**1. Introduction**

The equatorial ionization anomaly (EIA) is the result of ionization and electrodynamic processes in the ionosphere, representing a plasma density trough at the equator and two crests approximately 15◦ to the north and south. The formation process of EIA can be characterized as a fountain effect. That is, the daytime E region wind dynamo produces an

**Citation:** Wan, X.; Zhong, J.; Xiong, C.; Wang, H.; Liu, Y.; Li, Q.; Kuai, J.; Cui, J. Seasonal and Interhemispheric Effects on the Diurnal Evolution of EIA: Assessed by IGS TEC and IRI-2016 over Peruvian and Indian Sectors. *Remote Sens.* **2022**, *14*, 107. https://doi.org/ 10.3390/rs14010107

Academic Editors: Serdjo Kos, José Fernández and Juan F. Prieto

Received: 6 November 2021 Accepted: 24 December 2021 Published: 27 December 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

eastward electric field to move the plasma upward via E×B force; the increasing gravitational potential and pressure gradient would finally result in ambipolar diffusion, which transports the equatorial source plasma downward/poleward to the north and south, to create double plasma density crests [1–10].

Extensive studies have investigated the development of the EIA under various conditions configured by solar radiation, season, local time (LT), longitude, and geomagnetic disturbances, to reveal the impacts of those parameters on the ionizations, electrodynamics, and neutral dynamics behind the fountain effects. Yeh et al. [1] found that EIA crests generally begin to appear at 0900 LT, and the farthest latitude of the crests during daytime is correlated with the level of the fountain effect and the local ionospheric total electron content (TEC). Liu et al. [2] reported that the crest-to-trough ratio (CTR) of EIAs observed by the CHAMP satellite gradually increases from morning to noon, reaching its maximum value between 1800 and 1900 LT as a result of pre-reversal enhancement (PRE). Xiong et al. [3] reported that the electron density and magnetic latitudes of both EIA crests peak at approximately 1400 LT. The local time variation of the electron density crest during daylight hours is similar to that of the trough but with a 1–2 h delay [4].

Another interesting aspect of the EIA is the interhemispheric asymmetry, which is most pronounced during solstitial seasons. In the morning hours, the crest in the winter hemisphere generally forms earlier and has a greater magnitude than that in the summer hemisphere; during the afternoon, the summer hemisphere features the larger EIA crest [3,6–10]. The transition time of this interhemispheric EIA asymmetry occurs at around 1200–1400 LT, depending on solar activity levels [6,7]. In addition, this transition time is a function of observed altitude, since the time lag of fountain effects varies with height [3]. Interhemispheric EIA asymmetry has also been reported during equinoxes [7,10,11]. Neutral wind variations associated with displacements of the geographic and geomagnetic equators as well as magnetic declination angles have been proposed as the main factors affecting EIA asymmetry [10]. Balan et al. [11] argued that the displacement of the geographic and geomagnetic equators is a more significant factor than the declination angle.

The above-mentioned studies mainly focus on the EIA that exhibits clear double crests signature. However, before the first emergence of EIA double crests, the fountain-like processes had already been launched, but this stage receives much less attention in the research community. In detail, the sunlit ionization process is instantaneous, while the mechanical transportation process is much slower. Thus, at the beginning, stronger sunlit ionization actually would cause faster plasma accumulation at the subsolar position near the equator. The fountain effects take time to compete against the uneven ionization, to form EIA. The contributions of various physical processes during different stages of the EIA diurnal evolution are still not well known. In particular, the dynamic process before the emergence of the EIA crest is usually neglected and had not been investigated yet.

Note that the sunlit ionization and electrodynamical transportation have different reference equators, i.e., the geographic and geomagnetic equators. The displacement between the two equators varies at different longitudes, which would significantly impact the EIA evolution. Moreover, meridional thermospheric wind, which drags the ion along with it, is reported to impact the EIA evolution in two opposite ways. On the one hand, the transequatorial thermospheric wind pushes the plasma along the field line to contribute/counter the ambipolar diffusion in the winter/summer hemisphere, leading to a winter hemispheric priority during EIA's development [6,7]. On the other hand, the transequatorial thermospheric wind would lift/lower the F region height in the summer/winter hemisphere, leading to stronger/weaker intensity (i.e., TEC) of EIA crest [11–13]. It can be seen that the inclusion of thermospheric neutral wind effects could make the EIA evolution more complicated, the dominant seasonal cycles at different longitudes are still not well understood, and the physical processes and mechanisms involved are still in debate [6,7,12–14].

