The plasmasphere is the continuation of the ionosphere into the magnetosphere and represents the region of cold and relatively dense plasma in the inner magnetosphere . The base of the plasmasphere is defined as the transition from atomic oxygen to atomic hydrogen and occurs at altitudes between 500 and 2000 km depending on the geophysical conditions . The outer boundary of the plasmasphere, called the plasmapause, represents the cutoff in the plasma density, the location of which depends on the level of the geomagnetic disturbances. In the equatorial plane the plasmapause is typically found near 5–7 RE e.g.,.

The plasmapause position, LPP, is determined by the large-scale convection electric field which is superimposed on the corotation electric field. The convection electric field is the result of the interaction of the solar wind with the geomagnetic field. The corotation electric field is produced in the E region of the ionosphere and is conveyed into the plasmasphere along the magnetic field lines. During increasing magnetic activity, the stronger convection electric field pushes the plasmapause closer to the Earth down to 2 RE, peeling off the outer layers of the plasmasphere. On the other hand, during decreasing activity the plasmapause moves outward, and a slow refilling process from the dayside F region ionosphere begins. These characteristics are in agreement with the observations of Cluster e.g., and IMAGE satellite data e.g.,.

The LPP and its dependence on the geomagnetic activity have been studied both theoretically and empirically. The LPP has been directly related to the time variations in the convection electric field.

Two theoretical approaches have been used to describe the dynamics of the LPP: (i) the last closed streamline of the equatorial plasma related to the last closed equipotential of the electric field and (ii) the interchange instability mechanism appearing in the postmidnight sector during geomagnetic storms and substorms .

Empirical LPP has been estimated by examining ground-based whistler observations, in situ satellite density measurements (e.g., ISEE, CRRES), field-aligned current signature observations (CHAMP) and geomagnetic indices .

The often cited model of gives the LPP as a function of the maximum of the geomagnetic Kp index observed in the previous 24 h. expressed the LPP as a function of the maximum Kp index in the previous 12 h. extended that investigation by using Kp, Dst and AE geomagnetic indices taking the hours relative to the plasmapause crossing: 36 for Kp, 24 for Dst and 36 for AE. The new feature in their model is the LPP magnetic local time (MLT) dependence.

They obtained a little difference in the quality of the plasmapause models for different indices and a lack of local time dependences in the Dst model. A new empirical model of the LPP based on field-aligned currents measured by the CHAMP (CHAllenging Minisatellite Payload) satellite was introduced by . All these studies found that the plasmapause is more earthward during geomagnetically active periods with the plasmapause bulge displaced toward dusk.

In the three-dimensional dynamic model of the plasmasphere that has been recently coupled to the ionosphere , the plasmapause depends on the MLT and on the Kp index observed during the last 24 h. During substorm and storm events, the geomagnetic activity increases and enhances the convection electric field, mainly in the postmidnight MLT sector. This leads to an inward motion of the plasmapause closer to the Earth in this sector and then later in other MLT sectors due to the corotation of the plasmapause with the Earth. Using a E5D convection electric field , the equatorial LPP is calculated in all MLT sectors and is provided on the European space weather portal (www.spaceweather.eu). Using the same mechanism but stronger convection electric fields, the plasmapause position is found to be closer to the Earth .

correlated the average plasmapause radial positions observed by the EUV (Extreme Ultraviolet Imager) instrument on IMAGE with the solar wind parameters: Bz (Z component of the interplanetary magnetic field, IMF, vector B in the geocentric solar magnetospheric system, GSM), the IMF clock angle, and the polar cap potential drop ϕ. The LPP is found to be most tightly correlated with Bz. The time lags in the plasmapause response to Bz and IMF clock angle were found to be 180 min, and 240 min with respect to ϕ.

In the present study, we investigate the LPP determined by the WHISPER (Waves of HIgh frequency and Sounder for Probing of Electron density by Relaxation) instrument on board Cluster as a function of various LPP indicators: a.

solar wind coupling functions Bz, BV related to the interplanetary electric field (B is the magnitude of the IMF vector B, V is solar wind velocity), and novel function dΦmp/dt introduced by , which combines different physical processes responsible for the magnetospheric activity and is explained in detail in Sect. 2;

The paper is organized as follows. After this introduction, Sect. 2 describes the data and method of analysis. Section 3 contains the characteristics of the analyzed samples. In Sect. 4 we present the results of the cross-correlations between LPP and various LPP indicators. Discussions of the obtained results and conclusions are given in the last section.

