	2014 JUN	5	18:9:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1151	56,814.733333	2014 JUN	6	17:36:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1152	56,814.734375	2014 JUN	6	17:37:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1153	56,814.735417	2014 JUN	6	17:39:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1154	56,814.736458	2014 JUN	6	17:40:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1155	56,814.737500	2014 JUN	6	17:42:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1156	56,814.738542	2014 JUN	6	17:43:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1157	56,814.739583	2014 JUN	6	17:45:0	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1158	56,814.740625	2014 JUN	6	17:46:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1159	56,814.741667	2014 JUN	6	17:48:0	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1160	56,814.742708	2014 JUN	6	17:49:29	16	16	0	16	0	16	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1161	56,814.743750	2014 JUN	6	17:51:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1162	56,814.744792	2014 JUN	6	17:52:30	24	0	0	0	24	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
1163	56,814.748958	2014 JUN	6	17:58:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1164	56,814.750000	2014 JUN	6	18:0:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1203	56,815.734375	2014 JUN	7	17:37:30	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1204	56,815.735417	2014 JUN	7	17:39:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1205	56,815.736458	2014 JUN	7	17:40:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1206	56,815.737500	2014 JUN	7	17:42:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1207	56,815.738542	2014 JUN	7	17:43:30	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1208	56,815.739583	2014 JUN	7	17:45:0	12	12	0	12	0	12	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1209	56,815.740625	2014 JUN	7	17:46:29	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1210	56,815.741667	2014 JUN	7	17:48:0	26	0	0	0	26	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
1211	56,815.742708	2014 JUN	7	17:49:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1297	56,816.729167	2014 JUN	8	17:30:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1298	56,816.730208	2014 JUN	8	17:31:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1299	56,816.731250	2014 JUN	8	17:33:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1300	56,816.732292	2014 JUN	8	17:34:30	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1301	56,816.733333	2014 JUN	8	17:36:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1302	56,816.734375	2014 JUN	8	17:37:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1303	56,816.735417	2014 JUN	8	17:39:0	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1304	56,816.736458	2014 JUN	8	17:40:29	18	18	0	18	0	18	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1305	56,816.737500	2014 JUN	8	17:42:0	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1306	56,816.738542	2014 JUN	8	17:43:30	20	0	0	0	20	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
1307	56,816.768750	2014 JUN	8	18:27:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1308	56,816.769792	2014 JUN	8	18:28:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1382	56,817.700000	2014 JUN	9	16:48:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1383	56,817.701042	2014 JUN	9	16:49:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1384	56,817.702083	2014 JUN	9	16:51:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1385	56,817.703125	2014 JUN	9	16:52:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1386	56,817.704167	2014 JUN	9	16:54:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1387	56,817.705208	2014 JUN	9	16:55:29	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1388	56,817.706250	2014 JUN	9	16:57:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1389	56,817.707292	2014 JUN	9	16:58:30	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1390	56,817.708333	2014 JUN	9	17:60: 0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1391	56,817.709375	2014 JUN	9	17:1:29	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1392	56,817.710417	2014 JUN	9	17:3:0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1393	56,817.711458	2014 JUN	9	17:4:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1394	56,817.712500	2014 JUN	9	17:6:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1395	56,817.713542	2014 JUN	9	17:7:30	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1396	56,817.714583	2014 JUN	9	17:9:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1397	56,817.715625	2014 JUN	9	17:10:29	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1398	56,817.716667	2014 JUN	9	17:12:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1399	56,817.717708	2014 JUN	9	17:13:29	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1400	56,817.718750	2014 JUN	9	17:15:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1401	56,817.719792	2014 JUN	9	17:16:30	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1402	56,817.720833	2014 JUN	9	17:18:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1403	56,817.721875	2014 JUN	9	17:19:30	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1404	56,817.722917	2014 JUN	9	17:21:0	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1405	56,817.723958	2014 JUN	9	17:22:29	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1406	56,817.725000	2014 JUN	9	17:24:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1407	56,817.726042	2014 JUN	9	17:25:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1408	56,817.727083	2014 JUN	9	17:27:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1409	56,817.728125	2014 JUN	9	17:28:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1410	56,817.729167	2014 JUN	9	17:30:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1411	56,817.730208	2014 JUN	9	17:31:29	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1412	56,817.731250	2014 JUN	9	17:33:0	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1413	56,817.732292	2014 JUN	9	17:34:30	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1414	56,817.733333	2014 JUN	9	17:36:0	23	23	0	23	0	23	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1415	56,817.734375	2014 JUN	9	17:37:30	24	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1416	56,817.735417	2014 JUN	9	17:39:0	27	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1417	56,817.736458	2014 JUN	9	17:40:29	29	0	0	0	29	0	0	0.0	0.0	0.0	1.3	0.0	0.0
1418	56,817.743750	2014 JUN	9	17:51:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1520	56,818.723958	2014 JUN	10	17:22:29	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1521	56,818.725000	2014 JUN	10	17:24:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1522	56,818.726042	2014 JUN	10	17:25:30	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1523	56,818.727083	2014 JUN	10	17:27:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1524	56,818.728125	2014 JUN	10	17:28:30	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1525	56,818.729167	2014 JUN	10	17:30:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1526	56,818.730208	2014 JUN	10	17:31:29	18	18	0	18	0	18	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1527	56,818.731250	2014 JUN	10	17:33:0	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1528	56,818.732292	2014 JUN	10	17:34:30	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1529	56,818.733333	2014 JUN	10	17:36:0	24	0	0	0	24	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
1530	56,818.734375	2014 JUN	10	17:37:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1531	56,818.735417	2014 JUN	10	17:39:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
1578	56,819.720833	2014 JUN	11	17:18:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1579	56,819.721875	2014 JUN	11	17:19:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1580	56,819.722917	2014 JUN	11	17:21:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1581	56,819.723958	2014 JUN	11	17:22:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1582	56,819.725000	2014 JUN	11	17:24:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1583	56,819.726042	2014 JUN	11	17:25:30	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1584	56,819.727083	2014 JUN	11	17:27:0	19	19	0	19	0	19	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
1585	56,819.728125	2014 JUN	11	17:28:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1586	56,819.729167	2014 JUN	11	17:30:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
1587	56,819.730208	2014 JUN	11	17:31:29	22	0	0	0	22	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
1588	56,819.737500	2014 JUN	11	17:42:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2049	56,820.718750	2014 JUN	12	17:15:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2050	56,820.719792	2014 JUN	12	17:16:30	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2051	56,820.720833	2014 JUN	12	17:18:0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2052	56,820.721875	2014 JUN	12	17:19:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2053	56,820.722917	2014 JUN	12	17:21:0	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2054	56,820.723958	2014 JUN	12	17:22:29	14	14	0	14	0	14	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2055	56,820.725000	2014 JUN	12	17:24:0	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2056	56,820.726042	2014 JUN	12	17:25:30	25	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2057	56,820.727083	2014 JUN	12	17:27:0	24	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2058	56,820.728125	2014 JUN	12	17:28:30	26	0	0	0	26	0	0	0.0	0.0	0.0	1.3	0.0	0.0
2059	56,820.729167	2014 JUN	12	17:30:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2060	56,820.730208	2014 JUN	12	17:31:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2061	56,820.732292	2014 JUN	12	17:34:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2062	56,820.734375	2014 JUN	12	17:37:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2196	56,821.707292	2014 JUN	13	16:58:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2197	56,821.708333	2014 JUN	13	17:60: 0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2198	56,821.709375	2014 JUN	13	17:1:29	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2199	56,821.710417	2014 JUN	13	17:3:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2200	56,821.711458	2014 JUN	13	17:4:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2201	56,821.712500	2014 JUN	13	17:6:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2202	56,821.713542	2014 JUN	13	17:7:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2203	56,821.714583	2014 JUN	13	17:9:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2204	56,821.715625	2014 JUN	13	17:10:29	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2205	56,821.716667	2014 JUN	13	17:12:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2206	56,821.717708	2014 JUN	13	17:13:29	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2207	56,821.718750	2014 JUN	13	17:15:0	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2208	56,821.719792	2014 JUN	13	17:16:30	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2209	56,821.720833	2014 JUN	13	17:18:0	20	20	0	20	0	20	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2210	56,821.721875	2014 JUN	13	17:19:30	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2211	56,821.722917	2014 JUN	13	17:21:0	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2212	56,821.723958	2014 JUN	13	17:22:29	25	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2213	56,821.725000	2014 JUN	13	17:24:0	26	0	0	0	26	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
