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  1. Abstract

    Medium‐scale Traveling Ionospheric Disturbances (MSTIDs) are prominent and ubiquitous features of the mid‐latitude ionosphere, and are observed in Super Dual Auroral Radar Network (SuperDARN) and high‐resolution Global Navigational Satellite Service (GNSS) Total Electron Content (TEC) data. The mechanisms driving these MSTIDs are an open area of research, especially during geomagnetic storms. Previous studies have demonstrated that nightside MSTIDs are associated with an electrodynamic instability mechanism like Perkins, especially during geomagnetically quiet conditions. However, dayside MSTIDs are often associated with atmospheric gravity waves. Very few studies have analyzed the mechanisms driving MSTIDs during strong geomagnetic storms at mid‐latitudes. In this study, we present mid‐latitude MSTIDs observed in de‐trended GNSS TEC data and SuperDARN radars over the North American sector, during a geomagnetic storm (peakKpreaching 9) on 7–8 September 2017. In SuperDARN, MSTIDs were observed in ionospheric backscatter with Line of Sight (LOS) velocities exceeding 800 m/s. Additionally, radar LOS velocities oscillated with amplitudes reaching ±500 m/s as the MSTIDs passed through the fields‐of‐view. In detrended TEC, these MSTIDs produced perturbations reaching ∼50 percent of background TEC magnitude. The MSTIDs were observed to propagate in the westward/south‐westward direction with a time period of ∼15 min. Projecting de‐trended GNSS TEC data along SuperDARN beams showed that enhancements in TEC were correlated with enhancements in SuperDARN SNR and positive LOS velocities. Finally, SuperDARN LOS velocities systematically switched polarities between the crests and the troughs of the MSTIDs, indicating the presence of polarization electric fields and an electrodynamic instability process during these MSTIDs.

     
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  2. Abstract

    The Super Dual Auroral Radar Network (SuperDARN) is a network of High Frequency (HF) radars that are typically used for monitoring plasma convection in the Earth's ionosphere. A majority of SuperDARN backscatter can broadly be divided into three categories: (a) ionospheric scatter due to reflections from plasma irregularities in the E and F regions of the ionosphere, (b) ground scatter caused by reflections from the ground/sea surface following reflection in the ionosphere, and (c) backscatter from meteor trails left by meteoroids as they enter the Earth's atmosphere. Due to the complex nature of HF propagation and mid‐latitude electrodynamics, it is often not straightforward to distinguish between different modes of backscatter observed by SuperDARN. In this study, we present a new two‐stage machine learning algorithm for identifying different backscatter modes in SuperDARN data. In the first stage, a neural network that “mimics” ray‐tracing is used to predict the probability of ionospheric and ground scatter occurring at a given location along with parameters like the elevation angles, reflection heights etc. The inputs to the network include parameters that control HF propagation, such as signal frequency, season, UT time, and geomagnetic activity levels. In the second stage, the output probabilities from the neural network and actual SuperDARN data are clustered together to determine the category of the backscatter. Our model can distinguish between meteor scatter, 1/2 hop E‐/F‐region ionospheric as well as ground/sea scatter. We validate our model by comparing predicted elevation angles with those measured at a SuperDARN radar.

     
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  3. Abstract

    The sub‐auroral polarization stream (SAPS) is a region of westward high velocity plasma convection equatorward of the auroral oval that plays an important role in mid‐latitude space weather dynamics. In this study, we present observations of SAPS flows extending across the North American sector observed during the recovery phase of a minor geomagnetic storm. A resurgence in substorm activity drove a new set of field‐aligned currents (FACs) into the ionosphere, initiating the SAPS. An upward FAC system is the most prominent feature spreading across most SAPS local times, except near dusk, where a downward current system is pronounced. The location of SAPS flows remained relatively constant, firmly inside the trough, independent of the variability in the location and intensity of the FACs. The SAPS flows were sustained even after the FACs weakened and retreated polewards with a decline in geomagnetic activity. The observations indicate that the mid‐latitude trough plays a crucial role in determining the location of the SAPS and that SAPS flows can be sustained even after the magnetospheric driver has weakened.

     
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  4. Abstract

    The existence of Birkeland magnetic field‐aligned current (FAC) system was proposed more than a century ago, and it has been of immense interest for investigating the nature of solar wind‐magnetosphere‐ionosphere coupling ever since. In this paper, we present the first application of deep learning architecture for modeling the Birkeland currents using data from the Active Magnetosphere and Planetary Electrodynamics Response Experiment (AMPERE). The model uses a 1‐hr time history of several different parameters such as interplanetary magnetic field (IMF), solar wind, and geomagnetic and solar indices as inputs to determine the global distribution of Birkeland currents in the Northern Hemisphere. We present a comparison between our model and bin‐averaged statistical patterns under steady IMF conditions and also when the IMF is variable. Our deep learning model shows good agreement with the bin‐averaged patterns, capturing several prominent large‐scale features such as the Regions 1 and 2 FACs, the NBZ current system, and the cusp currents along with their seasonal variations. However, when IMF and solar wind conditions are not stable, our model provides a more accurate view of the time‐dependent evolution of Birkeland currents. The reconfiguration of the FACs following an abrupt change in IMF orientation can be traced in its details. The magnitude of FACs is found to evolve with e‐folding times that vary with season and MLT. When IMF Bz turns southward after a prolonged northward orientation, NBZ currents decay exponentially with an e‐folding time of25 min, whereas Region 1 currents grow with an e‐folding time of 6–20 min depending on the MLT.

