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Interplanetary electric fields and their relationship to low-latitude electric fields under disturbed conditions
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Journal of Atmospheric and Solar-Terrestrial Physics 69 (2007) 1147–1159 www.elsevier.com/locate/jastp
Interplanetary electric fields and their relationship to low-latitude electric fields under disturbed conditions Adela Anghela,, David Andersona, Naomi Maruyamaa, Jorge Chaub, Kiyo Yumotoc, Archana Bhattacharyyad, S. Alexd a
Cooperative Institute for Research in Environmental Sciences, National Centers for Environmental Prediction National Weather Service, National Oceanic and Atmospheric Administration (NOAA), University of Colorado and Space Environment Center, 325 Broadway, Boulder, CO 80305-3337, USA b Radio Observatorio de Jicamarca, Instituto Geofisico del Peru, Peru c Space Environment Research Center, Kyushu University, Japan d Indian Institute of Geomagnetism, India Received 12 April 2006; received in revised form 7 July 2006; accepted 31 August 2006 Available online 31 March 2007
Abstract Recent studies have demonstrated that ground-based magnetometer observations can be used to infer realistic, daytime vertical E B drift velocities in the Peruvian and Philippine longitude sectors. It has also been demonstrated that under certain conditions the time variability in the interplanetary electric field (IEF)—minutes to hours—is reflected in the daytime, prompt penetration of high-latitude electric fields to low latitudes. In this paper, we incorporate magnetometerinferred E B drift techniques to extend this study to include the Indian sector E B drift velocities and to investigate the relationships between IEF conditions and daytime, low-latitude electric field observations under both geomagnetically quiet and disturbed conditions. This paper addresses several basic questions related to the relationships between IEF conditions and low-latitude east–west electric fields. (1) When low-latitude electric fields exhibit quiet-time, Sq-type behavior, what are the IEF conditions? (2) Under disturbed conditions, what are the relationships between the IEF parameters and the low-latitude electric fields in the Peruvian, Philippine, and Indian longitude sectors? (3) If the three longitude sector electric field responses are similar under disturbed conditions, is the response consistent with the current ideas put forward at the Millstone Hill Workshop on promptly penetrating electric fields and over-shielding effects at low latitudes? We address the above questions by analyzing magnetometer-inferred E B drift velocities between January 2001 and December 2004 when there exists more than 500 quiet days and more than 235 geomagnetically disturbed days, defined by daily Ap values greater than 20. It is suggested that the neural network approach that provides realistic E B drift velocities based on magnetometer observations can be applied at any longitude where appropriately placed magnetometers exist. It is found that: (1) the average quiet, daytime upward E B drift velocity vs. LT in the Indian sector is comparable to the average velocity vs. LT in the Peruvian sector and both are roughly 3–5 m/s less than the values in the Philippine sector; (2) under quiet conditions, the peak velocity occurs at 1100 LT in the Peruvian sector and at 1000 LT in both the Philippine and Indian sectors; and (3) during disturbed conditions, it is observed that daytime, promptly
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E-mail address: [email protected] (A. Anghel). 1364-6826/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jastp.2006.08.018
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penetrating electric fields occur, simultaneously, in the Philippine, Indian and Peruvian sectors, in response to fluctuating IEF conditions. r 2007 Elsevier Ltd. All rights reserved. Keywords: Low-latitude electric fields; Interplanetary electric fields; Neural networks
1. Introduction The daytime, vertical E B plasma drift in the equatorial F-region of the ionosphere is the primary transport mechanism for determining the electron density profiles between 7201 dip latitude as a function of altitude, latitude, and local time. As measured by radars at Jicamarca Radio Observatory (JRO), Peru, the vertical plasma drifts show large day-to-day variability, which is responsible for large day-to-day fluctuations in the low-latitude plasma density distribution. However, the equatorial drift observations are sporadic and irregular, but knowing the vertical drifts globally, more realistically, and on a day-to-day basis is very important since several theoretical models require global and more realistic vertical drifts in order to improve the accuracy in ionospheric parameter specification (e.g., Anderson et al., 1987; Bailey et al., 1993; Preble et al., 1994). Also, knowing the vertical drifts globally and more realistically, the effects of promptly penetrating high-latitude electric fields to low latitudes, during geomagnetic storms and substorms, can be studied theoretically at a number of different longitudes. Therefore, it is not surprising that in the last decades a major effort has been devoted to obtain global empirical models of the vertical plasma drifts using radar, magnetometer, satellite, and ionosonde observations (e.g., Richmond et al., 1980; Fejer and Scherleiss, 1995; Batista et al., 1996; Scherliess and Fejer, 1999; Anderson et al., 2002, 2004). Previous studies showed that at equatorial latitudes, the daytime vertical E B plasma drifts can be estimated from the ground magnetic signature of the equatorial electrojet (EEJ). However, the dayside magnetic field measurements are difficult to interpret since during daytime the magnetometers located on the magnetic equator record not only the EEJ induced magnetic fields but also magnetic fields induced by other remote currents such as the ring current. Gonzales et al. (1979) separated the magnetic contribution of the EEJ by subtracting the magnetic field measured at a
station off the magnetic equator from that measured at a station situated on the magnetic equator. Rastogi and Klobuchar (1990) estimated the daytime vertical E B drifts by comparing the difference in the magnitudes of the horizontal H component between two magnetometers located on and off the dip equator, DH, with total electron content (TEC) observations. However, the previous studies presented only qualitative relationships between the equatorial, daytime vertical E B plasma drifts, and the magnetic field data. Only recently, Anderson et al. (2002) showed that there exist quantitative relationships and they developed linear relationships based on Jicamarca incoherent scatter radar (ISR) drift observations and magnetometer data from Canete and Piura, Peru, for a solar maximum period and for a limited number of days. Using a significantly larger database of vertical plasma drifts from the Jicamarca unattended longterm ionosphere atmosphere (JULIA) radar and magnetometer observations from Piura and Jicamarca, Anderson et al. (2004) determined quantitative relationships over a longer period of time from August 2001 to December 2003. Three different approaches were considered for data analysis: a linear regression analysis, a polynomial regression approach, and a neural network approach. The input space consisted of eight variables: year, day of the year, F10.7 cm solar radio flux, 90-day average F10.7 cm solar radio flux, daily Ap, 3-hourly magnetic kp index, local time, and DH. The relationships were successfully validated using an independent set of Jicamarca ISR vertical drift velocities. They found that the neural network approach is more suitable for capturing the nonlinearity of the DH vs. E B drift relationship. Additionally, they conducted a sensitivity analysis and reported that DH is the most significant input parameter and each other input contributed with less than 10% in reducing the root-mean-square (RMS) error. In a recent paper, Anderson et al. (2006) showed that the same neural network drift model based on
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Peruvian observations can be successfully applied at the Philippine longitude sector. The neural network drift model was trained with Peruvian magnetometer and vertical drift observations between August 2001 and February 2005. For the Philippine sector, they used magnetometer observations from Davao and Muntinlupa between 2001 and 2004. Their study concentrated on geomagnetically quiet days, with daily Ap values less than 10. Basically, the data from the Peruvian and Philippine longitude sectors were binned into three seasons and DH-inferred E B drifts were determined for each day. Then, the average vertical drift patterns were calculated and compared with the Fejer–Scherliess climatological (Scherliess and Fejer, 1999) quiet-day curves for each season and for each longitude sector. They found excellent agreement in the Peruvian sector and very good agreement in the Philippine sector (see their Figs. 4–9). The implications of their results are significant, suggesting that realistic vertical plasma drifts can now be obtained globally and on a day-today basis, wherever appropriately placed magnetometers exist. Expanding on their studies, in this paper we investigate the possibility and the implications of applying the Peruvian neural network drift model at other longitude sectors by conducting a quiettime and a disturbed-time analysis. To train the network, we used the same training set as in Anderson et al. (2006) and included the solar zenith angle (SZA) as an additional input to the network. The neural network drift model was then employed to provide realistic, daytime vertical E B drift velocities at the Indian longitude sector based on magnetometer observations from Tirunelveli and Alibag, spanning the period from January 2001 to December 2002 and between 0700 and 1700 LT. Our study concentrated on geomagnetically quiet days, with daily Ap values less than 10, and geomagnetically disturbed days, with daily Ap values more than 20. For the quiet-time analysis, as described in Anderson et al. (2006), the Indian data were binned into three seasons, and the seasonal DH-inferred E B drift patterns were calculated and compared with Fejer–Scherliess drift patterns. As part of the quiet-time analysis, we also investigated the longitudinal variability of the quiet-time E B drift patterns at Peruvian, Philippine, and Indian longitude sectors. At equatorial latitudes, daytime ionospheric electric fields drive both the EEJ in the E-region
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of the ionosphere and the F-region plasma motions. The relative efficiency with which the EEJ and the vertical plasma drifts are driven varies with the time of the day, from day-to-day, with season, solar cycle, and longitude, and during periods of enhanced geomagnetic activity, the efficiency is strongly affected by magnetospheric and disturbance dynamo effects (Scherliess and Fejer, 1999). During enhanced geomagnetic activity periods, large ionospheric electric field and current perturbations travel from high to equatorial latitudes changing the ionization distribution over large areas and controlling the generation of ionospheric plasma irregularities (Fejer and Scherliess, 1995). The most important sources of low-latitude ionospheric electrodynamic disturbances are the prompt penetration and the disturbance dynamo electric fields. In this paper, we use equatorial vertical E B drift observations and interplanetary electric field (IEF) measurements to study the storm-driven penetration effects at different longitudes in order to improve our understanding of the electrodynamic response of the low-latitude ionosphere to enhanced geomagnetic activity. Therefore, for the disturbed-time analysis, we compared the variations observed in the IEFs with those in the low-latitude zonal electric fields at the Peruvian, Philippine, and Indian longitude sectors. The IEFs were derived from the advanced composition explorer (ACE) satellite measurements, and the zonal electric fields were obtained from the Jicamarca ISR or JULIA vertical drift observations or were calculated from the DH-inferred E B drifts, knowing that 1 mV/m corresponds to a vertical drift of 40 m/s in the Peruvian sector, 28 m/s in the Philippine sector, and 20 m/s in the Indian sector. The specific questions that will be address in this paper are: (1) How well do the quiet-time DH-inferred E B drift velocities compare with the Fejer–Scherliess climatological model in the Indian sector and (2) What are the relationships between the IEFs and low-latitude zonal electric fields during geomagnetically disturbed periods? In the subsequent sections we will: (1) outline our data analysis approach, (2) present quiet-time comparisons with the Fejer–Scherliess model, (3) relate IEF conditions with low-latitude zonal electric fields under geomagnetically disturbed conditions, and (4) summarize the results and present the significance and implications of our findings from a space weather perspective.