2214	56,821.727083	2014 JUN	13	17:27:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2215	56,821.753125	2014 JUN	13	18:4:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2216	56,821.754167	2014 JUN	13	18:6:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2390	56,822.694792	2014 JUN	14	16:40:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2391	56,822.695833	2014 JUN	14	16:42:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2392	56,822.696875	2014 JUN	14	16:43:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2393	56,822.697917	2014 JUN	14	16:45:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2394	56,822.698958	2014 JUN	14	16:46:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2395	56,822.700000	2014 JUN	14	16:48:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2396	56,822.701042	2014 JUN	14	16:49:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2397	56,822.702083	2014 JUN	14	16:51:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2398	56,822.703125	2014 JUN	14	16:52:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2399	56,822.704167	2014 JUN	14	16:54:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2400	56,822.705208	2014 JUN	14	16:55:29	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2401	56,822.706250	2014 JUN	14	16:57:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2402	56,822.707292	2014 JUN	14	16:58:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2403	56,822.708333	2014 JUN	14	17:60: 0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2404	56,822.709375	2014 JUN	14	17:1:29	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2405	56,822.710417	2014 JUN	14	17:3:0	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2406	56,822.711458	2014 JUN	14	17:4:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2407	56,822.712500	2014 JUN	14	17:6:0	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2408	56,822.713542	2014 JUN	14	17:7:30	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2409	56,822.714583	2014 JUN	14	17:9:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2410	56,822.715625	2014 JUN	14	17:10:29	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2411	56,822.716667	2014 JUN	14	17:12:0	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2412	56,822.717708	2014 JUN	14	17:13:29	15	15	0	15	0	15	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2413	56,822.718750	2014 JUN	14	17:15:0	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2414	56,822.719792	2014 JUN	14	17:16:30	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2415	56,822.720833	2014 JUN	14	17:18:0	22	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2416	56,822.721875	2014 JUN	14	17:19:30	23	0	0	0	23	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
2417	56,822.722917	2014 JUN	14	17:21:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2418	56,822.723958	2014 JUN	14	17:22:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2419	56,822.725000	2014 JUN	14	17:24:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2420	56,822.726042	2014 JUN	14	17:25:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2421	56,822.727083	2014 JUN	14	17:27:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2422	56,822.728125	2014 JUN	14	17:28:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2423	56,822.747917	2014 JUN	14	17:57:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2438	56,823.709375	2014 JUN	15	17:1:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2439	56,823.710417	2014 JUN	15	17:3:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2440	56,823.711458	2014 JUN	15	17:4:29	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2441	56,823.712500	2014 JUN	15	17:6:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2442	56,823.713542	2014 JUN	15	17:7:30	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2443	56,823.714583	2014 JUN	15	17:9:0	12	12	0	12	0	12	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2444	56,823.715625	2014 JUN	15	17:10:29	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2445	56,823.716667	2014 JUN	15	17:12:0	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2446	56,823.717708	2014 JUN	15	17:13:29	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2447	56,823.718750	2014 JUN	15	17:15:0	15	0	0	0	15	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
2448	56,823.746875	2014 JUN	15	17:55:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2494	56,824.706250	2014 JUN	16	16:57:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2495	56,824.707292	2014 JUN	16	16:58:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2496	56,824.708333	2014 JUN	16	17:60: 0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2497	56,824.709375	2014 JUN	16	17:1:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2498	56,824.710417	2014 JUN	16	17:3:0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2499	56,824.711458	2014 JUN	16	17:4:29	17	17	0	17	0	17	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2500	56,824.712500	2014 JUN	16	17:6:0	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2501	56,824.713542	2014 JUN	16	17:7:30	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2502	56,824.714583	2014 JUN	16	17:9:0	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2503	56,824.715625	2014 JUN	16	17:10:29	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2504	56,824.716667	2014 JUN	16	17:12:0	22	0	0	0	22	0	0	0.0	0.0	0.0	1.3	0.0	0.0
2505	56,824.717708	2014 JUN	16	17:13:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2506	56,824.718750	2014 JUN	16	17:15:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2507	56,824.719792	2014 JUN	16	17:16:30	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2508	56,824.721875	2014 JUN	16	17:19:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2556	56,825.703125	2014 JUN	17	16:52:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2557	56,825.704167	2014 JUN	17	16:54:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2558	56,825.705208	2014 JUN	17	16:55:29	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2559	56,825.706250	2014 JUN	17	16:57:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2560	56,825.707292	2014 JUN	17	16:58:30	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2561	56,825.708333	2014 JUN	17	17:60: 0	11	11	0	11	0	11	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2562	56,825.709375	2014 JUN	17	17:1:29	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2563	56,825.710417	2014 JUN	17	17:3:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2564	56,825.711458	2014 JUN	17	17:4:29	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2565	56,825.712500	2014 JUN	17	17:6:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2566	56,825.713542	2014 JUN	17	17:7:30	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2567	56,825.714583	2014 JUN	17	17:9:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2568	56,825.715625	2014 JUN	17	17:10:29	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2569	56,825.716667	2014 JUN	17	17:12:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2570	56,825.717708	2014 JUN	17	17:13:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2607	56,826.704167	2014 JUN	18	16:54:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2608	56,826.705208	2014 JUN	18	16:55:29	6	6	0	6	0	6	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2609	56,826.706250	2014 JUN	18	16:57:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2610	56,826.707292	2014 JUN	18	16:58:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2611	56,826.708333	2014 JUN	18	17:60: 0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2612	56,826.709375	2014 JUN	18	17:1:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2613	56,826.710417	2014 JUN	18	17:3:0	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2614	56,826.712500	2014 JUN	18	17:6:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2615	56,826.739583	2014 JUN	18	17:45:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2616	56,826.740625	2014 JUN	18	17:46:29	1	0	0	0	15	0	0	0.0	0.0	0.0	−0.2	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2741	56,827.696875	2014 JUN	19	16:43:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2742	56,827.697917	2014 JUN	19	16:45:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2743	56,827.698958	2014 JUN	19	16:46:29	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2744	56,827.700000	2014 JUN	19	16:48:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2745	56,827.701042	2014 JUN	19	16:49:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2746	56,827.702083	2014 JUN	19	16:51:0	9	9	0	9	0	9	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2747	56,827.703125	2014 JUN	19	16:52:30	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2748	56,827.704167	2014 JUN	19	16:54:0	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2749	56,827.705208	2014 JUN	19	16:55:29	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2750	56,827.706250	2014 JUN	19	16:57:0	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2751	56,827.707292	2014 JUN	19	16:58:30	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2752	56,827.708333	2014 JUN	19	17:60: 0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2834	56,828.693750	2014 JUN	20	16:39:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2835	56,828.694792	2014 JUN	20	16:40:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2836	56,828.695833	2014 JUN	20	16:42:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2837	56,828.696875	2014 JUN	20	16:43:30	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2838	56,828.697917	2014 JUN	20	16:45:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2839	56,828.698958	2014 JUN	20	16:46:29	10	10	0	10	0	10	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2840	56,828.700000	2014 JUN	20	16:48:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2841	56,828.701042	2014 JUN	20	16:49:30	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2842	56,828.702083	2014 JUN	20	16:51:0	22	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2843	56,828.703125	2014 JUN	20	16:52:30	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2844	56,828.704167	2014 JUN	20	16:54:0	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2845	56,828.705208	2014 JUN	20	16:55:29	22	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2846	56,828.710417	2014 JUN	20	17:3:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2895	56,829.693750	2014 JUN	21	16:39:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2896	56,829.694792	2014 JUN	21	16:40:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2897	56,829.695833	2014 JUN	21	16:42:0	5	5	0	5	0	5	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2898	56,829.696875	2014 JUN	21	16:43:30	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2899	56,829.697917	2014 JUN	21	16:45:0	15	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2900	56,829.698958	2014 JUN	21	16:46:29	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2901	56,829.700000	2014 JUN	21	16:48:0	9	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2902	56,829.701042	2014 JUN	21	16:49:30	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2903	56,829.702083	2014 JUN	21	16:51:0	29	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2904	56,829.703125	2014 JUN	21	16:52:30	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2905	56,829.704167	2014 JUN	21	16:54:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2933	56,830.689583	2014 JUN	22	16:33:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2934	56,830.690625	2014 JUN	22	16:34:30	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2935	56,830.691667	2014 JUN	22	16:36:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2936	56,830.692708	2014 JUN	22	16:37:29	3	3	0	3	0	3	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