     
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  5. Abstract

    In this study we have used 7 years (2011–2017) of quiet (Kp ≤ 2+) to moderately disturbed (Kp = 3) time nightside line‐of‐sight measurements from six midlatitude Super Dual Auroral Radar Network radars in the U.S. continent to characterize the subauroral convection in terms of magnetic latitude, magnetic local time, month, season, Kp, and the interplanetary magnetic field (IMF) clock angle. Our results show that (1) the quiet time (Kp ≤ 2+) subauroral flows are predominantly westward (20–90 m/s) in all months and become meridional (−20–20 m/s) near dawn and dusk, with the flows being the strongest and most structured in December and January. (2) The Kp dependency is prominent in all seasons such that for higher Kp the premidnight westward flow intensifies and the postmidnight eastward flow starts to emerge. (3) Sorting by IMF clock angle shows Bz+/Bz− features consistent with lower/higher Kp conditions, as expected, but also shows distinct differences that are associated with By sign. (4) There is a pronounced latitudinal variation in the zonal flow speed between 18 and 2 magnetic local time in winter (November to February) that exists under all IMF conditions but is most pronounced under IMF Bz− and higher Kp. Our analysis suggests that the quiet time subauroral flows are due to the combined effects of solar wind/magnetosphere coupling leading to penetration electric field and the neutral wind dynamo with the ionospheric conductivity modulating their relative dominance.

     
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  6. Abstract

    The auroral substorm has been extensively studied over the last six decades. However, our understanding of its driving mechanisms is still limited and so is our ability to accurately forecast its onset. In this study, we present the first deep learning‐based approach to predict the onset of a magnetic substorm, defined as the signature of the auroral electrojets in ground magnetometer measurements. Specifically, we use a time history of solar wind speed (Vx), proton number density, and interplanetary magnetic field (IMF) components as inputs to forecast the occurrence probability of an onset over the next 1 hr. The model has been trained and tested on a data set derived from the SuperMAG list of magnetic substorm onsets and can correctly identify substorms ∼75% of the time. In contrast, an earlier prediction algorithm correctly identifies ∼21% of the substorms in the same data set. Our model's ability to forecast substorm onsets based on solar wind and IMF inputs prior to the actual onset time, and the trend observed in IMFBzprior to onset together suggest that a majority of the substorms may not be externally triggered by northward turnings of IMF. Furthermore, we find that IMFBzandVxhave the most significant influence on model performance. Finally, principal component analysis shows a significant degree of overlap in the solar wind and IMF parameters prior to both substorm and nonsubstorm intervals, suggesting that solar wind and IMF alone may not be sufficient to forecast all substorms, and preconditioning of the magnetotail may be an important factor.

     
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  7. Abstract

    The Sun was remarkably active during the first week of September 2017 producing numerous solar flares, solar radiation storms, and coronal mass ejections. This activity caused disruption to terrestrial high‐frequency (HF, 3–30 MHz) radio communication channels including observations with the Super Dual Auroral Radar Network (SuperDARN) HF radars. In this paper, we analyze the response of SuperDARN groundscatter observations and decreases in background sky noise level in response to multiple solar flares occurring in quick succession and co‐occurring with solar energetic protons and auroral activity. We estimate the attenuation in HF signal strength using an approach similar to riometry and find that the radars exhibit a nonlinear response to compound solar flare events. Additionally, we find that the three different space weather drivers have varying degrees of influence on the HF signal properties at different latitudes. Our study demonstrates that in addition to monitoring high‐latitude convection, SuperDARN observations can be used to study the spatiotemporal evolution of disruption to HF communication during extreme space weather conditions.

     
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  8. Abstract

    Long‐lasting Pc5 ultralow frequency (ULF) waves spanning the dayside and extending fromL ∼ 5.5into the polar cap region were observed by conjugate ground magnetometers. Observations from MMS satellites in the magnetosphere and magnetometers on the ground confirmed that the ULF waves on closed field lines were due to fundamental toroidal standing Alfvén waves. Monochromatic waves at lower latitudes tended to maximize their power away from noon in both the morning and afternoon sectors, while more broadband waves at higher latitudes tended to have a wave power maximum near noon. The wave power distribution and MMS satellite observations during the magnetopause crossing indicate surface waves on a Kelvin‐Helmholtz (KH) unstable magnetopause coupled with standing Alfvén waves. The more turbulent ion foreshock during an extended period of radial interplanetary magnetic field (IMF) likely plays an important role in providing seed perturbations for the growth of the KH waves. These results indicate that the Pc5 waves observed on closed field lines and on the open field lines of the polar cap were from the same source.

     
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