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.

Abstract 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 complex drivers of equatorial plasma bubbles and resulting scintillation requires a system science approach spanning the Magnetosphere-Ionosphere-Thermosphere-Mesosphere disciplines. The current roadmap missions strongly support this approach, but gaps are identified in planned observations, with potential mission and solutions proposed.

The impact of neutral atmospheric waves has been demonstrated to have profound effects on the ionosphere, but the circumstances under which they generate ionospheric disturbances and seed plasma instabilities are not well understood.Neutral atmospheric waves vary from infrasonic waves of <20 Hz to gravity waves with periods on the order of ten minutes, for simplicity, hereafter they are combined under the common term Acoustic-Gravity Waves (AGWs).There are other longer period waves like planetary waves in the lower and middle atmosphere, whose effects are not as important in the ionosphere as the AGWs.The most ubiquitous and frequently observed impact of AGWs on the ionosphere are Traveling Ionospheric Disturbances (TIDs), but AGWs also affect the global ionosphere/thermosphere circulation and can trigger ionospheric instabilities (e.g.Perkins, Equatorial Spread F).The purpose of this white paper is to outline additional studies and observations that are required in the coming decade to improve our understanding of the impact of AGWs on the ionosphere. Recommendations:• Altitude resolved observations are required to understand the propagation of neutral atmospheric waves and their impacts on the ionosphere.• Upcoming planned and potential missions such as GDC, DYNAMIC, ENLoTIS and CubeSats will be crucial to obtain needed measurements, but additional observations will be required.• Modeling studies should be combined with multi-instrument observations to make progress on these important questions.

The impact of regional-scale neutral atmospheric waves has been demonstrated to have profound effects on the ionosphere, but the circumstances under which they generate ionospheric disturbances and seed plasma instabilities are not well understood. Neutral atmospheric waves vary from infrasonic waves of <20 Hz to gravity waves with periods on the order of 10 min, for simplicity, hereafter they are combined under the common term Acoustic and Gravity Waves (AGWs). There are other longer period waves like planetary waves from the lower and middle atmosphere, whose effects are important globally, but they are not considered here. The most ubiquitous and frequently observed impact of AGWs on the ionosphere are Traveling Ionospheric Disturbances (TIDs), but AGWs also affect the global ionosphere/thermosphere circulation and can trigger ionospheric instabilities (e.g., Perkins, Equatorial Spread F). The purpose of this white paper is to outline additional studies and observations that are required in the coming decade to improve our understanding of the impact of AGWs on the ionosphere.

Seasonal and zonal climatologies of Rayleigh–Taylor growth rates under geomagnetically quiet conditions during solar minimum and solar moderate conditions as a function of local time and altitude are calculated using open source data and software. It is under the action of the Rayleigh–Taylor instability that plumes of depleted plasma, or plasma bubbles, are understood to develop in the bottomside of the equatorial ionosphere. The growin python module utilizes other Heliophysics python modules to collate and process vertical plasma drift to drive the SAMI2 is Another Model of the Ionosphere (SAMI2) model and subsequently calculate the flux tube integrated Rayleigh–Taylor growth rate. The process is repeated for two different types of drift inputs: the Fejer–Scherliess model and measured drifts from the Communication/Navigation Outage Forecasting System (C/NOFS). These growth rates are compared to bubble occurrence frequencies obtained from a dataset of bubbles detected by the C/NOFS satellite. There is an agreement between periods of strong positive instability growth and high frequencies of bubble occurrence in both low and moderate solar activity conditions when using C/NOFS drifts. Fejer–Scherliess drifts are only in agreement with bubble occurrence frequencies during moderate solar activity conditions. Bubble occurrence frequencies are often above 25%, even when growth rates in the bottomside F region are negative. The climatological nature of the growth rates discussed here begs further study into the day-to-day variability of the growth rate and its drivers.

sami2py is a Python module that runs the SAMI2 (Sami2 is Another Model of the Ionosphere) ionospheric model, as well as load and archive the results. SAMI2 is a model developed by the Naval Research Laboratory to simulate the motions of plasma in a two-dimensional ionospheric environment along a dipole magnetic field. SAMI2 solves for the chemical and dynamical evolution of seven ion species in this environment (H+, He+, N+, O+, N2+, NO+, and O2+). The Python implementation allows for additional modifications to the empirical models within SAMI2, including the exospheric temperature in the empirical thermosphere and the input of E×B ion drifts. The code is open source and available to the community on GitHub. The work here discusses the implementation and use of sami2py, including integration with the pysat ecosystem and the growin python package for ionospheric calculations. As part of the Application Usability Level (AUL) framework, we will discuss the usability of this code in terms of several ionospheric applications.