2937	56,830.693750	2014 JUN	22	16:39:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2938	56,830.694792	2014 JUN	22	16:40:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2939	56,830.695833	2014 JUN	22	16:42:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2940	56,830.696875	2014 JUN	22	16:43:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2941	56,830.697917	2014 JUN	22	16:45:0	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2942	56,830.698958	2014 JUN	22	16:46:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2943	56,830.721875	2014 JUN	22	17:19:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2944	56,830.722917	2014 JUN	22	17:21:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
2997	56,831.685417	2014 JUN	23	16:27:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2998	56,831.686458	2014 JUN	23	16:28:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
2999	56,831.687500	2014 JUN	23	16:30:0	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3000	56,831.688542	2014 JUN	23	16:31:30	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3001	56,831.689583	2014 JUN	23	16:33:0	10	10	0	10	0	10	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3002	56,831.690625	2014 JUN	23	16:34:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3003	56,831.691667	2014 JUN	23	16:36:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3004	56,831.692708	2014 JUN	23	16:37:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3005	56,831.693750	2014 JUN	23	16:39:0	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3006	56,831.694792	2014 JUN	23	16:40:30	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3007	56,831.695833	2014 JUN	23	16:42:0	26	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3008	56,831.696875	2014 JUN	23	16:43:30	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3009	56,831.700000	2014 JUN	23	16:48:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3010	56,831.712500	2014 JUN	23	17:6:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3061	56,832.678125	2014 JUN	24	16:16:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3062	56,832.679167	2014 JUN	24	16:18:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3063	56,832.680208	2014 JUN	24	16:19:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3064	56,832.681250	2014 JUN	24	16:21:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3065	56,832.682292	2014 JUN	24	16:22:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3066	56,832.683333	2014 JUN	24	16:24:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3067	56,832.684375	2014 JUN	24	16:25:29	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3068	56,832.685417	2014 JUN	24	16:27:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3069	56,832.686458	2014 JUN	24	16:28:29	9	9	0	9	0	9	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3070	56,832.687500	2014 JUN	24	16:30:0	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3071	56,832.688542	2014 JUN	24	16:31:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3072	56,832.689583	2014 JUN	24	16:33:0	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3073	56,832.690625	2014 JUN	24	16:34:30	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3074	56,832.691667	2014 JUN	24	16:36:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3075	56,832.692708	2014 JUN	24	16:37:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3076	56,832.693750	2014 JUN	24	16:39:0	28	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3077	56,832.694792	2014 JUN	24	16:40:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3078	56,832.695833	2014 JUN	24	16:42:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3079	56,832.696875	2014 JUN	24	16:43:30	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3080	56,832.697917	2014 JUN	24	16:45:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3081	56,832.698958	2014 JUN	24	16:46:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3082	56,832.700000	2014 JUN	24	16:48:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3083	56,832.712500	2014 JUN	24	17:6:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3084	56,832.740625	2014 JUN	24	17:46:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3123	56,833.681250	2014 JUN	25	16:21:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3124	56,833.682292	2014 JUN	25	16:22:30	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3125	56,833.683333	2014 JUN	25	16:24:0	4	4	0	4	0	4	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3126	56,833.684375	2014 JUN	25	16:25:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3127	56,833.685417	2014 JUN	25	16:27:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3128	56,833.686458	2014 JUN	25	16:28:29	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3129	56,833.687500	2014 JUN	25	16:30:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3130	56,833.688542	2014 JUN	25	16:31:30	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3131	56,833.689583	2014 JUN	25	16:33:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3132	56,833.690625	2014 JUN	25	16:34:30	28	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3133	56,833.691667	2014 JUN	25	16:36:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3134	56,833.692708	2014 JUN	25	16:37:29	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3194	56,834.671875	2014 JUN	26	16:7:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3195	56,834.672917	2014 JUN	26	16:9:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3196	56,834.673958	2014 JUN	26	16:10:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3198	56,834.676042	2014 JUN	26	16:13:30	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3199	56,834.677083	2014 JUN	26	16:15:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3200	56,834.678125	2014 JUN	26	16:16:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3201	56,834.679167	2014 JUN	26	16:18:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3202	56,834.680208	2014 JUN	26	16:19:29	12	12	0	12	0	12	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3203	56,834.681250	2014 JUN	26	16:21:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3204	56,834.682292	2014 JUN	26	16:22:30	19	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3205	56,834.683333	2014 JUN	26	16:24:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3206	56,834.684375	2014 JUN	26	16:25:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3207	56,834.685417	2014 JUN	26	16:27:0	23	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3208	56,834.686458	2014 JUN	26	16:28:29	20	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3209	56,834.687500	2014 JUN	26	16:30:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3210	56,834.688542	2014 JUN	26	16:31:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3236	56,835.668750	2014 JUN	27	16:3:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3237	56,835.669792	2014 JUN	27	16:4:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3238	56,835.670833	2014 JUN	27	16:6:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3239	56,835.671875	2014 JUN	27	16:7:30	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3240	56,835.672917	2014 JUN	27	16:9:0	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3241	56,835.673958	2014 JUN	27	16:10:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3242	56,835.675000	2014 JUN	27	16:12:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3243	56,835.676042	2014 JUN	27	16:13:30	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3244	56,835.677083	2014 JUN	27	16:15:0	6	6	0	6	0	6	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3245	56,835.678125	2014 JUN	27	16:16:29	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3246	56,835.679167	2014 JUN	27	16:18:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3247	56,835.680208	2014 JUN	27	16:19:29	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3248	56,835.681250	2014 JUN	27	16:21:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3249	56,835.682292	2014 JUN	27	16:22:30	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3250	56,835.683333	2014 JUN	27	16:24:0	14	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3251	56,835.684375	2014 JUN	27	16:25:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3252	56,835.685417	2014 JUN	27	16:27:0	21	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3253	56,835.686458	2014 JUN	27	16:28:29	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3254	56,835.687500	2014 JUN	27	16:30:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3278	56,836.671875	2014 JUN	28	16:6:0	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3279	56,836.672917	2014 JUN	28	16:7:30	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3280	56,836.673958	2014 JUN	28	16:10:29	4	4	0	4	0	4	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3281	56,836.675000	2014 JUN	28	16:12:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3282	56,836.676042	2014 JUN	28	16:13:30	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3283	56,836.677083	2014 JUN	28	16:15:0	6	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3284	56,836.678125	2014 JUN	28	16:16:29	13	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3285	56,836.679167	2014 JUN	28	16:18:0	11	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3286	56,836.680208	2014 JUN	28	16:19:29	7	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3287	56,836.681250	2014 JUN	28	16:21:0	10	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3288	56,836.682292	2014 JUN	28	16:22:30	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3289	56,836.687500	2014 JUN	28	16:30:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag
3313	56,837.668750	2014 JUN	29	16:3:0	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3314	56,837.669792	2014 JUN	29	16:4:30	5	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3315	56,837.670833	2014 JUN	29	16:6:0	10	10	0	10	0	10	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
3316	56,837.671875	2014 JUN	29	16:7:30	8	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3317	56,837.672917	2014 JUN	29	16:9:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3318	56,837.673958	2014 JUN	29	16:10:29	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3319	56,837.675000	2014 JUN	29	16:12:0	16	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3320	56,837.676042	2014 JUN	29	16:13:30	17	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3321	56,837.677083	2014 JUN	29	16:15:0	12	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3322	56,837.678125	2014 JUN	29	16:16:29	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3323	56,837.679167	2014 JUN	29	16:18:0	18	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3324	56,837.680208	2014 JUN	29	16:19:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3325	56,837.681250	2014 JUN	29	16:21:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0