Our study is based on the following data sets:

1-hour averages of geomagnetic indices Dst and AE;

The planetary geomagnetic activity index, Ap, the storm-time disturbance index, Dst, and the auroral electrojet index, AE, are downloaded from ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP and http://wdc.kugi.kyoto-u.ac.jp/dstae/index.html. For more detailed information about the indices, we refer to and . Among the available geomagnetic indices, these three indices have been chosen for describing the LPP as a function of geomagnetic activity because their variations can be physically interpreted and related to the specific magnetospheric current system (e.g., the ring current and polar electrojet). In this context, the widely used Kp index is difficult to interpret. However, it is related to the Ap index, which is more convenient to use since it is based on a linear scale.

The solar wind data were obtained from the Solar Wind Electron Proton and Alpha Monitor SWEPAM; and the magnetometer MAG; on board the Advanced Composition Explorer ACE;. We used the merged hour-averaged level-2 ACE data given at http://www.srl.caltech.edu/ACE/ASC/level2/.

We note that different coupling functions between the solar wind and the magnetosphere have been investigated by many authors e.g.,and references therein. Their relative importance has often been revised e.g.,.

For the correlation study we analyzed the following LPP indicators based on the solar wind basic and derived parameters Bz, BV and dΦmp/dt , defined as dΦmp/dt=V4/3BT2/3sin⁡8/3(θc/2), where BT=By2+Bz2 is the projection of IMF vector in the Y–Z plane and θc=arctan⁡(By/Bz) is the IMF clock angle in GSM.

These coupling functions have been chosen because their role in changing the state of the magnetosphere may be physically interpreted. The conditions or processes that represent each of these functions are as follows. The energy transfer from the solar wind into the magnetosphere is most favorable when the IMF has a strong Bz component oriented southward. Then a reconnection with the Earth's magnetic field becomes possible, and consequently the strength of convection increases, leading to the modification of the plasmapause position. The BV quantity is directly related to the interplanetary electric field and thus to the changes in the plasmapause shape and position. Studies by have shown that the magnetosphere responds in different manners to different solar drivers, e.g., coronal mass ejections and corotating interaction regions. They further show that the same BV range plays an equally important role for both types of magnetospheric drivers. Since in the present study we do not aim to make the distinction between the solar drivers of geomagnetic disturbances, we chose the BV quantity as the representative coupling function. also found that BV is strongly correlated with geomagnetic indices Ap, AE and Dst, so we expected this quantity to be a good measure for the response of the plasmapause as well.

The quantity dΦmp/dt combines different physical processes responsible for the magnetospheric activity, such as the rate at which IMF field lines are convected toward the magnetopause, the fraction of field lines impacting the magnetosphere that merge, the amount of the opened flux, the length of the merging line. showed that among 20 employed coupling functions, dΦmp/dt represents the interaction between the solar wind and magnetosphere best for a wide variety of geomagnetic activity, even better than BsV (Bs is zero for Bz>0) which is one of the most widely used coupling function, and thus is used in the present study.

The analyzed LPP are determined from data provided by the WHISPER instrument on board the Cluster satellites. The Cluster mission consists of four identical spacecraft (C1, C2, C3, and C4) launched in 2000 on similar elliptical polar orbits with a time period of 57 h. The initial perigee was about 4 RE and the apogee at 19.6 RE . Each satellite crosses the inner magnetosphere from the Southern to the Northern Hemisphere around the perigee. Due to the annual precession of the orbit, all MLTs are covered over the course of a year. Each spacecraft carries 11 instruments. The WHISPER data allow determining the electron density inside and outside the plasmasphere . The frequency spectra obtained during the passive and active (sounding) operation modes of the instrument carry out a direct or indirect determination of the electron plasma frequency FP. The electron density Ne is deduced from FP by the relation Ne (cm-3) = FP2(kHz)/81. The instrument can estimate electron densities up to 80 cm-3 due to the instrument frequency upper limit at 80 kHz, with a temporal resolution of 2 s on average. The uncertainty on the plasma frequency measurements is 163 Hz, which gives a relative error on electron density of the order of 0.5–5 % at densities higher than 20 cm-3 .