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2. Processing approach and data description 2.1. Neural network approach In this paper, a multilayer feedforward neural network (e.g., Masters, 1993; Haykin, 1994; Demuth and Beale, 2001) was employed in estimating the equatorial, daytime vertical E B drift velocities from magnetometer DH observations at the Peruvian, Philippine and Indian longitude sectors. The neural network-based drift model is similar with the one described in Anderson et al. (2006). Since only Peruvian data were used for training the network, to account for the seasonal variations attributed to the displacement of the geographic and geomagnetic equators at different longitudes, the SZA was included as an additional input to the network. The SZA is the Sun’s angular distance from the vertical that relates to the geographic latitude of the site, the declination angle, and the hour angle, and depends on season and local time. However, there was less than 10% improvement in the RMS error for the entire Peruvian training set, and no significant changes in the estimated drifts were obtained at the Philippine and Indian sectors. This might be explained by the fact that the SZA is already an implicit input parameter, being an intrinsic part of DH. For this study, the magnetometer data sets were obtained from magnetometers located at three different longitude sectors: Peru, Philippine, and India, the vertical drifts from the Jicamarca ISR and JULIA radars, and the interplanetary measurements from the ACE satellite. Below is a brief overview of the instruments and data processing techniques. 2.2. Magnetometers Magnetometers operating at Jicamarca (geog. 11.91S, 283.11E, 0.81N dip latitude) and Piura (geog. 5.21S, 279.41E, 6.81N dip latitude) provided magnetometer observations in the Peruvian longitude sector. In the Philippine longitude sector, Prof. K. Yumoto supplied magnetometer observations from Davao (geog. 71N, 125.41E, 1.321S dip latitude) and Muntinlupa (geog. 14.371N, 121.021E, 6.391N dip latitude) (Yumoto, 2001). In the Indian sector, Dr. Bhatacharyya provided magnetometer data from Thirunelveli (geog. 8.71N, 76.91E, 0.51S dip latitude) and Alibag (geog. 18.61N, 72.91E, 101N dip latitude). The procedure
to calculate DH is detailed in Anderson et al. (2002) and can be applied at any longitude sector. Basically, at any longitude sector where two appropriately placed magnetometers exist, one on the magnetic equator and the other displaced 6–91 away, for each magnetometer, the H component nighttime baseline, calculated as a 10-h average between 1900 and 0500 LT, is first obtained for each day and then subtracted from the daytime values. Finally, DH is calculated as the difference in H between the resulting daytime values at the two magnetometers. Fig. 1 shows the Jicamarca H component observations—after subtracting the nighttime 10-h average baseline value—(dark blue dots), and DH values (light blue dots), between Jicamarca and Piura, as function of the vertical E B drifts obtained from the Jicamarca ISR and JULIA radars. The plots were produced based on data from all the 463 quiet and disturbed days used for training the neural network drift model. From these plots it is clear that there is an improved correlation for DH vs. E B drifts as compared with Jicamarca H vs. E B drifts. Also, the plots show a much larger scatter for Jicamarca H vs. E B drifts than for DH vs. E B drifts. This reinforces the findings of Rastogi and Klobuchar (1990) and Kane (1973) that it is more appropriate to qualitatively and quantitatively estimate the vertical E B drifts using DH measurements rather than using only H component observations from a single equatorial magnetometer. However, since the correlation coefficients, R, for the two cases have very close values, it seems that it might be possible to quantitatively estimate the vertical drifts based only on the equatorial H component observations. This could be useful for estimating the vertical drifts at longitudes where a single equatorial magnetometer is available. We plan on using equatorial H measurements alone as inputs to a neural network drift model and evaluate the performances of the model for this case. 2.3. ACE data To describe the interplanetary conditions, we used 64-s averages of merged ACE MAG-SWEPAM Level 2 interplanetary magnetic field (IMF) and solar wind velocity data (http://www.srl.caltech.edu/ACE/ ASC/level2/lvl2DATA_MAG-SWEPAM.html). The magnetic field experiment MAG on ACE provides continuous measurements of the local magnetic field
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Fig. 1. Jicamarca H component (dark blue dots) and DH (Jicamarca–Piura) (light blue dots) vs. vertical E B drifts from Jicamarca ISR and JULIA radars, for all the 463 quiet and disturbed days used for training the neural network drift model. The linear slopes and the correlation coefficients, R, are also indicated for the two cases.
in the interplanetary medium, and the solar wind experiment SWEPAM provides solar wind velocities. The two measurements are important in establishing the dawn-to-dusk component of the IEF, IEF Ey ¼ Vx Bz, that can promptly penetrate during storm times to equatorial latitudes until a shielding dusk-to-dawn electric field has time to develop in the inner magnetosphere. To analyze the storm-driven penetration effects at equatorial latitudes, we timeshifted the IEF Ey measured at the spacecraft and then compared the time shifted IEF Ey with the lowlatitude zonal electric fields at different longitudes (Kelley et al., 2003). 3. Quiet-time studies Several studies investigated the longitudinal variability of the equatorial F-region plasma drifts using satellite (e.g., Coley et al., 1990) and combined radar and satellite observations (e.g., Scherliess and Fejer, 1999). In this section, in addition to comparing the average DH-inferred drift patterns at the Indian sector with the Fejer–Scherliess quiet-day curves for different seasons, we also investigate the longitudinal variability of the quiet-time drift patterns at Peruvian, Philippine, and Indian longitude sectors. The Peruvian and Philippine day-today variability of the daytime E B drifts and the quiet-time drift patterns are discussed at length in Anderson et al. (2006), and here are mentioned some of their results for completeness but place
more emphasis on the vertical drifts at the Indian sector. At all three longitudes, the daytime DH-inferred drifts were estimated using the neural network drift model described in the previous section of the paper, making the assumption that the same neural network trained with Peruvian data can be applied at any other longitude sector. For the quiet-time analysis, we chose only those days when the daily Ap values were less than 10. Adopting the same constraints to our data that were also used by Scherliess and Fejer (1999) in developing their model, the Indian quiet-day observations were binned into three seasonal periods, the vertical DH-inferred E B drifts were determined for each day, and the average DH-inferred E B drift patterns were calculated and compared with Fejer–Scherliess drift patterns for each season. Three seasons were considered: (1) June solstice (May–August), (2) December solstice (November–February), and (3) Equinox (March– April, September–October). At the Indian sector, the daytime vertical E B drift velocities were calculated for 250 quiet days based on magnetometer observations from Tirunelveli and Alibag, between January 2001 and December 2002. Fig. 2 displays the daytime drift velocities as a function of local time at the Indian longitude sector for these quiet days with available magnetometer data. The solid red lines represent the average DH-inferred drift values and the solid blue lines are the average Fejer–Scherliess drifts. The best agreement between
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Fig. 2. Daytime vertical E B drift velocities vs. local time at the Indian longitude sector for all the 250 quiet days with available magnetometer data during the Equinox period, and June and December solstices. The solid red lines are the average DH-inferred drift patterns and the solid blue lines the average Fejer–Scherliess quiet-day curves.
the two average curves was obtained for the Equinox period. For the Peruvian and Philippine sectors, we used the same data sets as described in Anderson et al. (2006). At the Peruvian longitude
sector there were 488 quiet days of Jicamarca and Piura magnetometer observations between January 2001 and December 2004. From the Philippine sector, we had 366 quiet days of magnetometer
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Fig. 3. Quiet-time E B drift patterns vs. local time for the Equinox period, and June and December solstices in the Peruvian, Philippine, and Indian longitude sectors. The red lines are the average DH-inferred drift patterns and the blue lines the average Fejer–Scherliess quietday curves. The number of quiet-days worth of data for each season and longitude sector is indicated on each panel.
observations from Davao and Muntinlupa extending from January 2001 through May 2004. The numbers of quiet-days worth of data for each season and longitude sector are indicated on the panels in Fig. 3. Fig. 3 displays the average, daytime DH-inferred and Fejer–Scherliess vertical E B drift patterns as a function of local time for all quiet days with available magnetometer data during June solstice period (Fig. 3a), Equinox (Fig. 3b), and December solstice (Fig. 3c) in the Peruvian, Philippine, and Indian longitude sectors, respectively. In all panels, the solid red lines are the average patterns of the DH-inferred E B drift velocities while the solid blue lines are the Fejer–Scherliess average drift patterns. The error bars represent the standard deviations associated with these seasonal and longitudinal quiet-time drift patterns. As seen in Fig. 3, in all studied cases, the standard deviations corresponding to the DH-inferred E B drift patterns were close to 5 m/s around the noon hours and decreased gradually to about 2.5 m/s early in the morning and late afternoon hours.