. A sample of summarized search results in the month of November 2011 is shown in

Table 5. A sample of summarized search results in the month of November 2011.

#	MJD	Date	Day	Time UT	Stations	Stations/Cloud						Adj. Days’ Time Lag (min)
31	55,867.073958	2011 NOV	2	1:46:29	4	4	0	4	0	4	0	−4.5	0.0	−4.5	0.0	−4.5	0.0
32	55,867.075000	2011 NOV	2	1:48:0	2	0	2	0	0	0	0	0.0	−3.0	0.0	0.0	0.0	0.0
33	55,867.076042	2011 NOV	2	1:49:30	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
50	55,868.070833	2011 NOV	3	1:42:0	3	0	0	3	0	3	0	0.0	0.0	−4.5	0.0	−4.5	0.0
51	55,868.071875	2011 NOV	3	1:43:29	4	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
52	55,868.072917	2011 NOV	3	1:45:0	3	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
53	55,868.073958	2011 NOV	3	1:46:29	2	0	2	0	0	0	0	0.0	−1.5	0.0	0.0	0.0	0.0
54	55,868.077083	2011 NOV	3	1:51:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
114	55,871.063542	2011 NOV	6	1:31:30	2	0	0	2	0	2	0	0.0	0.0	−3.0	0.0	−3.0	0.0
115	55,871.064583	2011 NOV	6	1:33:0	2	0	2	0	0	0	0	0.0	−4.5	0.0	0.0	0.0	0.0
127	55,872.059375	2011 NOV	7	1:25:29	2	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
128	55,872.060417	2011 NOV	7	1:27:0	5	0	0	5	0	5	0	0.0	0.0	−4.5	0.0	−4.5	0.0
129	55,872.061458	2011 NOV	7	1:28:29	2	0	2	0	0	0	0	0.0	−4.5	0.0	0.0	0.0	0.0
130	55,872.062500	2011 NOV	7	1:30:0	1	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0