We dedicate this study not only to monitoring the interhemispheric asymmetry in a traditional way that focuses on the intensity of EIA, but also to trying to characterize the detailed time evolution to clarify the dynamical competition and cooperation between the sunlit ionization, ambipolar diffusion, and neutral wind drag. The TEC maps provided by IGS [15] are used under the geomagnetic quiet condition in 2013 in the Peruvian and Indian sectors. At low geomagnetic latitudes, these two sectors are both deployed with magnetometers to retrieve an equatorial electrojet (EEJ) that can be used as a proxy to select days with developed EIA, and GNSS receivers that provide reliable TEC product to investigate the seasonal and interhemispheric effects on the diurnal evolution of EIA. The International Reference Ionosphere (IRI-2016) model, widely recognized as a powerful tool to represent the climatological behavior of the ionosphere [16–19], is also adopted to check whether the empirical model can capture the real features of the EIA evolution.

In Section 2, we provide descriptions of the datasets. In Section 3, we introduce how the geomagnetic quiet days and days with well-developed EIAs are sorted out. Section 4 presents the seasonal/local variation of EIA, followed by detailed statistics of crests and trough variations. The crest-to-trough difference (CTD), is defined and described in Section 4.2. The EIA onset time, first emergence and peaks are further derived from CTD profiles and we presented the statistical results. In Section 5, we discuss the physical mechanisms involved. Conclusions of this study are provided in Section 6.

#### **2. Dataset**

#### *2.1. IGS TEC Maps and the IRI-2016 Model*

The IGS TEC data is an interpolated data product based on the TEC measurements from ground-based GNSS receivers that are distributed mainly over the continents [15]. Thus, the IGS TEC should provide trustable EIA observation over Indian and Peruvian sectors. The dataset has a time resolution of 15 min and spatial resolution of 2.5◦ × 5◦ in geographic latitude and longitude.

The distribution of GPS receivers over the oceanic region is much sparser than the continents, IGS TEC was found to overestimate Jason-2/3 derived TEC by more than 5 TECU [20]. This overestimation would possibly impact the climatological behavior of the retrieved EIA features. Thus, whether the IGS TEC is suitable to be extendedly applied to the longitudes over the oceanic region remains unclear. We choose another candidate data source from the IRI-2016 model, a widely used empirical ionospheric model, and it was recently improved with a new hmF2 model based on a new database from the worldwide network of ionosondes [17,19]. The IRI-2016 is used to retrieve the EIA feature and compare with IGS TEC data. This comparison would not only help to assess the performance of the empirical model in describing the regional EIA evolution, but also evaluate the feasibility of whether the empirical model can be applied to extend EIA study over the oceanic region.

For a given location, time, and date, the IRI model provides monthly averages of the ionospheric parameters, including electron density, electron temperature, ion temperature, ion composition, and TEC from an altitude range of 50–2000 km [17–19]. Options were set to calculate the TEC from the IRI-2016 model: The fof2 storm model was switched off, the Shubin-cosmic option was used for the hmf2 model, and NeQuick was used as the topside model. The maximum height of the TEC calculation was set to 2000 km.

#### *2.2. The EEJ Derived from Ground-Based Magnetometers*

Ground-based paired magnetometer measurements over the Peruvian and Indian sectors in 2013 were utilized to estimate the equatorial electrojets (EEJ). As a narrow current that flows in the E region above the magnetic equator, EEJ can be extracted by removing the solar quiet (Sq) current that barely shows latitudinal dependence [21]. To estimate the EEJ, we calculated the differences of the horizontal (H) component of the geomagnetic field between the paired magnetometer, as the residual horizontal magnetic field is recognized to be caused by the EEJ [22]. The measurements of Huancayo (HUA, −12.05◦ N, −75.33◦ E, 0.59◦ dip latitude) and Fuquene, (FUQ,18.11◦ N, −66.15◦ E, 17.06◦ dip latitude) are used for the Peruvian sector, and Tirunelveli (TIR, 8.7◦ N, 77.8◦ E, 0.59◦ dip latitude) versus Alibag (ABG, 18.6◦ N, 72.9◦ S, 13.67◦ dip latitude) are used for the Indian sector [19].

