Empirical model of equatorial electrojet based on ground-based magnetometer data during solar minimum in fall
© Hamid et al. 2015
Received: 6 July 2015
Accepted: 15 December 2015
Published: 29 December 2015
In this study, we constructed an empirical model of the equatorial electrojet (EEJ), including local time and longitudinal dependence, based on simultaneous data from 12 magnetometer stations located in six longitude sectors. An analysis was carried out using the equatorial electrojet index, EUEL, calculated from the geomagnetic northward H component. The magnetic EEJ strength is calculated as the difference between the normalized EUEL index of the magnetic dip equator station and the normalized EUEL index of the off-dip equator station located beyond the EEJ band. Analysis showed that this current is always strongest in the South American sector, regardless of local time (LT), and weakest in the Indian sector during 0900 and 1000 LT, but shifted to the African sector during 1100 to 1400 LT. These longitude variations of EEJ roughly follow variations of the inversed main field strength along the dip equator, except for the Indian and Southeast Asian sectors. The result showed that the EEJ component derived from the model exhibits a similar pattern with measured EEJ from ground data during noontime, mainly before 1300 LT.
The equatorial electrojet current has always been conceived as a phenomenon confined to a narrow area around ±3° of the dip equator. This current is an intense electric current, flowing eastward in the dayside of an equatorial E region that is about 600 km wide. The primary reason for this intense current density is the geomagnetic field geometry, which exhibits exactly horizontal lines of force at these latitudes. On the other hand, solar quiet (Sq) is a global current system consisting of two large vortices of electric currents in the dayside ionosphere, one in each hemisphere, counterclockwise in the Northern Hemisphere, and clockwise in the Southern Hemisphere. This current is driven by solar extreme ultraviolet (EUV) radiation, which not only produces the ionization in the E region but also heats the atmosphere and causes the wind. Both currents overlap at the dip equator to give the total current and significantly affect the geomagnetic data measured in the area (Forbes 1981; Stening 1995; Onwumechili 1997). Previous study by Hamid et al. (2014) has successfully separated equatorial electrojet (EEJ) and Sq at the dip equator observed from three longitude sectors: South America, India, and Southeast Asia. The normalization approach suggested by them gives an opportunity to construct an EEJ empirical model based on observational data, mainly from ground-based magnetometers, regardless of the effect of EEJ and Sq latitudinal variations.
There have been previous attempts in constructing EEJ models, both theoretically and empirically. Most theoretical approaches have assumed various current distributions and analyzed the resulting magnetic effects (Chapman 1951; Fambitakoye and Mayaud 1976). On the other hand, the empirical model of Onwumechili and Ezema (1992) is based on POGO satellite data, which provide measurement of several important parameters of EEJ, such as mean peak-current intensity. However, it does not offer a longitudinal profile of EEJ. The longitudinal variation of EEJ has been reported by several studies, such as Doumouya et al. (2003), Alken and Maus (2007), Shume et al. (2010), and Chandrasekhar et al. (2014). Among these, a comprehensive study is given by Doumouya et al. (2003) in their empirical model of EEJ magnetic signature based on ground magnetic data recorded from a single station at six longitude sectors. However, due to the absence of an off-dip equator station, they did not eliminate the Sq contribution to geomagnetic data. The same problem was faced by Doumouya and Cohen (2004). In this study, we used the same method proposed by Doumouya et al. (2003), with some modifications. First, we eliminated the Sq contribution at the dip equator by using a two-station method. Second, we considered latitudinal variation of the Sq and EEJ magnetic signature by normalizing the observation data to the dip equator. Both aspects were previously unattainable with ground-based data. The output from the present model was compared with the study using satellite data proposed by Alken and Maus (2007). Additionally, we validated the model output by comparing it with the observation data collected during the same period of the following year.