Before 2007, the plasmapause was sometimes never encountered by Cluster, as the perigee was located around 4 RE (i.e., sometimes at higher radial distances than the plasmapause). During the investigated time period (2007–2011), the satellites orbit with the perigee located inside the plasmasphere, as close as 2 RE, allowed us to determine the electron density inside and outside the plasmasphere from WHISPER . We have used two different and complementary data sets of plasmapause positions determined from WHISPER data, during two different time periods and two different orbitographies. The first data set (April 2007 to March 2009) was determined by . During this time period, we have only used the LPP determined from C3 because all three instruments needed in this first study were functioning well only on board this satellite. We have considered only the inbound crossings because the inbound and outbound ones were separated by only a few UT hours and a limited MLT difference see, for instance, Figs. 1 and 3 of. We have further supplemented this sample by a second data set of plasmapause positions between July 2010 and December 2011 in order to have data from a time period with higher geomagnetic activity. During this time period, the inbound and outbound crossings were widely separated in both UT and MLT, so we were able to use both crossings. Note that we used all satellites available during this second time period if the data were available. The L values were extracted from the CLWeb software (http://clweb.cesr.fr) using the IGRF2000 model for the internal magnetic field and the Tsyganenko T89 model for the external magnetic field . To determine the LPP, we follow , and we searched for the sharp increase in the electron plasma frequency, looking for a density increase of at least a factor of 3 over an L distance of 0.5 RE or less with an increase up to a density larger than 20 cm-3. The location of the upper value of the density ramp is considered the position of the plasmapause. Note that the upper limit of the instrument is indeed low and induces limitations in our plasmapause determination technique. However, we have excluded from our statistical study the events with small-density gradients and small maximum electron density values (see the typical strong density gradient of our events on Fig. ). Then we consider that the events selected here give an innermost plasmapause position, not far from the plasmapause position that would be defined as the middle of the plasmasphere boundary layer. Note that in this way, we have the same plasmapause position definition as in the study of . The presence of plasmaspheric plumes were further taken into account. We simply looked for the plasmapause and checked the presence of a plume. If there was one, we took care to not use the inner boundary of the plume as the plasmapause. In Fig.  we show an example of time–frequency electric field spectrograms measured by WHISPER on board the four Cluster satellites (C1, C2, C3, and C4) on 23 October 2011, between 17:00 and 23:00 UT. The plasmapause corresponds to the sharp increase in the electron plasma frequency, as seen for instance around 18:00 UT during the C1, C3 and C4 inbound plasmapause crossing and around 18:30 UT for C2 (see, respectively, the panels 1, 3, 4 and 2 of Fig. ).

The relationships between the LPP and both the solar-wind- and Earth-based LPP indicators are then investigated for three different MLT sectors (Sector1 – night sector (01:00–07:00 MLT); Sector2 – day sector (07:00–16:00 MLT); Sector3 – evening sector (16:00–01:00 MLT)) and for all MLT taken together (details and results of the analyses are presented in Sect. 4). Sector1, Sector2 and Sector3 contain 67, 64 and 180 plasmapause crossings, respectively. Such MLT intervals were selected to ensure, as much as possible, adequate statistics in each time bin. The cross-correlation analysis is applied and the delay times of the plasmapause to LPP indicators are obtained. The time series to correlate are created as follows.

One series contains the determined LPP which refers to the specific times (e.g., 7 April 2007, 19:49:48 UT). The second time series (of the same length as LPP) consists of given LPP indicator values taken at a fixed time lag Δt with respect to LPP. A correlation coefficient is computed between these two time series and then the procedure is repeated for time lags ranging between 0 and 30 h, with a step of 1 h (data resolution). The hour (UT) at which the plasmapause crossing begins (e.g., 19:00:00 UT for the plasmapause crossing on 7 April 2007 at 19:49:48 UT) is taken as the time for the first value of the second time series.

A given X–Y correlation corresponds to the linear form Y(t)=aX(t*)+b, where X(t*) represents the value of X that occurred Δt hours before the true value of Y(t). Thus t* is the “retarded time” (t*=t-Δt). Negative lag between two quantities, e.g., BV and LPP, hereinafter denoted as the BV–LPP correlation, means that BV is delayed with respect to LPP. Such lags are not considered since they are not physical. Note that the zero time lag actually means any delay between 0 and 1 h. Further note that using an upstream monitor ACE, the obtained time lags related to the solar-wind-based LPP indicators contain the response of the magnetosphere plus the propagation time (the time that the solar wind propagates between ACE position and the nose of the magnetosphere, which is ∼1 h on average).