Fig. 3a depicts the quiet-time drift patterns for the June solstice period at all three longitude sectors together with the corresponding standard deviations. At the Peruvian longitude sector, as shown in the left panel of Fig. 3a, the average DH-inferred drift curve for this period has its maximum value of 18 m/s around 1100 LT and the average Fejer–Scherliess drift curve has its maximum of 20 m/s at about the same time. Overall, the two curves are in excellent agreement. At the Philippine longitude sector, as illustrated in the middle panel in Fig. 3a, there are slight differences in amplitude and shape between the two average curves. At this longitude, the maximum average drift values are reached at about 1000 LT, for the Fejer–Scherliess drift pattern the maximum being 25 m/s and for the DH-inferred drift pattern 20 m/s. For the Indian longitude sector, the June solstice period is represented in the right panel in Fig. 3a. As seen in this figure, the Fejer–Scherliess model predicts a maximum of 24 m/s around 1000 LT and the average DH-inferred drift curve has a maximum of 17 m/s about 30 min earlier. Our data shows a large
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discrepancy in amplitude and shape between the two average drift curves. In summary, for the June solstice period, the average DH-inferred drift patterns at all three longitude sectors present some similarities in amplitude and shape: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with Indian drift values smaller than in Philippines; (2) at the Indian and Peruvian sectors, the patterns are very close in amplitude. Conversely, the average drift patterns as estimated by the Fejer–Scherliess model are similar in amplitude and shape at the Indian and Philippine longitude sectors and very different from the Peruvian pattern. The quiet-time drift patterns for the Equinox period at all three longitude sectors together with the corresponding standard deviations are shown in Fig. 3b. As seen in the left panel of Fig. 3b, there is an excellent agreement between the Peruvian average drift patterns as estimated by the neural network drift model from magnetometer data and Fejer–Scherliess model, with the maximum drift values of 22 m/s obtained at 1100 LT. For the Philippine and Indian sectors, there are differences between the average DH-inferred drift patterns and the average climatological patterns as seen in the middle and right panels of Fig. 3b, respectively, with the climatological patterns slightly higher throughout the day. Respective maximum values for the neural network model and Fejer–Scherliess climatology model are 24 and 25 m/s in the Philippine sector and 22 and 25 m/s in the Indian sector, with all four maximum values occurring at 1000 LT. As for the June solstice period, for Equinox, the average DH-inferred drift curves at all three longitude sectors present some similarities: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with the Indian drift values slightly smaller than in the Philippines; (2) at Indian and Peruvian sectors, the patterns are very close in amplitude. The average Fejer–Scherliess drift patterns are similar in shape and amplitude at the Indian and Philippine sectors with slightly higher values than the Peruvian pattern. At all three longitude sectors, the data show the largest differences between the average DH-inferred drift patterns and the average Fejer–Scherliess drift curves during the December solstice, as shown in Fig. 3c. The left panel of the Fig. 3c displays the Peruvian patterns indicating a maximum drift value of 15 m/s at about 1100 LT as calculated with the neural network drift model and 17 m/s more than 30 min earlier as predicted by the Fejer–Scherliess
model. As shown in the middle panel, the Philippine patterns, although very different in shape throughout the day with the DH-inferred average drift curve lying significantly below the climatological curve except near midday, reach the same maximum value of 20 m/s more than 30 min before 1100 LT. In the Indian sector, the average DH-inferred drift pattern has its maximum of 15 m/s around 1000 LT, while the climatological pattern reaches a maximum value of 18 m/s about 30 min later, as seen in the left panel of Fig. 3c. As observed for the other two seasons, the average DH-inferred drift curves at all three longitude sectors present some similarities in amplitude and shape: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with Indian drift values smaller than in Philippines; (2) at the Indian and Peruvian sectors, the patterns are very close in amplitude. The average Fejer–Scherliess drift patterns are different in shape at the Indian and Philippine sectors and present slightly higher values than in the Peruvian sector. In conclusion, for each season, the average DH-inferred drift curves at all three longitude sectors present some similarities: (1) at the Indian and Philippine sectors, the patterns are similar in shape with the maximum values around 1000 LT and with the Indian drift values smaller than in the Philippines; (2) at the Indian and Peruvian sectors, the patterns are close in amplitude, reaching maximum values in the Peruvian sector around 1100 LT. As observed in our study, for each season, the average Fejer–Scherliess drift patterns are relatively similar in shape and amplitude at the Indian and Philippine sectors, where the maximum values are reached around 1000 LT, and have slightly higher values than in the Peruvian sector, where the maximum values are around 1100 LT. Our comparisons between the average vertical DH-inferred drift patterns with the Fejer–Scherliess quiet-day curves show excellent agreement in the Peruvian sector especially for the June solstice and Equinox, good agreement in the Philippines especially at Equinox, and reasonable agreement in the Indian sector especially at Equinox. The best agreement between the climatology and neural network average patterns was obtain for the Equinox period at all three longitude sectors. It is worth noting that the shape and variability of the daytime, vertical DH-inferred drift curves truthfully reflect the daytime shape and variability of the DH values, since DH is by far the most important input parameter for the neural network drift model.
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In this section, we have clearly demonstrated that the DH vs. E B drift relationships that were developed in the Peruvian longitude sector can be applied in the Philippine and Indian sectors to provide realistic E B drift velocities. This is an important and significant conclusion since it means that, now, the day-to-day, ‘‘weather’’ aspects of daytime E B drift velocities can be investigated at different longitudes and related to IEF conditions. This is demonstrated in the next section for ‘‘disturbed’’ conditions where the daily Ap values are greater than 20. 4. Disturbed-time studies Compared with quiet-time E B drift patterns, low-latitude plasma drifts and currents respond quite differently during geomagnetically disturbed periods. Under steady-state conditions, the low- and mid-latitude ionosphere is shielded from the highlatitude convection by ions at the low-latitude edge of the plasma sheet (Wolf, 1975; Sakharov et al., 1989). During periods when high-latitude convection patterns are undergoing large changes, highlatitude electric fields ‘‘leak through’’ the shielding layer. Evidence for this penetration of electric fields to the equatorial regions is presented by Gonzales et al. (1983) and Fejer et al. (1990). Kikuchi and Araki (1979) suggested that the electrostatic fields travel to the equatorial regions instantaneously, by the zeroth-order TM mode in the earth–ionosphere waveguide. There are two major sources of low-latitude electric field disturbances during geomagnetically active periods. One originates from the magnetosphere and the other is due to the ionospheric disturbance dynamo. The latter results from the dynamo action of the storm time winds as a result of enhanced energy deposition at high latitudes. Scherleiss and Fejer (1997) developed an empirical model that describes the storm time dependence of the equatorial disturbance dynamo electric fields. When the cross polar cap potential suddenly increases as a result of an increase in the dawn-todusk, cross-tail electric field, this electric field can promptly penetrate to the equatorial region (undershielding) until a shielding electric field (dusk-todawn) has had time to develop in the inner magnetosphere (Wolf, 1983; Sazykin, 2000). This electric field shields the inner magnetosphere from strong convection fields. When the strong convection field decreases due to a northward turning of
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the IMF, the dusk-to-dawn electric field can promptly penetrate to low-latitudes (over-shielding) until the shielding layer and overall magnetospheric configuration readjust. In the figures that follow, we compare the IEF conditions observed by the ACE satellite, time shifted to the magnetopause location, with the lowlatitude zonal electric fields inferred from magnetometer observations in the Peruvian, Philippine, and Indian longitude sectors. For each figure, the various quantities are plotted as a function of universal time (UT) rather than local time (LT). In the Peruvian sector, 0000 UT is 1900 LT; in the Philippine sector, 0800 LT; and in the Indian sector, 0500 LT. In each figure, the orange line represents the daytime (0700–1700 LT), DH-inferred, vertical E B drift velocity and the corresponding eastward electric field in the Peruvian sector (1 mV/m 40 m/s). The red line corresponds to the daytime values in the Philippine sector (1 mV/m 28 m/s) while the light blue line represents daytime values in the Indian sector (1 mV/m 20 m/s). The green line represents the Jicamarca ISR drift and electric field observations over the 24 h day. In Figs. 4–6, the notation in the boxes to the right of the panels are defined here: ISR—Jicamarca incoherent scatter radar; Est. (DM)—Philippine sector magnetometers (Davao– Muntinlupa); Est. (TA)—Indian sector magnetometers (Thirunelvelli–Alibag); Est. (JP)—Peruvian sector magnetometers (Jicamarca–Piura). Fig. 4 depicts the IEF Ey conditions and the DH-inferred drifts for April 17–19, 2001. The thin lines in the second panel in Fig. 4 represent the Fejer–Scherliess climatological drift values for this period and at each of the three longitude sectors. For all of April 17, the IEF Ey conditions are ‘‘quiet’’ with Apo20 and the Peruvian, Philippine, and Indian sectors reflect quiet-time drifts of approximately 20 m/s in each sector. As shown in Fig. 4 at 0100 UT on 18 April, Bz suddenly turns southward leading to a strong, positive value for IEF Ey of 10 mV/m. This occurs when the Philippine sector is in daylight and the DH-inferred E B drift velocity begins to respond to IEF Ey conditions. Referring to the bottom panel of Fig. 4, between 0200 and 0400 UT on the 18th, that are marked by the black arrows, the low-latitude zonal electric field in the Philippine sector matches the IEF Ey/10 curve almost exactly for the entire period. This is a period of extended IMF Bz south condition, which Huang et al. (2005) would categorize as an extended penetration electric field.