Data and method of analysis
Geomagnetic and geographic coordinates of stations used in this study
Station name (code)
Adis Ababa (AAB)
EEJ empirical model
In this model, t m is a fitting parameter that controls the time window of the Gaussian-like shape with the average value being 4 h (Doumouya et al. 2003). On the other hand, both T (the local time of maximum EEJ) and the longitudinal function of EEJ, I 11(λ), are determined from the observation data. From the data analysis, it is shown that on average, T is equal to 1100 LT (see Fig. 3).
where a and h are the half width and height of EEJ, adopted as 330 and 105 km, respectively.
Root-mean-square deviation (RMSD) between measured data and model output of EEJ magnetic component
This model provides an improved version of the empirical model proposed by Doumouya et al. (2003). The data used are from the end of a long, deep, solar minimum (2009), in which the conductivity may be lower than the normal solar minimum (as EUV was very low during the deep minimum). Consequently, the magnitude of EEJ from this model could be lower. However, it may not affect the longitudinal variation, which depends mainly on B and the wind; neither is much influenced by the deep minimum. Two novel features of this current model are the normalization of observation data to the dip equator and the elimination of Sq contribution at the dip equator, which are limited in most of previous studies. The EEJ longitudinal profile obtained is similar to the one shown by Doumouya and Cohen (2004), with some discrepancy appearing in the African sector, where our result shows a decreasing trend in this sector while their result shows a small increasing trend. This could be caused by the location of the observatory station used. Our second station in this region (AAB, 38.77° E) is located nearest to the edge of the East African sector, compared to their station (MOK, 13:48° E). Therefore, a further study is suggested to include a dense longitudinal chain of stations across the sector. Other than that, their study used data from single stations to represent EEJ at the dip equator, and this might also cause the difference observed. Thus, the result obtained in this study is more precise, as the EEJ was successfully calculated after the Sq effect and uncertainty due to latitudinal variation of observation data have been removed. A better comparison can be made with the study by Alken and Maus (2007) where a clean EEJ signal was obtained using satellite data. It is expected that both satellite and ground-based plots of EEJ longitudinal profiles should highlight the prominent features. The EEJ profile from their study shows a strong enhancement at longitudes of 90° E and 90° W, which corresponds to the enhancement at the Southeast Asian and the South American sectors in our longitudinal profile of EEJ magnetic effect. However, the other two enhancements at longitude of about 0° and 180° in their study are unattainable by our model. This is due to the fact that our model is based on observation data controlled by the distribution of a ground-based magnetometer, which explains the differences observed, particularly in the region of the Atlantic and Pacific Oceans.
An empirical model of the EEJ magnetic signature, including local time and longitudinal dependence, was constructed on the basis of simultaneous observations recorded from 12 magnetometer stations located in six different longitude sectors after the normalization of observation data to the dip equator. The analysis showed that the EEJ component is strongest in the South American sector, regardless of local time and weakest in the Indian sector during 0900 and 1000 LT, but shifted to the African sector during the period 1100 to 1400 LT. The result showed that the EEJ component derived from the model presented a pattern similar to the measured EEJ from ground magnetic data mainly before 1300 LT. In summary, the improved empirical model in this study has successfully reproduced the EEJ components on a global scale around noontime. In the present study, the EEJ local time profile is assumed to be a simple Gaussian-type profile. Future work is necessary to improve this profile as well as to account for the longitudinal shift of the minimum EEJ at different local times and to explain the cause of the longitudinal profile obtained.
The authors thank all the member of the MAGDAS project for their cooperation and contribution to this study. Financial support was provided by the Universiti Kebangsaan Malaysia and Ministry of Education, Malaysia, using grants GGPM-2015-020 and FRGS/1/2015/ST02/UKM/02/1. H. Liu is supported by JSPS KAKENHI Grant Numbers 15K05301, 15H02135, and 15H03733. T. Uozumi and A. Yoshikawa were supported in part by JSPS Core-to-Core Program (B. Asia-Africa Science Platforms), Formation of Preliminary Center for Capacity Building for Space Weather Research.
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