In a preliminary analysis we tested the various lengths of the time interval prior to the Cluster plasmapause crossings. The 24 h time range was first examined and then an additional, significant peak in the cross-correlation functions of all quantities around Δt=27 h was identified. Accordingly the time interval has been enlarged. The highest correlation coefficient in each of the MLT sectors and also when all MLT are binned together is always found at a time lag of less than 30 h, and thus we took the length of 30 h as optimal. This investigation of the most appropriate time interval prior to the plasmapause crossing indicates that it likely takes several hours for any change in the LPP to propagate around the Earth for MLT sectors other than the postmidnight one see also. As an example in Fig. , we present the cross-correlation function between BV and LPP and AE and LPP in MLT Sector1. An increase in indicator value (here BV or AE) will cause shrinking of the plasmapause (a decrease in LPP), which means that the correlation is negative. The highest-correlation-coefficient time lag (Δt=11 and Δt=5 h) is indicated in the figures with red cross. Note the appearance of the secondary peak at Δt=27 h in the bottom panel of Fig. .

In addition to showing the minimum and maximum plasmapause extension, Table  contains the number of determined LPP for the two solar activity periods: the low (2007–2009) and the increasing phase (2010–2011).

To determine the LPP, we processed the data collected by Cluster. Sometimes there are no data because the instrument is off, the satellite is off or because there are eclipses. Sometimes, the plasmapause is crossed at too high density so that its position cannot be clearly identified. This results in the unequal data distribution through considered years. Note that most of the determined LPP come from measurements obtained in 2011. The LPP ranges between 2.9 and 8.8 RE. The mean LPP obtained by averaging all 311 determined LPP values amounts to 5.6 RE. Except for 2011, the considered time span includes declining, minimum and early increasing phases of the solar cycle. During this period (2007–2010) the geomagnetic activity was mostly low, and consequently the plasmapause was located quite far from the Earth (on average at 7.9 RE). As the solar activity starts to increase in 2011, we observed that the plasmapause shrinks to as low as 2.9 RE.

To give an overview of the characteristics of the analyzed samples, we present in Table  the maximal values of 1 AU solar wind parameters (basic and derived) studied and geomagnetic indices related to the LPP closest to the Earth (2.9 RE), LPP furthest from the Earth (8.8 RE) and to all considered LPP values. Maxima are taken over the interval of 30 h prior to the plasmapause crossing. For all parameters maxima are considerably different for low and high solar activity periods. The two values given for Bz refer to southward and northward Bz orientation (Bz<0 and Bz>0).

The lowest plasmapause that occurred in 2011 is not associated with the largest solar wind parameters and also not with the largest geomagnetic indices found within the used data sets. Note that according to the highest amplitude Dst (Dst =-40 nT), geomagnetic activity was weak . However, maximal AE was significant (AE=950 nT), being even larger than the highest AE associated with the high-speed solar wind streams driving geomagnetic activity during solar cycle 23 as reported by . It may indicate that in this case, the auroral electrojet played a more important role than the ring current in the formation of the plasmapause. The largest LPP, which occurred in 2008, is preceded by much lower maximal values of both solar wind parameters and geomagnetic indices.

The maximal values of all LPP indicators are found for LPP=3.9 RE and thus not for the lowest LPP within our sample. This confirms that the individual peak values of the plasmapause indicators may not be the most appropriate measure to characterize the plasmapause position.

In the following we relate the LPP indicators to the determined LPP by applying the cross-correlation analysis. We follow the method explained in Sect. 2. Most of the analyzed data distributions indicate that two (or even three, e.g., for AE) linear least-square relationships should be adopted, one for lower and the other for higher values of the LPP indicators. Since the period studied includes mostly periods of quiet solar activity, our data sets contain only few points at higher LPP indicator values which is certainly not enough to perform a reliable regression. Thus, the linear relationships are obtained by taking all LPP values within each of the three sectors and also when all MLT are binned together.

