J. Klenzing’s research while affiliated with National Aeronautics and Space Administration and other places

The term “Medium-Scale Traveling Ionospheric Disturbances” is used to describe a number of different propagating phenomena in ionospheric plasma density with a scale size of hundreds of km. This includes multiple generation mechanisms, including ion-neutral collisions, plasma instabilities, and electromagnetic forcing. Observational limitations can impede characterization and identification of MSTID generation mechanisms. We discuss inconsistencies in the current terminology used to describe these and provide a set of recommendations for description and discussion.

Atmospheric neutral density is a crucial component to accurately predict and track the motion of satellites. During periods of elevated solar and geomagnetic activity atmospheric neutral density becomes highly variable and dynamic. This variability and enhanced dynamics make it difficult to accurately model neutral density leading to increased errors which propagate from neutral density models through to orbit propagation models. In this paper we investigate the dynamics of neutral density during geomagnetic storms. We use a combination of solar and geomagnetic variables to develop three Random Forest machine learning models of neutral density. These models are based on (a) slow solar indices, (b) high cadence solar irradiance, and (c) combined high‐cadence solar irradiance and geomagnetic indices. Each model is validated using an out‐of‐sample data set using analysis of residuals and typical metrics. During quiet‐times, all three models perform well; however, during geomagnetic storms, the combined high cadence solar iradiance/geomagnetic model performs significantly better than the models based solely on solar activity. The combined model capturing an additional 10% in the variability of density and having an error up to six times smaller during geomagnetic storms then the solar models. Overall, this work demonstrates the importance of including geomagnetic activity in the modeling of atmospheric density and serves as a proof of concept for using machine learning algorithms to model, and in the future forecast atmospheric density for operational use.

The Space Physics Data Facility (SPDF) is a digital archive of space physics data and is useful for the storage, analysis, and dissemination of data. We discuss the process used to create an amended dataset and store it on the SPDF. The operational software to generate the archival data software uses the open-source Python package pysat, and an end-user module has been added to the pysatNASA module. The result is the addition of data products to the Mars Global Surveyor (MGS) magnetometer (MAG) dataset, its archival location on SPDF, and pysat compatibility. The primary and metadata format increases the convenience and efficiency for users of the MGS MAG data. The storage of planetary and heliophysics data in one location supports the use of data throughout the solar system for comparison, while pysat compatibility enables loading data in an identical format for ease of processing. We encourage the use of the outlined process for past, present, and future space science missions of all sizes and funding levels. This includes balloons to Flagship-class missions.

Equatorial plasma irregularities in the ionospheric F‐region proliferate after sunset, causing the most apparent radio scintillation “hot‐spot” in geospace. These irregularities are caused by plasma instabilities, and appear mostly in the form of under‐densities that rise up from the F‐region's bottomside. After an irregularity production peak at sunset, the amplitude of the resulting turbulence decays with time. Analyzing a large database of irregularity spectra observed by one of the European Space Agency's Swarm satellites, we have applied a novel but conceptually simple statistical analysis to the data, finding that turbulence in the F‐region tends to decay with a uniform, scale‐independent rate, thereby confirming and extending the results from an earlier case study. We find evidence for two regimes, one valid post‐sunset (1.4 hr decay rate) and one valid post‐midnight (2.6 hr). Our results should be of utility for large‐scale space weather modeling efforts that are unable to resolve turbulent effects.

Plasma escape from the high‐latitude ionosphere (ion outflow) serves as a significant source of heavy plasma to the magnetospheric plasma sheet and ring current regions. Outflows alter mass density and reconnection rates, hence global responses of the magnetosphere. A new fully kinetic and semi‐kinetic model, KAOS (Kinetic model of Auroral ion OutflowS), is constructed from first principles which traces large numbers of individual O⁺ ion macro‐particles along curved magnetic field lines, using a guiding‐center approximation, in order to facilitate calculation of ion distribution functions and moments. Particle forces include mirror and parallel electric field forces, a self‐consistent ambipolar electric field, and a parameterized source of ion cyclotron resonance wave heating, thought to be central to the transverse energization of ions. The model is initiated with a steady‐state ion density altitude profile and Maxwellian velocity distribution and particle trajectories are advanced via a direct simulation Monte Carlo scheme. This outlines the implementation of the kinetic outflow model, demonstrates the model's ability to achieve near‐hydrostatic equilibrium necessary for simulation spin‐up, and investigates L‐shell dependent wave heating and pressure cookers scenarios. This paper illustrates the model initialization process and numerical investigations of L‐shell dependent outflows and pressure cooker environments and serves to advance our understanding of the drivers and particle dynamics in the auroral ionosphere.

A major obstacle in cultivating a robust Heliophysics (and broader scientific) community is the lack of diversity throughout science, technology, engineering, and mathematics (STEM) fields. For many years, this has been understood as a “leaky pipeline” analogy, in which predominately minority students initially interested in STEM gradually fall (or are pushed) out of the field on their way to a scientific research position. However, this ignores critical structural and policy issues which drive even later career Ph.D.s out of a career in Heliophysics. We identify here several systemic problems that inhibit many from participating fully in the Heliophysics community, including soft money pressure, lack of accessibility and equity, power imbalances, lack of accountability, friction in collaboration, and difficulties in forming mentorship bonds. We present several recommendations to empower research-supporting organizations to help create a culture of inclusion, openness, and innovative science.

Digisonde data from three different longitude sectors from Jicamarca (12°S, 76.8°W, -2.5° declination angle) from 2001–2016, Ascension Island (7.9°S, 14.4°W, -15.09° declination angle) from 2000–2014, Kwajalein (8.71°N, 167.7°E, 7.5° declination angle) from 2004–2012, has been processed and analyzed to determine statistical studies of equatorial spread F, a diagnostic of irregular plasma structure in the ionosphere. A new method of spread F detection for low latitude region is used to determine solar and seasonal variation over these three sites. An algorithm has been developed to detect the foF2 and hmF2 values from an ionogram and this has been validated using manually scaled ionograms, as well as comparisons to the SAMI2 and IRI models.