#### *2.3. Horizontal Wind Simulated by TIEGCM*

To estimate the neutral wind effects on the development of EIA, the horizontal wind simulated from the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) is adopted. The TIEGCM is a first principle and physics-based model driven by a high-latitude electric field [23], solar EUV, and UV spectral fluxes parameterized by the F10.7 index [24].

#### **3. Methodology and Methods**

#### *3.1. Sorting Geomagnetic Quiet Days Using Kp Index*

*Kp* index is a quasi-logarithmic index, ranging in steps of 1/3 from 0 to 9, to quantify the level of the geomagnetic disturbance on a global scale [25,26]. All the data used in this study were firstly sorted under geomagnetic quiet conditions when the daily mean *Kp* values were less than 3.

#### *3.2. Sorting Developed EIA Using EEJ as a Proxy*

Note that sometimes the EIA structure is dismissed, which could not be used to monitor the time evolution of EIA. Thus, we firstly determine whether the EIA appears for further statistics. As mentioned previously, the formation of EIA is a product of the eastward zonal electric field; thus, the intensity of the zonal electric field could be a good indicator for the EIA development. However, the direct measurement of the electric field is rare; an alternate option is EEJ, which serves as a proxy to quantify the daytime zonal electric field. The EEJ refers to a narrow band of intense electric current flowing above the equatorial dip in the daytime E-region driven by the E-region electric field and conductivity. The EEJ mainly flows eastward corresponding to an eastward electric field, the infrequent westward flow of EEJ is called counter electrojet (CEJ) corresponding to a westward electric field. The EEJ is considered to be a suitable proxy for EIA intensity. Stolle et al. [27] found correlation coefficients greater than 0.8 between EEJ strength and EIA intensity. Venkatesh et al. [28] discovered that the daily summed EEJ strength had correlations of 0.62 and 0.72 with the EIA crest amplitude and latitude, respectively.

Figure 1 shows examples of a weak EEJ profile (Figure 1a) and a CEJ profile (Figure 1b), with associated TEC maps for the day. The double-crest structure barely formed as the EEJ intensity was relatively weak (Figure 1a), while the CEJ resulted in a single peak at the equator, meaning that the EIA was inhibited completely.

It is reported that the strength of the EIA shows a better correlation with integrated EEJ values than the daily maximum of EEJ [28]. In addition, the time delay of the EIA response to EEJ strength is 2–3 h [27,28]. To identify the days with weak EEJ or CEJ, the averaged ΔH at an LT bin of 0800–1200 is calculated first. Please note that the Weak EEJ or CEJ were combined to be referred to as WEC in this study. Days with WEC were then determined when the averaged ΔH components were less than the threshold value; the remainder of the quiet days were recognized as EEJ days. Our experiments (not shown here) showed that the EEJ intensity was generally higher in the Peruvian sector than in the Indian sector, probably as a result of tidal effects. Thus, we chose two thresholds, ~70 nT and ~20 nT of averaged EEJ intensity during 0800–1200 LT for the Peruvian and Indian sectors, respectively.

Figure 2 shows the day numbers of the WEC and EEJ cases at longitudes of −75◦ E and 75◦ E, which represent the Peruvian and Indian sectors, respectively. The WEC case showed a preferential occurrence during the two solstices at both longitudes, consistent with previous studies which had found that the EEJ is characterized by solstitial minima [29–31]. However, for a given longitude, the WEC showed specific seasonal preferences as well. There were more WEC days around the December Solstice (Nov, Dec, Jan, Feb) than the June Solstice (May, Jun, Jul, Aug) in the Peruvian sector, with this preference being reversed in the Indian sector. Considering the northward–southward deviation of the dip equator from the geographic equator at both the Indian and Peruvian longitudes, this implied that

more WEC occurred when the dip equator is in the summer hemisphere. One may also note that the WEC day is missed in Mar, Apr, Aug, Sep, and Oct in the Indian sector (Figure 2b).