In Table  we present the linear least-squares correlation coefficients R, the time lags Δt obtained for each of the three considered MLT sectors and all MLT taken together, and the rms errors (RMSEs) of the best fits. Note that positively defined LPP indicators (BV, dΦmp/dt, Ap, AE) are anticorrelated (negative R) with LPP, and thus their increase causes shrinking of the plasmapause. On the other hand, negatively defined LPP indicators (Bz and Dst) are correlated (positive R) with LPP. The correlations are evidently the highest in Sector1 and drop through Sector2 to Sector3. The only exception is Dst, where the correlation coefficient is somewhat higher in Sector2 than in Sector1. Between the considered solar-wind-based LPP indicators, BV is the best correlated with LPP in all sectors, and also when all MLT are binned together. The smallest differences among sectors, as quantified by R, are found for dΦmp/dt. Concerning geomagnetic indices, the highest correlation is AE–LPP in Sector1, and Dst–LPP in both Sector2 and Sector3. Note the significant decrease in AE–LPP correlation coefficients from Sector1 to Sector2. When all MLTs are binned together, correlation coefficients for the AE–LPP and Ap–LPP are the same and lower than the correlation coefficient for Dst–LPP relationship. The obtained time lags increase from Sector1 to Sector3, for all LPP indicators. For Bz and dΦmp/dt time lags are identical, except in Sector3. The time lag for BV–LPP is exceptional in Sector1, being the largest Δt found in this sector. Similar Δt are obtained for Ap and AE in all three sectors. Notably shorter Δt are obtained for Dst (4–7 h shorter depending on the sector). It is interesting to note that for almost all LPP indicators, we found a second peak at a time lag of around 27 h (see Fig. , bottom). This additional peak found at larger Δt probably causes the large Δt values when all MLT are considered together. This should be investigated in a further study. The RMSEs are approximately 0.6–1.2 RE in all sectors. For all indicators the RMSEs are the largest in Sector2. For solar wind parameters, the largest and the lowest RMSEs are found for Bz and BV, respectively, regardless of the sector. A large RMSE for Bz–LPP most likely reflects the fact that a northward-oriented Bz also plays an important part in eroding the plasmapause, at least at lower Bz values. For instance, LPP around 3.7 RE is observed at Bz oriented northward. Note that these values are very similar to the lowest LPP values found for southward Bz. Concerning geomagnetic indices, the lowest RMSE is obtained for AE in Sector1 and for Dst in both Sector2 and Sector3 and all MLTs.

Linear least-squares fit coefficients for the highest-correlation time lag used to obtain the relationships between the LPP and considered LPP indicators are presented in Table . For all LPP dependencies the slopes are the steepest in Sector2. The ratio between the slope in Sector2 and that in Sector1 or Sector3 is ∼4 for Ap–LPP, while it is ∼2 for the other LPP indicators. However, note that the slopes in Sector3 are somewhat lower than the slopes in Sector1. Thus, the same change in the specific LPP indicator will likely cause the largest movement of LPP in Sector2 and the smallest in Sector3. According to parameter b of the linear least-squares fits, for all investigated LPP dependences, the smallest plasmapause extension is allowed in the night sector (Sector1) and its largest extension in the day sector (Sector2) due to the plume formation.

We show the BV–LPP, AE–LPP and Dst–LPP scatterplots for the highest-correlation time lag in Figs. a–d, a–d and a–d, respectively. The fitted linear relations (solid lines) clearly show a trend of decreasing LPP with increasing BV and AE and increasing LPP with decreasing Dst. However, all displayed data distributions show that LPP only shrinks to a particular value as the LPP indicator increases and then appears to saturate. Namely, there is no LPP below ∼3, 3.8, 3.3 and 3 RE in Sectors1–3 and all MLT, respectively. Since the majority of our LPP values are placed at lower values of the analyzed LPP indicators, this must be taken only as an indication of the general plasmapause behavior.

The most probable LPP values in each of the three sectors and for all MLT are determined using the calculated linear fits for all of the LPP indicators. Table  contains the fitted LPP values for low and high activity. The indicator values at high activity are those at which LPP comes closest to the Earth. This happens in Sector2 for all LPP dependencies, and we associate LPP∼2.5RE in Sector2 with high activity.

Note that these indicator values for high activity, which cause significant shrinking of the plasmapause, are associated with the stronger values of LPP indicators although they are of moderate intensity, as seen in Table  (e.g., Dst =-40 nT). The reason is that our analysis is based on the LPP indicator value at the highest-correlation time lag instead of the interval maxima, as noted before.

Taking into account the RMSE given in Table , information about LPP reported in Table  shows that at low activity, the plasmapause is located closest to the Earth in Sector1 and furthest away from it in Sector2. At higher activity the closest plasmapause is found in Sector2 and the furthest away in Sector3. These results reveal the MLT asymmetries at both low and higher activity levels. During low activity, day–night asymmetry is more prominent (with the bulge on the dayside). As activity increases, the bulge is displaced toward the evening, and day–evening asymmetry becomes more prominent. Interestingly, all the LPP indicators used provide us with the same conclusion.