. The column “Stations” represents the number of stations in the cloud, and “Stations/Cloud” in six columns indicates the search results in six experiments. For control purposes, numbers for “Stations/Cloud” indicate the number of stations recording a cloud that coincide with data in column “Stations”. The column “Adj. Days’ Time Lag (min)” shows the difference in minutes (min) between the identified cloud of regular waves in adjacent days (current minus previous). Information on lines 31–33 represents clouds in one wave, lines 50–54 indicate another regular wave, lines 114–115 indicate another very small wave, and lines 127–130 indicate one more wave. Complete information for this data subset for the month of June 2014 is presented in Table A1, where the summarized search results of the selected month are shown.

November 2011, during the 24th solar activity cycle, was a month of low solar activity. This study demonstrates that the wavelength of regular ionospheric scintillation waves is dependent on solar activity. In the example shown in Table 5, the regular waves are very short. According to Formula (5), the predicted regular wave of the next day partially overlaps the regular wave of the current day in terms of time in minutes and seconds, unless one of the regular wavelengths of the comparable adjacent day is less than a certain time limit. For example, in Table 5, on NOV 2 and NOV 3, the time 1:42:00 < 1:49:30 and 1:51:00 > 1:46:29, but the wave of NOV 6 does not cover the adjacent day NOV 7 in the sense of minutes and seconds.

3.1. Linear Approximation

Because it was not possible to detect regular ionospheric scintillation waves on all days in some months, a linear approximation of the time series of detected waves was performed for peak clouds, median clouds, and initial (beginning) clouds. The approximation for initial clouds showed the most significant errors, rendering its use for forecasts invalid. The number of input data for peak and median clouds is shown in Figure 10.

In the linear approximation algorithm, approximation precision criteria were incorporated. For example, the criterion for the precision of peak time series approximation was 3 min. To achieve this precision, times with the largest discrepancy were discarded during each iteration, while keeping the number of remaining times no fewer than 4 in the solution. For the median, there were two solutions: one with the same conditions as for peak cloud times, but the other with different precision conditions—a precision of 17 min and a minimum of 6 time counts.

Based on the linear approximation results, it was checked whether such scintillation waves were present in the CORS dataset. The search results are shown in Figure 11.

As a result of linear approximation, the peak cloud times of the unidentified waves were also predicted. The search results are shown in

Table 6. The results of the wave search after peak time approximation.

#	Year	Month	Pred	Not	Found	Wlen	For-mean	Mean	Outliers	Thr
1	2007	JUN	30	1	30	39.2	27	11.7	3	60.0
2	2007	AUG	31	4	31	11.6	31	11.6	0	60.0
3	2008	JUN	30	1	30	14.2	29	13.3	1	60.0
4	2011	SEP	30	3	28	36.1	27	10.4	3	60.0
5	2011	NOV	30	8	5	28.5	4	2.3	26	60.0
6	2013	OCT	24	5	8	11.4	8	11.4	16	60.0
7	2013	NOV	30	8	26	31.6	24	3.4	6	60.0
8	2013	DEC	31	2	23	41.2	20	2.4	11	60.0
9	2014	JUN	30	2	30	26.8	29	25.4	1	60.0
10	2014	OCT	31	2	31	82.2	28	14.6	3	60.0
11	2014	DEC	31	0	31	57.6	24	25.3	7	60.0
12	2015	MAR	31	1	31	29.1	29	23.6	2	60.0
13	2015	MAY	31	3	31	22.6	29	10.2	2	60.0
14	2015	JUN	30	1	30	45.0	28	13.9	2	60.0
15	2015	OCT	31	2	30	57.6	26	6.1	5	60.0
16	2015	DEC	31	7	9	34.7	8	3.0	23	60.0
17	2016	APR	30	9	6	6.9	6	9.2	24	60.0
18	2016	JUL	31	2	28	28.3	25	8.9	6	60.0
19	2017	APR	30	2	28	57.6	25	11.0	5	60.0
20	2017	MAY	31	1	31	129.0	24	14.9	7	60.0
21	2017	JUL	31	6	31	143.2	26	16.8	5	60.0
22	2017	SEP	30	1	30	23.3	27	9.0	3	60.0
23	2017	OCT	31	0	28	33.8	24	5.6	7	60.0

. The information in columns is presented as follows: “Pred” indicates the predicted wave’s peak time; “Not” indicates the number of waves that are not found in search procedures; “Found” indicates the number of waves found in final search procedures based on linear approximation results; “Wlen” indicates the mean wavelength (min); “For-mean” indicates the number of waves without outliers of length; “Mean” indicates the mean wavelength (min) without outliers; “Outliers” indicates the number of outliers; “Thr” indicates the threshold for outliers 60 (min).

