J. M. Ruohoniemi’s research while affiliated with Virginia Tech and other places

Plain Language Summary The total solar eclipse on 8 April 2024 was the last one to be observed over the continental United States until 2045. In addition to blocking the visible light from the Sun over a swath ranging a few hundred kilometers, a solar eclipse will also partially obscure the Sun's extreme ultraviolet radiation. As a result, an eclipse can drive significant changes in the ionosphere, a charged region in the Earth's upper atmosphere that is predominantly ionized by solar radiation. A unique feature of this eclipse was that the path of totality traversed the fields‐of‐view of three Super Dual Auroral Radar Network (SuperDARN) radars–Fort Hays East, Blackstone, and Wallops Island. SuperDARN radars operate in the High Frequency (HF) range, and radio waves in this frequency range are particularly sensitive to changes in the ionosphere. In this study, we use observations provided by SuperDARN radars alongside a Digisonde to determine the temporal response of the different ionospheric layers to the eclipse and compare these observations with modeled behavior.

Joule heating is a major energy sink in the solar wind‐magnetosphere‐ionosphere system and modeling it is key to understanding the impact of space weather on the neutral atmosphere. Ion drifts and neutral wind velocities are key parameters when modeling Joule heating, however there is limited validation of the modeled ion and neutral velocities at mid‐latitudes. We use the Blackstone Super Dual Auroral Radar Network radar and the Michigan North American Thermosphere Ionosphere Observing Network Fabry‐Perot interferometer to obtain the local nightside ion and neutral velocities at ∼40° geographic latitude during the nighttime of 16 July 2014. Despite being a geomagnetically quiet period, we observe significant sub‐auroral ion flows in excess of 200 ms⁻¹. We calculate an enhancement to the local Joule heating rate due to these ion flows and find that the neutrals impart a significant increase or decrease to the total Joule heating rate of >75% depending on their direction. We compare our observations to outputs from the Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM). At such a low geomagnetic activity however, TIEGCM was not able to model significant sub‐auroral ion flows and any resulting Joule heating enhancements equivalent to our observations. We found that the neutral winds were the primary contributor to the Joule heating rates modeled by TIEGCM rather than the ions as suggested by our observations.

The phenomenon known as strong thermal emission velocity enhancement (STEVE) is a narrow optical structure that may extend longitudinally for thousands of kilometers. Initially observed by amateur photographers, it has recently garnered researchers’ attention. STEVE has been associated with a rapid westward flow of ions in the ionosphere, known as subauroral ion drift (SAID). In this work, we investigate three occurrences of STEVE, using data from one of the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ground-based all-sky imagers (ASIs) located at Pinawa, Manitoba, and from the Super Dual Auroral Radar Network (SuperDARN). This approach allows us to verify the correlation between STEVE and SAID, as well as analyze the temporal variation of SAID observed during STEVE events. Our results suggest that the SAID activity starts before the STEVE, and the magnitude of the westward flow decreases as the STEVE progresses toward the end of its optical manifestation.

Sporadic‐E (Es) are thin layers of enhanced ionization observed in the E‐region, typically between 95 and 120 km altitude. Es plays an important role in controlling the dynamics of the upper atmosphere and it is necessary to understand the geophysical factors influencing Es from both the scientific and operational perspectives. While the wind‐shear theory is widely accepted as an important mechanism responsible for the generation of Es, there are still gaps in the current state of our knowledge. For example, we are yet to determine precisely how changes in the dynamics of horizontal winds impact the formation, altitude, and destruction of Es layers. In this study, we report results from a coordinated experimental campaign between the Millstone Hill Incoherent Scatter Radar, the SuperDARN radar at Blackstone, and the Millstone Hill Digisonde to monitor the dynamics of mid‐latitude Es layers. We report observations during a 15‐hr window between 13 UT on 3 June 2022 and 4 UT on 4 June 2022, which was marked by the presence of a strong Es layer. We find that the height of the Es layer is collocated with strong vertical shears in atmospheric tides and that the zonal wind shears play a more important role than meridional wind shears in generating Es, especially at lower altitudes. Finally, we show that in the presence of Es, SuperDARN ground backscatter moves to closer ranges, and the height and critical frequency of the Es layer have a significant impact on the location and intensity of HF ground scatter.

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 (peak Kp reaching 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.