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Fig. 4. IEF Ey and Ez values for April 17–19, 2001 (top); equatorial E B drift velocities vs. UT at the Peruvian, Philippine, and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological drift values (middle); IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT, between 0000 and 1200 UT, on April 18, 2001 (bottom).
Fig. 5. IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT for January 11, 2002, at the Peruvian, Philippine, and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological values.
During this period of interest, when the IMF Bz component turned southward and remained stably southward for more than 2 h, the low-latitude zonal electric field at Philippines maintained an enhanced
and stable amplitude. For 18 April, the daily Ap is 50 so this represents a fairly disturbed day. At 0430 UT on 18 April, there is a sudden northward turning of IMF Bz and a sudden
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Fig. 6. IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT for July 6, 2002, at the Peruvian and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological values.
decrease in the upward E B drift velocity in both Philippine and Indian sectors (second black arrow). Simultaneously, in the nighttime Peruvian sector there is a sudden increase in the vertical E B drift. This is all consistent with the idea that, for overshielding, the electric fields at low latitudes are directed from dusk-to-dawn which means that the daytime E B drifts will be downward and the nighttime E B drift velocities will be upward. The third black arrow at 29 UT, points to the sudden turning of the IEF Ey conditions from negative to positive values and the prompt penetration electric fields in the Peruvian, Philippine, and Indian sectors. For this case, the penetrating electric field is ‘‘dawn-to-dusk’’, which means that in the Peruvian nighttime sector, the electric field is westward (downward drift) and in the daytime Philippine and Indian sectors the electric field is eastward (upward E B drift). The fact that the daytime drifts are so small in the Philippine and Indian sector implies that the disturbance dynamo electric field is fairly strong and acts to cancel the penetration electric field in these two longitude sectors. While January 11 and July 6 2002, have been listed as disturbed days, they are only slightly disturbed, since the respective Ap values are 21 and 23. Even so, as discussed below, the factor of 10 between the IEF Ey values and the low latitude, daytime electric fields seems to be maintained. Fig. 5 shows the IEF Ey conditions and the low-latitude electric fields for January 11, 2002. From 0000 to 0900 UT, the Philippine sector is in daylight and the low-latitude electric field is approximately 10% of the IEF Ey values, between 0100 and 0900 UT. In the Indian sector, between 0200 and 0800 UT, the
low-latitude electric field also replicates the 10% value of the IEF Ey. In the Peruvian sector daylight period, the JULIA observed electric field also matches the IEF Ey values between 1600 and 2000 UT with a factor of 10 reduction even though the IEF Ey values fluctuate between 72 mV/m which are normally quiet-time values. In Fig. 6, between 0200 and 0600 UT in the Indian sector, the IEF Ey fluctuations between 75 mV/m are mirrored in the low-latitude Ey values that fluctuate, in phase, between 70.4 mV/m. In the Peruvian sector, between 1500 and 1900 UT, the positive increases in IEF Ey of approximately 3 mV/m are reflected in Ey increases of about 0.3 mV/m and these are also in phase with each other. Figs. 4–6, demonstrate that ‘‘weather’’ signatures in E B drift velocities can be realistically studied in several longitude sectors if there exist appropriately placed magnetometers. 5. Summary The equatorial, daytime vertical E B drift model described in this study constitutes a successful attempt of using multilayer feedforward neural networks for the development of an empirical equatorial global drift model. We chose the neural network approach since neural networks have the ability to learn the non-linear relationships that exist between input and output parameters without any previous knowledge of any analytical function describing the relationships. However, their learning ability is affected by the quantity and quality of the training data, and neural network modeling requires a substantial database of reliable data to train the network with in order to learn the input–output
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relationships. Another major advantage in employing a neural network is the relative ease with which it can be retrained should more data become available. In this study, a MATLAB 3-layer feedforward neural network was employed in estimating the equatorial, daytime vertical E B drift velocities, and as shown in this paper, our quiet-time neural network-based drift values compared favorable with those obtained from a previous empirical drift model, the Fejer–Scherliess drift model. From our quiet-time studies, the fact that reasonable agreement with Fejer–Scherliess climatological drift model has been achieved suggest that realistic, low-latitude daytime, vertical E B plasma drifts can be obtained from ground-based magnetometers in Peruvian, Philippine, and Indian sectors, and by inference, at all longitude sectors where appropriately placed magnetometers exist. It is worth mentioning that the neural network drift model can be applied for both geomagnetically quiet and disturbed conditions since the training and testing data sets included both quiet and disturbed days. We now have the capability of investigating the ‘‘weather’’ aspects of daytime E B drifts and lowlatitude electric fields at several longitudes, simultaneously, and relate these to IEF conditions as demonstrated in the previous section. The three examples of geomagnetically disturbed conditions presented in this paper show that promptly penetrating and over-shielding electric fields produce opposite day/night effects in the E B plasma drift patterns at equatorial latitudes that substantiate current magnetosphere/ionosphere coupling theories as pointed out in Kelley et al. (1979) and Gonzales et al. (1979).
Acknowledgments We would like to thank Cliff Minter at CIRES/ University of Colorado and NOAA/SEC and Ludger Scherliess at the Center for Atmospheric and Space Sciences, Utah State University, for many useful discussions and suggestions during the course of this work. Funding to carry out this study came from an NSF Space Weather Grant, ATM 0516606. The Jicamarca Radio Observatory, including its magnetometers is a facility of the Instituto Geofisico del Peru and is operated with support from the NSF Cooperative Agreement ATM-0432565 through Cornell University.
References Anderson, D.N., Mendillo, M., Herniter, B., 1987. A semiempirical low-latitude ionospheric model. Radio Science 22, 292–306. Anderson, D.N., Anghel, A., Yumoto, K., Ishitsuka, M., Kudeki, E., 2002. Estimating daytime, vertical E B drift velocities in the equatorial F-region using ground-based magnetometer observations. Geophysical Research Letters 29 (12), 1596. Anderson, D., Anghel, A., Chau, J., Veliz, O., 2004. Daytime vertical E B drift velocities inferred from ground-based magnetometer observations at low latitudes. Space Weather 2, S11001. Anderson, D., Anghel, A., Chau, J.L., Yumoto, K., 2006. Global, low latitude, vertical E B drift velocities inferred from daytime magnetometer observations. Space Weather 4, S08003. Bailey, G.J., Sellek, R., Rippeth, Y., 1993. A modeling study of the equatorial topside ionosphere. Annales Geophysics 11, 263–272. Batista, I.S., deMedeiros, R.T., Abdu, M.A., deSouza, J.R., Bailey, G.J., dePaula, E.R., 1996. Equatorial ionospheric plasma drift model over the Brazilian region. Journal of Geophysical Research 101, 10,887–10,892. Coley, W.R., McClure, J.P., Hanson, W.B., 1990. Equatorial fountain effect and dynamo drift signatures from AE-E observations. Journal of Geophysical Research 95, 21,285–21,290. Demuth, H., Beale, M., 2001. Neural Network Toolbox User’s Guide. The MathWorks Inc. Fejer, B.G., Scherleiss, L., 1995. Time dependent response of equatorial ionospheric electric fields to magnetospheric disturbances. Geophysical Research Letters 22, 851–854. Fejer, B.G., Spiro, W.W., Wolf, R.A., Foster, J.C., 1990. Latitudinal variation of perturbation electric fields during magnetically disturbed periods: 1986 SUNDIAL observations and model results. Annales Geophysics 8, 441–454. Gonzales, C.A., Kelley, M.C., Fejer, B.G., Vickrey, J.F., Woodman, R.F., 1979. Equatorial electric fields during magnetically disturbed conditions. 2: implications of simultaneous auroral and equatorial measurements. Journal of Geophysical Research 84, 5803. Gonzales, C.A., Kelley, M.C., Behnke, R.A., Vickrey, J.F., Wand, R., Holt, J., 1983. On the latitudinal variations of the ionospheric electric field during magnetospheric disturbances. Journal of Geophysical Research 88, 9135–9144. Haykin, S., 1994. Neural Networks: a Comprehensive Foundation. Macmillan College Publishing Company, New York. Huang, C.S., Foster, J.C., Kelley, M.C., 2005. Long duration penetration of the planetary electric field to the low- latitude ionosphere during the main phase of magnetic storms. Journal of Geophysical Research 110, A11309. Kane, R.P., 1973. A critical appraisal of the method of estimating equatorial electrojet strength. Proceedings of the Indian Academy of Sciences 78 (A), 149–158. Kelley, M.C., Fejer, B.J., Gonzales, C.A., 1979. An explanation for anomalous equatorial ionospheric electric fields associated with a northward turning of the interplanetary magnetic field. Geophysical Research Letters 6, 301–304.