**Figure 1.** Two examples of the GPS-measured TEC map at Indian sector during (**a**) a weak EEJ day and (**b**) a CEJ day.

#### **4. Results**

#### *4.1. Overview of EIA during EEJ and Weak EEJ/CEJ Days*

Figure 3 shows the TEC maps during EEJ days in the Peruvian and Indian sectors. The data shown in the left, middle, and right columns represent different seasons: December Solstice (Dec. S., which includes Nov, Dec, Jan, and Feb), June Solstice (June. S., which includes May, Jun, Jul, and Aug), and equinoxes (March, April, September, October), respectively. The daily EEJ was first plotted in Figure 3a,d; solid black lines represent four-month averages.

**Figure 3.** Seasonal averaged TEC maps over the Peruvian and Indian sectors during EEJ days when the averaged EEJ intensity at 0800–1200 UT was relatively high during three seasons of December Solstice (left column), June Solstice (middle column) and equinoxes (right column). (**a**) EEJ profiles of Peruvian sector; (**b**) GPS TEC at Peruvian sector; (**c**) IRI-2016 derived TEC at Peruvian sector; (**d**–**f**) are organized in a same format as (**a**–**c**) but for the Indian sector.

For GPS TEC observations at both Peruvian and Indian sectors, the most prominent feature of EIA is the interhemispheric asymmetry. During two solstices, the crests in the winter/summer hemisphere are stronger during morning/afternoon hours. Moreover, the stronger crest generally resides at a higher latitude. An exception is that at the Peruvian during Dec. S., the southern crest (in the summer hemisphere) was stronger first, which lasted an interval of 0800–0900 LT; afterward, the northern crest (in the winter hemisphere) turned out to be dominant. During equinoxes, interhemispheric asymmetry still exists in that a stronger northern/southern appeared in the Peruvian/Indian sector. Similar seasonal and longitudinal variations of the interhemispheric asymmetry have been investigated by many studies. Those studies generally accepted that the displacements of the geographic and geomagnetic equators, the different geomagnetic declinations, and the meridional neutral wind show clear seasonal and longitudinal variations, which affects the diurnal evolution of EIA [3,6–9,11].

The developed EIA signatures can also be characterized by the IRI-2016, nevertheless, with a lower absolute TEC level by approximately 20 TECU, compared with the GPS TEC. In the Peruvian sector, IRI-2016 performed stronger crests in the summer hemisphere. However, in the Indian sector, the northern crest is persistently stronger than the southern crests. Hence, it can be noticed that the IRI-2016 performed quite a different interhemispheric asymmetry pattern compared with that of the GPS observations, especially during equinoxes.

On WEC days (Figure 4), as expected, the EIA barely formed as observed by the GPS TEC. The Peruvian sector even showed a single-peak structure over the equator during the December Solstice and two equinoxes, indicating that the fountain effect was completely inhibited. For the developed EIA, the intensity is weaker than that during EEJ days; that is, the double crest was narrower, and the TEC values were reduced by approximately 15 TECU and 5 TECU in the Peruvian and Indian sectors, respectively. Note that during the equinoxes there are no WEC days for the Indian sector as displayed in the statistical day numbers in Figure 2b, thus the corresponding TEC data were not available (Figure 4d–f).

**Figure 4.** *Cont*.

**Figure 4.** Similar to Figure 3, but during days that the average EEJ intensity at 0800–1200 UT was relatively low (refer to WEC days) at Peruvian sector (**a**–**c**) and Indian sector (**d**–**f**). The lack of equinox results in (**d**–**f**) is due to that the WEC days are not found.

However, TEC values predicted by the IRI-2016 model on WEC days remained at nearly the same levels as those during EEJ days, indicating that the model cannot capture an inhibited EIA signature. For a given time, the IRI-2016 predicted the result in terms of the monthly averaged value. Hence, this inherent characteristic of the empirical model would result in unreliable predictions for days with unusual space weather occurring during geomagnetic quiet times such as CEJs. To successfully ascertain the characteristics of daytime EIAs, we excluded those types of WEC days in the following analysis.