The results in Table 6 and Figure 11 confirm that the method of predicting waves via linear transformation is not applicable during months of low solar activity, when the scintillation wavelength is less than 10 min. On the other hand, it can be successfully used in months of elevated solar activity.

3.2. Solar Activity and Length of Regular Ionospheric Scintillation Waves

The overview of the percentage distribution of the wavelength of the identified regular waves is shown in

Table 7. Wavelength percentage in min.

Year	Month	[0; 5)	[5; 10)	[10; 15)	[15; 20)	[20; 25)	[25;30)	[30; 60)	>60
2007	JUN	10.0	20.0	33.3	26.7	0.0	0.0	0.0	10.0
2007	AUG	6.5	35.4	32.3	19.4	3.2	3.2	0.0	0.0
2008	JUN	0.0	20.0	60.1	6.7	3.3	3.3	3.3	3.3
2011	SEP	17.9	53.5	17.9	0.0	0.0	0.0	7.1	3.6
2011	NOV	80.0	0.0	0.0	0.0	0.0	0.0	0.0	20.0
2013	OCT	62.5	12.5	0.0	0.0	12.5	0.0	12.5	0.0
2013	NOV	69.3	19.2	3.8	0.0	0.0	0.0	0.0	7.7
2013	DEC	82.7	0.0	4.3	0.0	0.0	0.0	0.0	13.0
2014	JUN	3.3	0.0	13.3	36.8	10.0	10.0	23.3	3.3
2014	OCT	3.2	6.5	32.3	45.1	3.2	0.0	0.0	9.7
2014	DEC	0.0	0.0	0.0	0.0	67.7	0.0	9.7	22.6
2015	MAR	3.3	0.0	0.0	19.4	41.9	16.1	12.9	6.5
2015	MAY	12.9	35.5	29.0	16.1	0.0	0.0	0.0	6.5
2015	JUN	10.0	13.3	46.7	13.3	0.0	0.0	10.0	6.7
2015	OCT	50.0	30.0	3.3	0.0	0.0	0.0	3.3	13.4
2015	DEC	66.7	22.2	0.0	0.0	0.0	0.0	0.0	11.1
2016	APR	83.3	0.0	0.0	0.0	0.0	0.0	16.7	0.0
2016	JUL	39.4	21.4	10.7	10.7	0.0	7.1	0.0	10.7
2017	APR	0.0	25.0	57.2	7.1	0.0	0.0	0.0	10.7
2017	MAY	0.0	0.0	61.3	9.7	3.2	0.0	3.2	22.6
2017	JUL	3.2	0.0	42.0	25.8	3.2	3.2	6.5	16.1
2017	SEP	10.0	50.0	23.4	3.3	3.3	0.0	0.0	10.0
2017	OCT	42.8	35.7	3.6	3.6	0.0	0.0	0.0	14.3
AVERAGE		28.6	17.4	20.6	10.6	6.6	1.9	4.7	9.6

for various months and years of the 24th solar cycle in the mid-latitude country of Latvia.

The period was selected from the first to the last MJD day, during which regular disturbance was identified over the course of a month. For better visibility, Figure 12, Figure 13, Figure 14, Figure 15, Figure 16 and Figure 17 show the distributions of wavelengths mentioned in Table 7 for each year. These graphs illustrate the dependence of the wavelength distribution on solar geomagnetic activity. Solar activity decreased in 2007 and 2008 (Figure 12), but it was still relatively high.

In a period of low solar activity, in November 2011, up to 80% of wavelengths are very short ([0; 5) minutes) (Table 7, Figure 13). However, 20% of waves last more than 1 h.

During the period of high solar activity, in June 2014, the wavelength was [15; 20) minutes long in 36.8% of occurrences; in October 2014, the wavelength was [20; 25) minutes long in 45.1% of occurrences; and in December 2014, the wavelength was [20; 25) minutes long in 67.7% of occurrences, and in 22.6% of occurrences, the wavelength was more than 1 h long (Figure 14).

Figure 15 shows how wavelengths change with changes in solar activity. In March, May, and June 2015, solar activity is very high, while in October and December, it is significantly lower. In March, there is a very uniform distribution starting at 15 min (42% [20; 25)). The wavelength does not exceed 5 min in 50% of cases in October and in 67% of cases in December.

Figure 16 shows the wavelength distribution in 2016, when solar activity was low.

Solar geomagnetic activity began to increase in April 2017 and sharply decreased in October 2017. However, in September 2017, a very strong geomagnetic storm was observed in the USA and Canada, but it did not appear in Latvia. This is also reflected in the wavelength distribution shown in Figure 17.

3.3. Pearson’s Correlation

Table 8. Pearson’s correlation coefficient results before the removal of the Loss-of-Lock (1st row) and after the removal of the Loss-of-Lock (2nd row).