ARTICLE IN PRESS A. Anghel et al. / Journal of Atmospheric and Solar-Terrestrial Physics 69 (2007) 1147–1159 Kelley, M.C., Makela, J.J., Chau, J.L., Nicolls, M.J., 2003. Penetration of the solar wind electric field into the magnetosphere/ionosphere system. Geophysical Research Letters 30 (4), 1158. Kikuchi, T., Araki, T., 1979. Horizontal transmission of the polar electric field to the equator. Journal of Atmosphere and Terrestrial Physics 41, 927–936. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, Inc. Preble, A.J., Anderson, D.N., Fejer, B.G., Doherty, P.H., 1994. Comparison between calculated and observed F region electron density profiles at Jicamarca, Peru. Radio Science 29, 857–866. Rastogi, R.G., Klobuchar, J.A., 1990. Ionospheric electron content within the equatorial f2 layer anomaly belt. Journal of Geophysical Research 95, 19,045–19,052. Richmond, A.D., Blanc, M., Emery, B.A., Wand, R.H., Fejer, B.G., Woodman, R.F., Ganguly, S., Amayenc, P., Behnke, R.A., Calderon, C., Evans, J.V., 1980. An empirical model of quiet-day ionospheric electric fields at middle and low latitudes. Journal of Geophysical Research 85, 4658–4664.
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Sakharov, Y.Ye., Nikitin, M.A., Smirnov, O.A., 1989. Response of low-latitude electric fields to the action of a magnetospheric source. Geomagnetism and Aeronomy 29, 344–349. Sazykin, S., 2000. Theoretical studies of penetration of magnetospheric electric fields to the ionosphere. Ph.D. Thesis, Utah State University. Scherleiss, L., Fejer, B.G., 1997. Storm time dependence of equatorial disturbance dynamo zonal electric fields. Journal of Geophysical Research 102, 24,037–24,046. Scherliess, L., Fejer, B.G., 1999. Radar and satellite global equatorial F region vertical drift model. Journal of Geophysical Research 104, 6829–6842. Wolf, R.A., 1975. Ionosphere–magnetosphere coupling. Space Science Review 17, 4677–4698. Wolf, R.A., 1983. The quasi-static (slow-flow) region of the magnetosphere. In: Carovillano, R.L., Forbes, J.M. (Eds.), Solar-Terrestrial Physics, pp. 303–368. Yumoto, K., 2001. Characteristics of Pi 2 magnetic pulsations observed at the CPMN stations: a review of the STEP results. Earth Planets Space 53, 981–992.
Journal of Atmospheric and Solar-Terrestrial Physics 69 (2007) 1147–1159 www.elsevier.com/locate/jastp
Interplanetary electric fields and their relationship to low-latitude electric fields under disturbed conditions Adela Anghela,, David Andersona, Naomi Maruyamaa, Jorge Chaub, Kiyo Yumotoc, Archana Bhattacharyyad, S. Alexd a
Cooperative Institute for Research in Environmental Sciences, National Centers for Environmental Prediction National Weather Service, National Oceanic and Atmospheric Administration (NOAA), University of Colorado and Space Environment Center, 325 Broadway, Boulder, CO 80305-3337, USA b Radio Observatorio de Jicamarca, Instituto Geofisico del Peru, Peru c Space Environment Research Center, Kyushu University, Japan d Indian Institute of Geomagnetism, India Received 12 April 2006; received in revised form 7 July 2006; accepted 31 August 2006 Available online 31 March 2007
Abstract Recent studies have demonstrated that ground-based magnetometer observations can be used to infer realistic, daytime vertical E B drift velocities in the Peruvian and Philippine longitude sectors. It has also been demonstrated that under certain conditions the time variability in the interplanetary electric field (IEF)—minutes to hours—is reflected in the daytime, prompt penetration of high-latitude electric fields to low latitudes. In this paper, we incorporate magnetometerinferred E B drift techniques to extend this study to include the Indian sector E B drift velocities and to investigate the relationships between IEF conditions and daytime, low-latitude electric field observations under both geomagnetically quiet and disturbed conditions. This paper addresses several basic questions related to the relationships between IEF conditions and low-latitude east–west electric fields. (1) When low-latitude electric fields exhibit quiet-time, Sq-type behavior, what are the IEF conditions? (2) Under disturbed conditions, what are the relationships between the IEF parameters and the low-latitude electric fields in the Peruvian, Philippine, and Indian longitude sectors? (3) If the three longitude sector electric field responses are similar under disturbed conditions, is the response consistent with the current ideas put forward at the Millstone Hill Workshop on promptly penetrating electric fields and over-shielding effects at low latitudes? We address the above questions by analyzing magnetometer-inferred E B drift velocities between January 2001 and December 2004 when there exists more than 500 quiet days and more than 235 geomagnetically disturbed days, defined by daily Ap values greater than 20. It is suggested that the neural network approach that provides realistic E B drift velocities based on magnetometer observations can be applied at any longitude where appropriately placed magnetometers exist. It is found that: (1) the average quiet, daytime upward E B drift velocity vs. LT in the Indian sector is comparable to the average velocity vs. LT in the Peruvian sector and both are roughly 3–5 m/s less than the values in the Philippine sector; (2) under quiet conditions, the peak velocity occurs at 1100 LT in the Peruvian sector and at 1000 LT in both the Philippine and Indian sectors; and (3) during disturbed conditions, it is observed that daytime, promptly
Corresponding author. Tel.: +1 303 497 7235; fax: +1 303 497 3645.
E-mail address: [email protected] (A. Anghel). 1364-6826/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jastp.2006.08.018
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penetrating electric fields occur, simultaneously, in the Philippine, Indian and Peruvian sectors, in response to fluctuating IEF conditions. r 2007 Elsevier Ltd. All rights reserved. Keywords: Low-latitude electric fields; Interplanetary electric fields; Neural networks
1. Introduction The daytime, vertical E B plasma drift in the equatorial F-region of the ionosphere is the primary transport mechanism for determining the electron density profiles between 7201 dip latitude as a function of altitude, latitude, and local time. As measured by radars at Jicamarca Radio Observatory (JRO), Peru, the vertical plasma drifts show large day-to-day variability, which is responsible for large day-to-day fluctuations in the low-latitude plasma density distribution. However, the equatorial drift observations are sporadic and irregular, but knowing the vertical drifts globally, more realistically, and on a day-to-day basis is very important since several theoretical models require global and more realistic vertical drifts in order to improve the accuracy in ionospheric parameter specification (e.g., Anderson et al., 1987; Bailey et al., 1993; Preble et al., 1994). Also, knowing the vertical drifts globally and more realistically, the effects of promptly penetrating high-latitude electric fields to low latitudes, during geomagnetic storms and substorms, can be studied theoretically at a number of different longitudes. Therefore, it is not surprising that in the last decades a major effort has been devoted to obtain global empirical models of the vertical plasma drifts using radar, magnetometer, satellite, and ionosonde observations (e.g., Richmond et al., 1980; Fejer and Scherleiss, 1995; Batista et al., 1996; Scherliess and Fejer, 1999; Anderson et al., 2002, 2004). Previous studies showed that at equatorial latitudes, the daytime vertical E B plasma drifts can be estimated from the ground magnetic signature of the equatorial electrojet (EEJ). However, the dayside magnetic field measurements are difficult to interpret since during daytime the magnetometers located on the magnetic equator record not only the EEJ induced magnetic fields but also magnetic fields induced by other remote currents such as the ring current. Gonzales et al. (1979) separated the magnetic contribution of the EEJ by subtracting the magnetic field measured at a
station off the magnetic equator from that measured at a station situated on the magnetic equator. Rastogi and Klobuchar (1990) estimated the daytime vertical E B drifts by comparing the difference in the magnitudes of the horizontal H component between two magnetometers located on and off the dip equator, DH, with total electron content (TEC) observations. However, the previous studies presented only qualitative relationships between the equatorial, daytime vertical E B plasma drifts, and the magnetic field data. Only recently, Anderson et al. (2002) showed that there exist quantitative relationships and they developed linear relationships based on Jicamarca incoherent scatter radar (ISR) drift observations and magnetometer data from Canete and Piura, Peru, for a solar maximum period and for a limited number of days. Using a significantly larger database of vertical plasma drifts from the Jicamarca unattended longterm ionosphere atmosphere (JULIA) radar and magnetometer observations from Piura and Jicamarca, Anderson et al. (2004) determined quantitative relationships over a longer period of time from August 2001 to December 2003. Three different approaches were considered for data analysis: a linear regression analysis, a polynomial regression approach, and a neural network approach. The input space consisted of eight variables: year, day of the year, F10.7 cm solar radio flux, 90-day average F10.7 cm solar radio flux, daily Ap, 3-hourly magnetic kp index, local time, and DH. The relationships were successfully validated using an independent set of Jicamarca ISR vertical drift velocities. They found that the neural network approach is more suitable for capturing the nonlinearity of the DH vs. E B drift relationship. Additionally, they conducted a sensitivity analysis and reported that DH is the most significant input parameter and each other input contributed with less than 10% in reducing the root-mean-square (RMS) error. In a recent paper, Anderson et al. (2006) showed that the same neural network drift model based on