The cross-correlation analysis was applied to study the plasmapause position LPP, determined using the WHISPER instrument on board the Cluster satellites, as a function of various solar wind and Earth-based LPP indicators. The maximum (in an absolute sense) value of the LPP indicators that precedes the plasmapause crossing is generally higher for smaller LPP than for larger LPP. However, our analyses show that the value at the highest-correlation time lag is more appropriate for describing the plasmapause responses to any disturbances rather than the maximum values in the prevailing interval before the LPP, as commonly used in other studies e.g.,and references therein. Thus, the obtained results (fit parameters) cannot be directly compared, but general findings confirm those of previous research . All studies show that the plasmapause is closer to Earth during geomagnetically active periods, with the plasmapause bulge displaced toward dusk.

Delay times of the LPP in relation to the arrival of LPP indicators were obtained. The values range from 0 to 27 h, depending on the MLT sectors and on the LPP indicators. The analysis is performed for three different MLT sectors (Sector1 – night (01:00–07:00 MLT); Sector2 – day (07:00–16:00 MLT); Sector3 – evening (16:00–01:00 MLT)) and for all MLT taken together. Based on the correlation coefficients and RMSE, we conclude that all LPP indicators studied are capable of describing the observed plasmapause position well. Among solar wind coupling functions, BV is found to be a slightly superior LPP indicator in all sectors and for all MLT binned together. As regards geomagnetic indicators, AE is found to be the best one in Sector1 and Dst is the best in Sector2, Sector3, as well as for all MLT. also found that AE is particularly effective in the night and dawn sectors. However, no MLT dependence is visible in their Dst model.

Among all indicators, Bz provides the least reliable LPP. Generally, the correlations are the highest in Sector1 and decrease through Sector2 to Sector3. Our Bz–LPP correlation for all MLTs is somewhat lower than the one obtained by and the time lag is very different. The discrepancy may result from different methodology and different plasmapause observations used in both studies.

The obtained time lags increase from Sector1 to Sector3, for all LPP indicators. The time lag for BV–LPP is exceptional in Sector1 and amounts to 11 h. Similar delays are obtained for Ap and AE in all three sectors. Notably shorter time delays (4–7 h shorter depending on the sector) are obtained for Dst, suggesting that the ring current may play an important role in quickly peeling off the plasmapause via non-convection processes.

Since the highest correlation coefficients and the fastest plasmapause response to different LPP indicators are in Sector1, the following simple scenario may be involved. The information about the history (e.g., strength and variability) of any of the LPP indicators during 30 h previous to the LPP changes is stored in the plasmasphere. After approximately 2–5 h (note again that BV is an exception), the plasmasphere responds and begins to erode on the nightside. Due to the Earth's rotation, the information is passed to the dayside and later to the evening sector. According to all indicators studied, it takes about 24 h for any change to propagate all around the Earth. This may suggest that there is some instability that likely propagates around the Earth, which is consistent with the mechanism of interchange instability proposed by . Our analysis further indicates that this instability propagates with a velocity that may slightly differ from the corotation velocity.

The scatter around the fit of the plasmapause is larger for lowest values of LPP indicators as noted also by . This is the most prominent in Sector2. According to our findings, LPP exhibits the largest values on the dayside (somewhere between 07:00 and 16:00 MLT) and smallest values in the postmidnight sector during low activity levels. This is in agreement with . By contrast, noted a slight asymmetry in the noon–midnight direction, with an LPP peak in the night sector. During more active periods, we observed that LPP peaks in the evening sector (between 16:00 and 01:00 MLT) and the smallest plasmapause expansions are found on the dayside (between 07:00 and 16:00 MLT). This is in agreement with results presented by and , while this asymmetry was not found in any other studies e.g.,. Further, at enhanced magnetic activity, we observed a tendency for LPP to saturate as there is no LPP below a certain value (depending on the MLT sector). However, at high LPP indicator values, we do not have sufficient data points to make a general conclusion.

We plan to continue this study by enlarging our LPP data set, possibly during a period of higher solar activity. This will allow us to verify the obtained results and to more reliably constrain the lower limit of LPP for various MLT sectors. Further, the possibility to perform the analyses looking at narrow MLT sectors will enable us to more precisely identify both the MLT in which the plasmapause is formed and the displacement of the bulge during the active magnetic period. With the extension of our study, we hope to get a better insight into the physical mechanisms responsible for the plasmapause formation. This is very important since the plasmapause plays a crucial role in the propagation of the mass and energy distribution within the inner magnetosphere.