TEC and Cycle Slips				TEC and Faulty Solutions				TEC and Cycle Slips from Faulty Solutions				Cycle Slips and Faulty Solutions
[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]
18	5	0	23	18	4	0	24	25	4	0	17	25	1	2	18
19	5	0	22	16	6	0	24	26	3	0	17	21	0	2	23

summarizes the analysis of the Pearson’s coefficients’ results in both versions—a complete set of input data (row 1) and input data without Loss-of-Lock situations (row 2). The results for each of the four data types are summarized in four columns: Pearson’s correlation coefficient is in the following bounds: [0; 0.4), indicating a very weak correlation, in bounds of [0.4; 0.7), indicating a moderate correlation, in bounds of [0.7; 1], indicating a strong correlation, and in bounds of (0; –1], indicating a negative correlation. In both versions 1 and 2, the results are very similar—a weak correlation and a negative correlation between TEC and the number of cycle slips, TEC and the number of faulty solutions, TEC and cycle slips in faulty solutions, and between cycle slips and faulty solutions. Only in two cases is there a very strong correlation between cycle slips and the number of incorrect solutions, one of which is in March 2015.

Unfortunately, the ROTI values are only available starting in 2010 [41].

Similar to Table 8,

Table 9. Pearson’s correlation coefficient results for ROTI and faulty solutions.

ROTI and Cycle Slips				ROTI and Faulty Solutions				ROTI and Cycle Slips from Faulty Solutions				ROTI and TEC
[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]	[0; 0.4)	[0.4; 0.7)	[0.7; 1]	(0; −1]
18	5	3	8	13	6	1	14	15	4	1	14	18	7	0	9

summarizes the analysis of Pearson’s coefficients for each of the four data types, with results presented in four columns. A correlation summary of the ROTI is provided in Table 9.

Figure 18 shows the monthly mean values for the following:

• TEC-max over the territory of Latvia;
• The mean value of cycle slip counts found with Bernese GNSS software v5.2 for all volumes of reduced solutions, including faulty solutions (CSLP);
• The mean count of faulty solutions (F.sol.);
• The mean count of cycle slips discovered using the Bernese GNSS software v5.2 for faulty solutions.

As the number of cycle slips is greater than the number of faulty solutions, the Bernese GNSS software v5.2 identified most of the affected positions; however, there are still many faulty solutions that the Bernese GNSS software v5.2 did not recognize.

In Figure 19, the variations in Pearson’s correlation coefficient in three cases are depicted: between TEC and the number of cycle slips, TEC and the number of faulty solutions (f. s.), and TEC and faulty solutions with removed Loss-of-Lock sequences (No LoL). The conclusion is that in most situations, TEC max, which is defined as a smooth value over a territory of Latvia, is not comparable to the sporadic nature of the real-time instantaneous spatial distribution of TEC [42].

3.4. The Impact of Daily Regular Ionospheric Waves on Positioning Accuracy

To assess the impact of regular scintillation wave on positioning accuracy, error data and ROTI data were randomly selected from the Latvian CORS dataset defined in Formula (1). An example of ROTI@ground and mean positioning 3D discrepancies for all clouds of regular waves on 23 March 2015 is shown in

Table 10. ROTI@ground and mean positioning 3D discrepancies (m) for each cloud of the wave in high solar activity.

#	Date	Time UT	Stations	ROTI@ground	Mean Discr (m)
1328	23 MAR 2015	21:43:29	2	0.0190	0.061
1329	23 MAR 2015	21:45:0	3	0.0325	0.246
1330	23 MAR 2015	21:46:30	7	0.0379	0.341
1331	23 MAR 2015	21:48:0	13	0.0386	1.783
1332	23 MAR 2015	21:49:29	23	0.0386	0.963
1333	23 MAR 2015	21:51:0	29	0.0389	0.408
1334	23 MAR 2015	21:52:29	18	0.0390	0.326
1335	23 MAR 2015	21:54:0	16	0.0394	0.213
1336	23 MAR 2015	21:55:30	14	0.0392	0.197
1337	23 MAR 2015	21:57:0	9	0.0391	0.169
1338	23 MAR 2015	21:58:30	14	0.0394	0.165
1339	23 MAR 2015	22:0:0	6	0.0391	0.182
1340	23 MAR 2015	22:1:29	7	0.0398	0.140
1341	23 MAR 2015	22:3:0	2	0.0395	0.167
1342	23 MAR 2015	22:4:30	3	0.0390	0.150
22 WAVE No. 1328	AVERAGE			0.0373	0.368

. In the Stations column, the number of stations in the cloud is indicated.

The ROTI@ground and mean discrepancies for individual clouds in the situation when solar activity was low are depicted in

Table 11. ROTI@ground and mean positioning 3D discrepancies for each cloud of the wave in low solar activity.

#	Date	Time UT	Stations	ROTI@ground	Mean Discr (m)
63	4 NOV 2011	1:39:0	6	0.0334	0.179
64	4 NOV 2011	1:40:30	3	0.0391	0.160
3 WAVE No 63	AVERAGE			0.0363	0.169
114	6 NOV 2011	1:31:30	2	0.0173	0.097
115	6 NOV 2011	1:33:0	2	0.0255	0.141
4 WAVE No 114	AVERAGE			0.0214	0.119
127	7 NOV 2011	1:25:29	2	0.0187	0.066
128	7 NOV 2011	1:27:0	5	0.0343	0.159
129	7 NOV 2011	1:28:29	2	0.0353	0.147
5 WAVE No. 127	AVERAGE			0.0294	0.124

.

In March 2015, the solar activity increased.

Table 12. ROTI@ground and mean discrepancies of regular waves in March 2015.

Date	ROTI@ground	Discr	TEC	CSLP	Date	ROTI@ground	Discr	TEC	CSLP
2	0.0386	0.271	31	2	17	0.0532	3.993	40	29
3	0.0383	0.378	29.4	3	18	0.0387	1.423	18.4	2
4	0.0385	1.202	31.9	3	19	0.0379	0.329	25.2	5
5	0.0396	0.422	33.8	1	20	0.0366	0.301	19.9	2
6	0.0409	0.334	35.1	3	21	0.0386	0.371	25	2
7	0.0382	0.354	31.2	3	22	0.0373	0.64	33.5	1
8	0.0413	0.363	34.7	0	23	0.0419	0.368	31.7	1
9	0.0398	0.424	28	2	24	0.0404	0.343	32.3	2
10	0.0391	0.672	30.2	2	25	0.0396	1.495	32.9	2
11	0.0428	0.822	31.3	2	26	0.0413	0.407	33.3	0
12	0.0413	0.686	30	2	27	0.0388	0.38	30.9	2
13	0.0414	0.306	33.4	0	28	0.0368	0.466	36.4	3
14	0.039	0.597	30.3	1	29	0.0372	0.641	35.6	1
15	0.5576	0.414	30	0	30	0.0374	0.4	29	1
16	0.1046	0.318	29.3	2	31	0.0372	0.932	36.3	0

shows the impact of regular wave, ROTI@ground, and the average positioning error of each day. It does not include individual clouds like in Table 10 and Table 11. The mean values of the impact of daily regular scintillation waves are computed. Data in Table 12 show that daily regular scintillation waves cause serious positioning errors.