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Peruvian observations can be successfully applied at the Philippine longitude sector. The neural network drift model was trained with Peruvian magnetometer and vertical drift observations between August 2001 and February 2005. For the Philippine sector, they used magnetometer observations from Davao and Muntinlupa between 2001 and 2004. Their study concentrated on geomagnetically quiet days, with daily Ap values less than 10. Basically, the data from the Peruvian and Philippine longitude sectors were binned into three seasons and DH-inferred E B drifts were determined for each day. Then, the average vertical drift patterns were calculated and compared with the Fejer–Scherliess climatological (Scherliess and Fejer, 1999) quiet-day curves for each season and for each longitude sector. They found excellent agreement in the Peruvian sector and very good agreement in the Philippine sector (see their Figs. 4–9). The implications of their results are significant, suggesting that realistic vertical plasma drifts can now be obtained globally and on a day-today basis, wherever appropriately placed magnetometers exist. Expanding on their studies, in this paper we investigate the possibility and the implications of applying the Peruvian neural network drift model at other longitude sectors by conducting a quiettime and a disturbed-time analysis. To train the network, we used the same training set as in Anderson et al. (2006) and included the solar zenith angle (SZA) as an additional input to the network. The neural network drift model was then employed to provide realistic, daytime vertical E B drift velocities at the Indian longitude sector based on magnetometer observations from Tirunelveli and Alibag, spanning the period from January 2001 to December 2002 and between 0700 and 1700 LT. Our study concentrated on geomagnetically quiet days, with daily Ap values less than 10, and geomagnetically disturbed days, with daily Ap values more than 20. For the quiet-time analysis, as described in Anderson et al. (2006), the Indian data were binned into three seasons, and the seasonal DH-inferred E B drift patterns were calculated and compared with Fejer–Scherliess drift patterns. As part of the quiet-time analysis, we also investigated the longitudinal variability of the quiet-time E B drift patterns at Peruvian, Philippine, and Indian longitude sectors. At equatorial latitudes, daytime ionospheric electric fields drive both the EEJ in the E-region
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of the ionosphere and the F-region plasma motions. The relative efficiency with which the EEJ and the vertical plasma drifts are driven varies with the time of the day, from day-to-day, with season, solar cycle, and longitude, and during periods of enhanced geomagnetic activity, the efficiency is strongly affected by magnetospheric and disturbance dynamo effects (Scherliess and Fejer, 1999). During enhanced geomagnetic activity periods, large ionospheric electric field and current perturbations travel from high to equatorial latitudes changing the ionization distribution over large areas and controlling the generation of ionospheric plasma irregularities (Fejer and Scherliess, 1995). The most important sources of low-latitude ionospheric electrodynamic disturbances are the prompt penetration and the disturbance dynamo electric fields. In this paper, we use equatorial vertical E B drift observations and interplanetary electric field (IEF) measurements to study the storm-driven penetration effects at different longitudes in order to improve our understanding of the electrodynamic response of the low-latitude ionosphere to enhanced geomagnetic activity. Therefore, for the disturbed-time analysis, we compared the variations observed in the IEFs with those in the low-latitude zonal electric fields at the Peruvian, Philippine, and Indian longitude sectors. The IEFs were derived from the advanced composition explorer (ACE) satellite measurements, and the zonal electric fields were obtained from the Jicamarca ISR or JULIA vertical drift observations or were calculated from the DH-inferred E B drifts, knowing that 1 mV/m corresponds to a vertical drift of 40 m/s in the Peruvian sector, 28 m/s in the Philippine sector, and 20 m/s in the Indian sector. The specific questions that will be address in this paper are: (1) How well do the quiet-time DH-inferred E B drift velocities compare with the Fejer–Scherliess climatological model in the Indian sector and (2) What are the relationships between the IEFs and low-latitude zonal electric fields during geomagnetically disturbed periods? In the subsequent sections we will: (1) outline our data analysis approach, (2) present quiet-time comparisons with the Fejer–Scherliess model, (3) relate IEF conditions with low-latitude zonal electric fields under geomagnetically disturbed conditions, and (4) summarize the results and present the significance and implications of our findings from a space weather perspective.
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2. Processing approach and data description 2.1. Neural network approach In this paper, a multilayer feedforward neural network (e.g., Masters, 1993; Haykin, 1994; Demuth and Beale, 2001) was employed in estimating the equatorial, daytime vertical E B drift velocities from magnetometer DH observations at the Peruvian, Philippine and Indian longitude sectors. The neural network-based drift model is similar with the one described in Anderson et al. (2006). Since only Peruvian data were used for training the network, to account for the seasonal variations attributed to the displacement of the geographic and geomagnetic equators at different longitudes, the SZA was included as an additional input to the network. The SZA is the Sun’s angular distance from the vertical that relates to the geographic latitude of the site, the declination angle, and the hour angle, and depends on season and local time. However, there was less than 10% improvement in the RMS error for the entire Peruvian training set, and no significant changes in the estimated drifts were obtained at the Philippine and Indian sectors. This might be explained by the fact that the SZA is already an implicit input parameter, being an intrinsic part of DH. For this study, the magnetometer data sets were obtained from magnetometers located at three different longitude sectors: Peru, Philippine, and India, the vertical drifts from the Jicamarca ISR and JULIA radars, and the interplanetary measurements from the ACE satellite. Below is a brief overview of the instruments and data processing techniques. 2.2. Magnetometers Magnetometers operating at Jicamarca (geog. 11.91S, 283.11E, 0.81N dip latitude) and Piura (geog. 5.21S, 279.41E, 6.81N dip latitude) provided magnetometer observations in the Peruvian longitude sector. In the Philippine longitude sector, Prof. K. Yumoto supplied magnetometer observations from Davao (geog. 71N, 125.41E, 1.321S dip latitude) and Muntinlupa (geog. 14.371N, 121.021E, 6.391N dip latitude) (Yumoto, 2001). In the Indian sector, Dr. Bhatacharyya provided magnetometer data from Thirunelveli (geog. 8.71N, 76.91E, 0.51S dip latitude) and Alibag (geog. 18.61N, 72.91E, 101N dip latitude). The procedure
to calculate DH is detailed in Anderson et al. (2002) and can be applied at any longitude sector. Basically, at any longitude sector where two appropriately placed magnetometers exist, one on the magnetic equator and the other displaced 6–91 away, for each magnetometer, the H component nighttime baseline, calculated as a 10-h average between 1900 and 0500 LT, is first obtained for each day and then subtracted from the daytime values. Finally, DH is calculated as the difference in H between the resulting daytime values at the two magnetometers. Fig. 1 shows the Jicamarca H component observations—after subtracting the nighttime 10-h average baseline value—(dark blue dots), and DH values (light blue dots), between Jicamarca and Piura, as function of the vertical E B drifts obtained from the Jicamarca ISR and JULIA radars. The plots were produced based on data from all the 463 quiet and disturbed days used for training the neural network drift model. From these plots it is clear that there is an improved correlation for DH vs. E B drifts as compared with Jicamarca H vs. E B drifts. Also, the plots show a much larger scatter for Jicamarca H vs. E B drifts than for DH vs. E B drifts. This reinforces the findings of Rastogi and Klobuchar (1990) and Kane (1973) that it is more appropriate to qualitatively and quantitatively estimate the vertical E B drifts using DH measurements rather than using only H component observations from a single equatorial magnetometer. However, since the correlation coefficients, R, for the two cases have very close values, it seems that it might be possible to quantitatively estimate the vertical drifts based only on the equatorial H component observations. This could be useful for estimating the vertical drifts at longitudes where a single equatorial magnetometer is available. We plan on using equatorial H measurements alone as inputs to a neural network drift model and evaluate the performances of the model for this case. 2.3. ACE data To describe the interplanetary conditions, we used 64-s averages of merged ACE MAG-SWEPAM Level 2 interplanetary magnetic field (IMF) and solar wind velocity data (http://www.srl.caltech.edu/ACE/ ASC/level2/lvl2DATA_MAG-SWEPAM.html). The magnetic field experiment MAG on ACE provides continuous measurements of the local magnetic field
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Fig. 1. Jicamarca H component (dark blue dots) and DH (Jicamarca–Piura) (light blue dots) vs. vertical E B drifts from Jicamarca ISR and JULIA radars, for all the 463 quiet and disturbed days used for training the neural network drift model. The linear slopes and the correlation coefficients, R, are also indicated for the two cases.