The dependencies of global positioning system (GPS) scintillation indices on ionospheric plasma flow and the rate of change of total electron content (TEC) around the dawn sector for the first time of the polar ionosphere are investigated. The phase scintillation index (σφ) derived from GPS measurements of the Canadian High Arctic Ionospheric Network (CHAIN) shows linear dependencies on both the plasma drift speed measured by the SuperDARN radar and on the rate of change of TEC estimated from the GPS receivers of CHAIN. However, the amplitude scintillation index (S4) does not show any dependence on the plasma flow or the rate of change of TEC. These results further support Wang et al. (2018), https://doi.org/10.1002/2017JA024805 at the noon sector. The dependence of the phase scintillation index on the plasma flow further evidences that the standard phase scintillation index is dominated by refractive variations due to the use of a fixed cut‐off frequency of 0.1 Hz while detrending the phase observable. The dependence of the phase scintillation index on the rate of change of TEC consolidates the dominance of refractive variations inside.

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.

Based on ray tracing in a smooth ionosphere described by the IRI‐2012 model we have inferred the seasonal‐diurnal dynamics of radio noise observed by four mid‐latitude high‐frequency (HF) radars. In the calculations, noise is assumed to be homogeneous and stationary, but the main contribution comes from the radar skip zone boundary due to focusing radiowaves effect. Noise absorption along the ray path is simulated from the IRI‐2012 electron density, and from the molecular nitrogen density and electron temperatures obtained from the NRLMSISE‐00 model. Earth magnetic field is not taken into account both in the absorption and ray‐tracing calculations due to insufficient accuracy of the ionospheric model. The model results are compared with experimental radar data, and good agreement between the two is demonstrated. It is shown that experimentally observed seasonal and diurnal dynamics of the noise correlates well with model predictions. We demonstrated saturation effect at low noise levels. The model makes it possible to estimate the amount of absorption in D‐ and E‐layers using noise observations at SuperDARN and SuperDARN‐like poleward‐oriented radars, especially at mid‐latitudes. This is important for the retrieval of long term variations in the electron density in the lower ionosphere, by using wide coverage provided by these radars' network. The model also makes it feasible to interpret vertical absorption by experimental noise observations, thereby significantly expanding the capability of HF radars to monitor the lower ionosphere, and to provide data for joint analysis with other data, obtained by these radars at E‐ and F‐layer heights.

We utilized citizen scientist photographs of subauroral emissions in the upper atmosphere and identified a repeatable sequence of proton aurora and subauroral red (SAR) arc during substorms. The sequence started with a pair of green diffuse emissions and a red arc that drifted equatorward during the substorm expansion phase. Simultaneous spectrograph and satellite observations showed that they were subauroral proton aurora, where ion precipitation created secondary electrons that illuminated aurora in green and red colors. The ray structures in the red arc also indicated existence of low‐energy electron precipitation. The green diffuse aurora then decayed but the red arc (SAR arc) continued to move equatorward during the substorm recovery phase. This sequence suggests that the SAR arc was first generated by secondary electrons associated with ion precipitation and may then transition to heat flux or Joule heating. Proton aurora provides observational evidence that ion injection to the inner magnetosphere is the energy source for the initiation of the SAR arc.

Sudden enhancement in high‐frequency absorption is a well‐known impact of solar flare‐driven Short‐Wave Fadeout (SWF). Less understood, is a perturbation of the radio wave frequency as it traverses the ionosphere in the early stages of SWF, also known as the Doppler flash. Investigations have suggested two possible sources that might contribute to it’s manifestation: first, enhancements of plasma density in the D‐and lower E‐regions; second, the lowering of the F‐region reflection point. Our recent work investigated a solar flare event using first principles modeling and Super Dual Auroral Radar Network (SuperDARN) HF radar observations and found that change in the F‐region refractive index is the primary driver of the Doppler flash. This study analyzes multiple solar flare events observed across different SuperDARN HF radars to determine how flare characteristics, properties of the traveling radio wave, and geophysical conditions impact the Doppler flash. In addition, we use incoherent scatter radar data and first‐principles modeling to investigate physical mechanisms that drive the lowering of the F‐region reflection points. We found, (a) on average, the change in E‐ and F‐region refractive index is the primary driver of the Doppler flash, (b) solar zenith angle, ray’s elevation angle, operating frequency, and location of the solar flare on the solar disk can alter the ionospheric regions of maximum contribution to the Doppler flash, (c) increased ionospheric Hall and Pedersen conductance causes a reduction of the daytime eastward electric field, and consequently reduces the vertical ion‐drift in the lower and middle latitude ionosphere, which results in lowering of the F‐region ray reflection point.