3.5. Time Lag of Daily Regular Ionospheric Scintillation Waves

The period analyzed included the first to last MJD day in which a regular disturbance was identified over the course of a year. The average daily time lag for regular ionospheric scintillations was calculated (

Table 13. Regularity results for the peak daily shift.

#	Time Span	Daily Shift (day)	Daily Shift (min)
1	JUN 2007–30 AUG 2007	0.997210	–4.0
2	SEP 2011–28 NOV 2011	0.997195	–4.0
3	OCT 2013–23 DEC 2013	0.997055	–4.2
4	JUN 2014–30 DEC 2014	0.997155	–4.1
5	MAR 2015–31 DEC 2015	0.997177	–4.1
6	APR 2016–30 JUL 2016	0.997153	–4.1
7	APR 2017–30 OCT 2017	0.997145	–4.1
	Average	0.997156
	STDV	0.000046	0.1 min

). The value of the daily time lag is close to the length of the sidereal day. This confirms the hypothesis that the initial source of the radiation causing the scintillation is not the Sun: while it is enhanced by solar activity, its origin is interplanetary, similar to Pc1 pulsation waves.

Researchers have concluded [43] that Pc1 waves appear at midnight just before dawn. Our studies demonstrate the likelihood of daily scintillation waves occurring at any time of day (Table S1), if they are indeed the scintillation waves referred to as Pc1 waves.

Pc1 waves are enhanced by solar activity [43,44,45]. Figure 12, Figure 13, Figure 14, Figure 15, Figure 16 and Figure 17 show that the wavelength of regular waves is dependent on solar activity enhancement.

The regular wave repetition shift is 0.997156 days (or −4.1 min), which is an approximate sidereal day difference from the solar UT day. Consequently, the waves are assumed to originate from interplanetary media.

4. Discussion

The regularity of daily disturbances is time-shifted and close to the length of a sidereal day. Moreover, the duration (length) of daily scintillation waves is influenced by solar activity. This daily regular ionospheric scintillation wave phenomenon is similar to the Pc1 interplanetary wave phenomenon; however, unlike Pc1 observations, it is recorded at any time of the day (Table S1). Only during periods of low solar activity do the regular scintillation waves not appear.

Table 14. A comparison of Pc1 waves with GPS observations of ionospheric waves on the same date.

#	Date of Pc1 Waves	Pc1 Waves	Dur. (min)	CPS d. Waves	Dur. (min)
1	4 November 2011	1	10	1	1.5
2	29 November 2011	1	30	1	7.5
3	18 March 2015	2	12	1	31.5
4	19 March 2015	4	10	3	3; 27; 4.1
5	23 March 2015	4	46	1	21
6	29 March 2015	3	19	1	22.5
7	25 June 2015	1	2	3	12; 4.5; 6
8	28 June 2015	1	4	4	1.5; 3; 4.5; 607.5
9	3 April 2017	1	8	2	1.5; 7.5
10	9 April 2017	1	88	2	16.6; 16.5
11	26 April 2017	1	10	3	255; 1.5; 10.5
12	27 April 2017	1	10	1	12
13	19 May 2017	1	36	6	82.5; 3; 1.5; 16.5; 126; 15

provides the dates of the observed Pc1 waves [43] and a comparison of the length of these waves with the number and length of waves observed by GPS in Latvia on those dates. The Pc1 dates were published in a preprint [43], but were unfortunately lacking an indication of the observation time.

In two other studies, the dates and times of Pc1 wave magnetometric measurements were highlighted.

Theoretical predictions with observational results from conjugate high-latitude stations in Antarctica and Greenland were made by Pilipenko et al. [44] during the summer (7 June 2014) and winter (7 January 2014) periods. A disturbance is a sudden commencement (SC) pulse caused by the impact of an interplanetary shock (IS) on the magnetosphere. At high latitude, the duration of the local field line eigenperiod T_A is ~5–10 min, which can exceed an SC impulse with 1–2 min [44]. According to the SuperDARNTEC maps, the 7 June 2014 event (16:50–16:55 UT) occurred in the Southern Hemisphere. In Figure 20, a daily regular scintillation event recorded by the Latvian CORS on 7 June 2014, is shown.

Francia et al. [33] studied the correspondence between Pc1 activity and ionospheric irregularities at polar latitudes. They performed magnetometric observations on 22 February 2007, at the MST station in Antarctica, between 02:00 and 14:00 UT.

Latvian CORS GPS observations confirmed that February 2007 had low solar activity. However, on February 22, two cases of ionospheric scintillation were recorded (Figure 21).

It is still a challenge to determine the apparent relationship between ionospheric conditions and Pc1 wave occurrence [43].

It cannot be claimed that the comparison of daily regular ionospheric scintillation events over time with the mentioned Pc1 wave cases indicates interrelation. However, the observed periodic day-to-day shift suggests that the scintillation recurrence impulse may originate in the interplanetary environment through a mutual relationship.

In Table S1 (Supplementary Materials) the fixed epoch of the peak cloud of regular daily scintillation wave is shown on the left side but on the right side the location of the regular daily scintillation wave in interval 0–24 h, marking for the 24-h scale graduations at hours (0, 6, 12, 18, 24) is shown. This very large list of daily regular ionospheric scintillation events cannot be placed horizontally in the manuscript, so it is presented vertically due to its large size. However, it clearly shows how the peak cloud epoch of the regular daily scintillation wave moves along the time scale. It is likely that this Table S1 would be useful to researchers who wish to check the existence of the daily scin-tillation phenomenon in their observation data.