in the interplanetary medium, and the solar wind experiment SWEPAM provides solar wind velocities. The two measurements are important in establishing the dawn-to-dusk component of the IEF, IEF Ey ¼ Vx Bz, that can promptly penetrate during storm times to equatorial latitudes until a shielding dusk-to-dawn electric field has time to develop in the inner magnetosphere. To analyze the storm-driven penetration effects at equatorial latitudes, we timeshifted the IEF Ey measured at the spacecraft and then compared the time shifted IEF Ey with the lowlatitude zonal electric fields at different longitudes (Kelley et al., 2003). 3. Quiet-time studies Several studies investigated the longitudinal variability of the equatorial F-region plasma drifts using satellite (e.g., Coley et al., 1990) and combined radar and satellite observations (e.g., Scherliess and Fejer, 1999). In this section, in addition to comparing the average DH-inferred drift patterns at the Indian sector with the Fejer–Scherliess quiet-day curves for different seasons, we also investigate the longitudinal variability of the quiet-time drift patterns at Peruvian, Philippine, and Indian longitude sectors. The Peruvian and Philippine day-today variability of the daytime E B drifts and the quiet-time drift patterns are discussed at length in Anderson et al. (2006), and here are mentioned some of their results for completeness but place
more emphasis on the vertical drifts at the Indian sector. At all three longitudes, the daytime DH-inferred drifts were estimated using the neural network drift model described in the previous section of the paper, making the assumption that the same neural network trained with Peruvian data can be applied at any other longitude sector. For the quiet-time analysis, we chose only those days when the daily Ap values were less than 10. Adopting the same constraints to our data that were also used by Scherliess and Fejer (1999) in developing their model, the Indian quiet-day observations were binned into three seasonal periods, the vertical DH-inferred E B drifts were determined for each day, and the average DH-inferred E B drift patterns were calculated and compared with Fejer–Scherliess drift patterns for each season. Three seasons were considered: (1) June solstice (May–August), (2) December solstice (November–February), and (3) Equinox (March– April, September–October). At the Indian sector, the daytime vertical E B drift velocities were calculated for 250 quiet days based on magnetometer observations from Tirunelveli and Alibag, between January 2001 and December 2002. Fig. 2 displays the daytime drift velocities as a function of local time at the Indian longitude sector for these quiet days with available magnetometer data. The solid red lines represent the average DH-inferred drift values and the solid blue lines are the average Fejer–Scherliess drifts. The best agreement between
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Fig. 2. Daytime vertical E B drift velocities vs. local time at the Indian longitude sector for all the 250 quiet days with available magnetometer data during the Equinox period, and June and December solstices. The solid red lines are the average DH-inferred drift patterns and the solid blue lines the average Fejer–Scherliess quiet-day curves.
the two average curves was obtained for the Equinox period. For the Peruvian and Philippine sectors, we used the same data sets as described in Anderson et al. (2006). At the Peruvian longitude
sector there were 488 quiet days of Jicamarca and Piura magnetometer observations between January 2001 and December 2004. From the Philippine sector, we had 366 quiet days of magnetometer
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Fig. 3. Quiet-time E B drift patterns vs. local time for the Equinox period, and June and December solstices in the Peruvian, Philippine, and Indian longitude sectors. The red lines are the average DH-inferred drift patterns and the blue lines the average Fejer–Scherliess quietday curves. The number of quiet-days worth of data for each season and longitude sector is indicated on each panel.
observations from Davao and Muntinlupa extending from January 2001 through May 2004. The numbers of quiet-days worth of data for each season and longitude sector are indicated on the panels in Fig. 3. Fig. 3 displays the average, daytime DH-inferred and Fejer–Scherliess vertical E B drift patterns as a function of local time for all quiet days with available magnetometer data during June solstice period (Fig. 3a), Equinox (Fig. 3b), and December solstice (Fig. 3c) in the Peruvian, Philippine, and Indian longitude sectors, respectively. In all panels, the solid red lines are the average patterns of the DH-inferred E B drift velocities while the solid blue lines are the Fejer–Scherliess average drift patterns. The error bars represent the standard deviations associated with these seasonal and longitudinal quiet-time drift patterns. As seen in Fig. 3, in all studied cases, the standard deviations corresponding to the DH-inferred E B drift patterns were close to 5 m/s around the noon hours and decreased gradually to about 2.5 m/s early in the morning and late afternoon hours.
Fig. 3a depicts the quiet-time drift patterns for the June solstice period at all three longitude sectors together with the corresponding standard deviations. At the Peruvian longitude sector, as shown in the left panel of Fig. 3a, the average DH-inferred drift curve for this period has its maximum value of 18 m/s around 1100 LT and the average Fejer–Scherliess drift curve has its maximum of 20 m/s at about the same time. Overall, the two curves are in excellent agreement. At the Philippine longitude sector, as illustrated in the middle panel in Fig. 3a, there are slight differences in amplitude and shape between the two average curves. At this longitude, the maximum average drift values are reached at about 1000 LT, for the Fejer–Scherliess drift pattern the maximum being 25 m/s and for the DH-inferred drift pattern 20 m/s. For the Indian longitude sector, the June solstice period is represented in the right panel in Fig. 3a. As seen in this figure, the Fejer–Scherliess model predicts a maximum of 24 m/s around 1000 LT and the average DH-inferred drift curve has a maximum of 17 m/s about 30 min earlier. Our data shows a large
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discrepancy in amplitude and shape between the two average drift curves. In summary, for the June solstice period, the average DH-inferred drift patterns at all three longitude sectors present some similarities in amplitude and shape: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with Indian drift values smaller than in Philippines; (2) at the Indian and Peruvian sectors, the patterns are very close in amplitude. Conversely, the average drift patterns as estimated by the Fejer–Scherliess model are similar in amplitude and shape at the Indian and Philippine longitude sectors and very different from the Peruvian pattern. The quiet-time drift patterns for the Equinox period at all three longitude sectors together with the corresponding standard deviations are shown in Fig. 3b. As seen in the left panel of Fig. 3b, there is an excellent agreement between the Peruvian average drift patterns as estimated by the neural network drift model from magnetometer data and Fejer–Scherliess model, with the maximum drift values of 22 m/s obtained at 1100 LT. For the Philippine and Indian sectors, there are differences between the average DH-inferred drift patterns and the average climatological patterns as seen in the middle and right panels of Fig. 3b, respectively, with the climatological patterns slightly higher throughout the day. Respective maximum values for the neural network model and Fejer–Scherliess climatology model are 24 and 25 m/s in the Philippine sector and 22 and 25 m/s in the Indian sector, with all four maximum values occurring at 1000 LT. As for the June solstice period, for Equinox, the average DH-inferred drift curves at all three longitude sectors present some similarities: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with the Indian drift values slightly smaller than in the Philippines; (2) at Indian and Peruvian sectors, the patterns are very close in amplitude. The average Fejer–Scherliess drift patterns are similar in shape and amplitude at the Indian and Philippine sectors with slightly higher values than the Peruvian pattern. At all three longitude sectors, the data show the largest differences between the average DH-inferred drift patterns and the average Fejer–Scherliess drift curves during the December solstice, as shown in Fig. 3c. The left panel of the Fig. 3c displays the Peruvian patterns indicating a maximum drift value of 15 m/s at about 1100 LT as calculated with the neural network drift model and 17 m/s more than 30 min earlier as predicted by the Fejer–Scherliess
model. As shown in the middle panel, the Philippine patterns, although very different in shape throughout the day with the DH-inferred average drift curve lying significantly below the climatological curve except near midday, reach the same maximum value of 20 m/s more than 30 min before 1100 LT. In the Indian sector, the average DH-inferred drift pattern has its maximum of 15 m/s around 1000 LT, while the climatological pattern reaches a maximum value of 18 m/s about 30 min later, as seen in the left panel of Fig. 3c. As observed for the other two seasons, the average DH-inferred drift curves at all three longitude sectors present some similarities in amplitude and shape: (1) at the Indian and Philippine sectors, the patterns are similar in shape, with Indian drift values smaller than in Philippines; (2) at the Indian and Peruvian sectors, the patterns are very close in amplitude. The average Fejer–Scherliess drift patterns are different in shape at the Indian and Philippine sectors and present slightly higher values than in the Peruvian sector. In conclusion, for each season, the average DH-inferred drift curves at all three longitude sectors present some similarities: (1) at the Indian and Philippine sectors, the patterns are similar in shape with the maximum values around 1000 LT and with the Indian drift values smaller than in the Philippines; (2) at the Indian and Peruvian sectors, the patterns are close in amplitude, reaching maximum values in the Peruvian sector around 1100 LT. As observed in our study, for each season, the average Fejer–Scherliess drift patterns are relatively similar in shape and amplitude at the Indian and Philippine sectors, where the maximum values are reached around 1000 LT, and have slightly higher values than in the Peruvian sector, where the maximum values are around 1100 LT. Our comparisons between the average vertical DH-inferred drift patterns with the Fejer–Scherliess quiet-day curves show excellent agreement in the Peruvian sector especially for the June solstice and Equinox, good agreement in the Philippines especially at Equinox, and reasonable agreement in the Indian sector especially at Equinox. The best agreement between the climatology and neural network average patterns was obtain for the Equinox period at all three longitude sectors. It is worth noting that the shape and variability of the daytime, vertical DH-inferred drift curves truthfully reflect the daytime shape and variability of the DH values, since DH is by far the most important input parameter for the neural network drift model.
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In this section, we have clearly demonstrated that the DH vs. E B drift relationships that were developed in the Peruvian longitude sector can be applied in the Philippine and Indian sectors to provide realistic E B drift velocities. This is an important and significant conclusion since it means that, now, the day-to-day, ‘‘weather’’ aspects of daytime E B drift velocities can be investigated at different longitudes and related to IEF conditions. This is demonstrated in the next section for ‘‘disturbed’’ conditions where the daily Ap values are greater than 20. 4. Disturbed-time studies Compared with quiet-time E B drift patterns, low-latitude plasma drifts and currents respond quite differently during geomagnetically disturbed periods. Under steady-state conditions, the low- and mid-latitude ionosphere is shielded from the highlatitude convection by ions at the low-latitude edge of the plasma sheet (Wolf, 1975; Sakharov et al., 1989). During periods when high-latitude convection patterns are undergoing large changes, highlatitude electric fields ‘‘leak through’’ the shielding layer. Evidence for this penetration of electric fields to the equatorial regions is presented by Gonzales et al. (1983) and Fejer et al. (1990). Kikuchi and Araki (1979) suggested that the electrostatic fields travel to the equatorial regions instantaneously, by the zeroth-order TM mode in the earth–ionosphere waveguide. There are two major sources of low-latitude electric field disturbances during geomagnetically active periods. One originates from the magnetosphere and the other is due to the ionospheric disturbance dynamo. The latter results from the dynamo action of the storm time winds as a result of enhanced energy deposition at high latitudes. Scherleiss and Fejer (1997) developed an empirical model that describes the storm time dependence of the equatorial disturbance dynamo electric fields. When the cross polar cap potential suddenly increases as a result of an increase in the dawn-todusk, cross-tail electric field, this electric field can promptly penetrate to the equatorial region (undershielding) until a shielding electric field (dusk-todawn) has had time to develop in the inner magnetosphere (Wolf, 1983; Sazykin, 2000). This electric field shields the inner magnetosphere from strong convection fields. When the strong convection field decreases due to a northward turning of
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the IMF, the dusk-to-dawn electric field can promptly penetrate to low-latitudes (over-shielding) until the shielding layer and overall magnetospheric configuration readjust. In the figures that follow, we compare the IEF conditions observed by the ACE satellite, time shifted to the magnetopause location, with the lowlatitude zonal electric fields inferred from magnetometer observations in the Peruvian, Philippine, and Indian longitude sectors. For each figure, the various quantities are plotted as a function of universal time (UT) rather than local time (LT). In the Peruvian sector, 0000 UT is 1900 LT; in the Philippine sector, 0800 LT; and in the Indian sector, 0500 LT. In each figure, the orange line represents the daytime (0700–1700 LT), DH-inferred, vertical E B drift velocity and the corresponding eastward electric field in the Peruvian sector (1 mV/m 40 m/s). The red line corresponds to the daytime values in the Philippine sector (1 mV/m 28 m/s) while the light blue line represents daytime values in the Indian sector (1 mV/m 20 m/s). The green line represents the Jicamarca ISR drift and electric field observations over the 24 h day. In Figs. 4–6, the notation in the boxes to the right of the panels are defined here: ISR—Jicamarca incoherent scatter radar; Est. (DM)—Philippine sector magnetometers (Davao– Muntinlupa); Est. (TA)—Indian sector magnetometers (Thirunelvelli–Alibag); Est. (JP)—Peruvian sector magnetometers (Jicamarca–Piura). Fig. 4 depicts the IEF Ey conditions and the DH-inferred drifts for April 17–19, 2001. The thin lines in the second panel in Fig. 4 represent the Fejer–Scherliess climatological drift values for this period and at each of the three longitude sectors. For all of April 17, the IEF Ey conditions are ‘‘quiet’’ with Apo20 and the Peruvian, Philippine, and Indian sectors reflect quiet-time drifts of approximately 20 m/s in each sector. As shown in Fig. 4 at 0100 UT on 18 April, Bz suddenly turns southward leading to a strong, positive value for IEF Ey of 10 mV/m. This occurs when the Philippine sector is in daylight and the DH-inferred E B drift velocity begins to respond to IEF Ey conditions. Referring to the bottom panel of Fig. 4, between 0200 and 0400 UT on the 18th, that are marked by the black arrows, the low-latitude zonal electric field in the Philippine sector matches the IEF Ey/10 curve almost exactly for the entire period. This is a period of extended IMF Bz south condition, which Huang et al. (2005) would categorize as an extended penetration electric field.
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Fig. 4. IEF Ey and Ez values for April 17–19, 2001 (top); equatorial E B drift velocities vs. UT at the Peruvian, Philippine, and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological drift values (middle); IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT, between 0000 and 1200 UT, on April 18, 2001 (bottom).
Fig. 5. IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT for January 11, 2002, at the Peruvian, Philippine, and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological values.
During this period of interest, when the IMF Bz component turned southward and remained stably southward for more than 2 h, the low-latitude zonal electric field at Philippines maintained an enhanced
and stable amplitude. For 18 April, the daily Ap is 50 so this represents a fairly disturbed day. At 0430 UT on 18 April, there is a sudden northward turning of IMF Bz and a sudden
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Fig. 6. IEF Ey/10 and the ionospheric zonal electric fields Ey vs. UT for July 6, 2002, at the Peruvian and Indian longitude sectors with the thin lines representing the Fejer–Scherliess climatological values.
decrease in the upward E B drift velocity in both Philippine and Indian sectors (second black arrow). Simultaneously, in the nighttime Peruvian sector there is a sudden increase in the vertical E B drift. This is all consistent with the idea that, for overshielding, the electric fields at low latitudes are directed from dusk-to-dawn which means that the daytime E B drifts will be downward and the nighttime E B drift velocities will be upward. The third black arrow at 29 UT, points to the sudden turning of the IEF Ey conditions from negative to positive values and the prompt penetration electric fields in the Peruvian, Philippine, and Indian sectors. For this case, the penetrating electric field is ‘‘dawn-to-dusk’’, which means that in the Peruvian nighttime sector, the electric field is westward (downward drift) and in the daytime Philippine and Indian sectors the electric field is eastward (upward E B drift). The fact that the daytime drifts are so small in the Philippine and Indian sector implies that the disturbance dynamo electric field is fairly strong and acts to cancel the penetration electric field in these two longitude sectors. While January 11 and July 6 2002, have been listed as disturbed days, they are only slightly disturbed, since the respective Ap values are 21 and 23. Even so, as discussed below, the factor of 10 between the IEF Ey values and the low latitude, daytime electric fields seems to be maintained. Fig. 5 shows the IEF Ey conditions and the low-latitude electric fields for January 11, 2002. From 0000 to 0900 UT, the Philippine sector is in daylight and the low-latitude electric field is approximately 10% of the IEF Ey values, between 0100 and 0900 UT. In the Indian sector, between 0200 and 0800 UT, the
low-latitude electric field also replicates the 10% value of the IEF Ey. In the Peruvian sector daylight period, the JULIA observed electric field also matches the IEF Ey values between 1600 and 2000 UT with a factor of 10 reduction even though the IEF Ey values fluctuate between 72 mV/m which are normally quiet-time values. In Fig. 6, between 0200 and 0600 UT in the Indian sector, the IEF Ey fluctuations between 75 mV/m are mirrored in the low-latitude Ey values that fluctuate, in phase, between 70.4 mV/m. In the Peruvian sector, between 1500 and 1900 UT, the positive increases in IEF Ey of approximately 3 mV/m are reflected in Ey increases of about 0.3 mV/m and these are also in phase with each other. Figs. 4–6, demonstrate that ‘‘weather’’ signatures in E B drift velocities can be realistically studied in several longitude sectors if there exist appropriately placed magnetometers. 5. Summary The equatorial, daytime vertical E B drift model described in this study constitutes a successful attempt of using multilayer feedforward neural networks for the development of an empirical equatorial global drift model. We chose the neural network approach since neural networks have the ability to learn the non-linear relationships that exist between input and output parameters without any previous knowledge of any analytical function describing the relationships. However, their learning ability is affected by the quantity and quality of the training data, and neural network modeling requires a substantial database of reliable data to train the network with in order to learn the input–output
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relationships. Another major advantage in employing a neural network is the relative ease with which it can be retrained should more data become available. In this study, a MATLAB 3-layer feedforward neural network was employed in estimating the equatorial, daytime vertical E B drift velocities, and as shown in this paper, our quiet-time neural network-based drift values compared favorable with those obtained from a previous empirical drift model, the Fejer–Scherliess drift model. From our quiet-time studies, the fact that reasonable agreement with Fejer–Scherliess climatological drift model has been achieved suggest that realistic, low-latitude daytime, vertical E B plasma drifts can be obtained from ground-based magnetometers in Peruvian, Philippine, and Indian sectors, and by inference, at all longitude sectors where appropriately placed magnetometers exist. It is worth mentioning that the neural network drift model can be applied for both geomagnetically quiet and disturbed conditions since the training and testing data sets included both quiet and disturbed days. We now have the capability of investigating the ‘‘weather’’ aspects of daytime E B drifts and lowlatitude electric fields at several longitudes, simultaneously, and relate these to IEF conditions as demonstrated in the previous section. The three examples of geomagnetically disturbed conditions presented in this paper show that promptly penetrating and over-shielding electric fields produce opposite day/night effects in the E B plasma drift patterns at equatorial latitudes that substantiate current magnetosphere/ionosphere coupling theories as pointed out in Kelley et al. (1979) and Gonzales et al. (1979).
Acknowledgments We would like to thank Cliff Minter at CIRES/ University of Colorado and NOAA/SEC and Ludger Scherliess at the Center for Atmospheric and Space Sciences, Utah State University, for many useful discussions and suggestions during the course of this work. Funding to carry out this study came from an NSF Space Weather Grant, ATM 0516606. The Jicamarca Radio Observatory, including its magnetometers is a facility of the Instituto Geofisico del Peru and is operated with support from the NSF Cooperative Agreement ATM-0432565 through Cornell University.
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