On the Sources of Cold and Dense Plasma in Plasmasphere Drainage Plumes

Previous observations have revealed that the ionospheric Storm Enhanced Density (SED) plumes are colocated with the cold, dense plumes observed at the dayside magnetopause. However, the origin of the cold, dense plume plasma is not well understood with multiple possible sources in the magnetosphere. Improving our understanding of these plasmaspheric plumes is crucial due to their impact on the dayside magnetic reconnection. We report that plumes were simultaneously observed in both the ionosphere and plasmasphere by TEC and geosynchronous spacecraft for the magnetic storms occurred in 2013 and 2015 on Mar 17. Moreover, in 2015, the plume was also observed by THEMIS spacecraft near the dayside magnetopause. Simulations using a physics-based model of the ionosphere and plasmasphere reproduced the observed plume colocation in the ionosphere and plasmasphere for both storms. Our results suggest that plasmaspheric plume was created by the enhanced convection transporting the plasma sunward that was peeled off from the outer plasmasphere, whereas the ionospheric plume plasma came from the density enhancement generated in the dayside subauroral ionosphere. These plumes were observed near the same closed field lines at the peak of the geomagnetic activity because the cold plasma motion in the ionosphere and plasmasphere is connected through the ExB drift motion. Furthermore, our results suggest that weaker storms transport more plasmaspheric materials toward the dayside/duskside magnetopause. However stronger storms may have a larger impact on the dayside reconnection because plasmaspheric plumes tend to be shifted to the noon MLT sector where dayside reconnections more likely to occur. &nbsp; &nbsp; Plain Language Summary The plasmasphere is the region of cold, relatively dense ionized gas (mostly protons and helium ions) that resides on the magnetic field lines close to the Earth. It is the upward extension of cold, dense Earth&rsquo;s ionospheric plasma as the ionosphere had filled the persistently &ldquo;closed&rdquo; flux tubes of plasmasphere. Enhanced convection plasma flow during solar storms peels the cold, dense plasma away from the outer plasmasphere to form a plume of plasma that moves sunward. Recently, the plasmaspheric cold, dense plasma has been found near where the Earth&rsquo;s magnetic field first contacts the solar wind, however, where the plasma originates from remains unclear. Understanding the plume plasma is very important because they could rearrange the magnetic topology by altering the rate of the magnetic reconnection, which determines how much solar wind energy gets into the Earth&rsquo;s magnetosphere. Here we report that in two solar storms in 2013 and 2015, plumes were observed simultaneously in both ionosphere and plasmasphere. Our study suggests that the observed plumes in the plasmasphere and ionosphere were mainly created by different mechanisms, but were observed near the same magnetic field lines at the peak of the solar storms.</p

Storm time neutral density assimilation in the thermosphere ionosphere with TIDA

To improve Thermosphere–Ionosphere modeling during disturbed conditions, data assimilation schemes that can account for the large and fast-moving gradients moving through the modeled domain are necessary. We argue that this requires a physics based background model with a non-stationary covariance. An added benefit of using physics-based models would be improved forecasting capability over largely persistence-based forecasts of empirical models. As a reference implementation, we have developed an ensemble Kalman Filter (enKF) software called Thermosphere Ionosphere Data Assimilation (TIDA) using the physics-based Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model as the background. In this paper, we present detailed results from experiments during the 2003 Halloween Storm, 27–31 October 2003, under very disturbed (Kp = 9) conditions while assimilating GRACE-A and B, and CHAMP neutral density measurements. TIDA simulates this disturbed period without using the L1 solar wind measurements, which were contaminated by solar energetic protons, by estimating the model drivers from the density measurements. We also briefly present statistical results for two additional storms: September 27 – October 2, 2002, and July 26 – 30, 2004, to show that the improvement in assimilated neutral density specification is not an artifact of the corrupted forcing observations during the 2003 Halloween Storm. By showing statistical results from assimilating one satellite at a time, we show that TIDA produces a coherent global specification for neutral density throughout the storm – a critical capability in calculating satellite drag and debris collision avoidance for space traffic management

On Space Weather Data Assimilation

Most if not all terrestrial weather prediction services today are based on data assimilation and numerical weather prediction models. Space Weather services are expected to follow a similar path towards data assimilation. However, the application of data assimilation in Space Weather requires a different implementation compared to terrestrial weather because space systems tend to be strongly forced and because the amount of data available for assimilation is critically small. In this paper we review the implementation of an ensemble Kalman filter data assimilation system based on the Space Weather Prediction Center operational Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model. We present assimilation results for neutral mass density during geomagnetically quiet and disturbed conditions and discuss the future use of data assimilation for the thermosphere ionosphere system

Comparison between operational and research simulations with CTIPe model during geomagnetic storm conditions

The perturbations in the magnetosphere-ionosphere-thermosphere system are significant during geomagnetic storm conditions. The response in the ionosphere-thermosphere (IT) system to these conditions can be analyzed with the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics physics based model (CTIPe). We perform simulations using first the inputs that were available in real-time (operational run) and second with the best estimate obtained after the fact (research run), and compare the results. The CTIPe simulations show input dependent global changes in neutral winds, temperature, and composition which are reflected in the global electron density structure. Comparing the research run results with ionosonde, GNSS and CHAMP satellite observations allows validating the CTIPe results and complete the interpretation of the physical mechanisms behind the perturbations during the event

Thermosphere modeling capabilities assessment: geomagnetic storms

The specification and prediction of density fluctuations in the thermosphere, especially during geomagnetic storms, is a key challenge for space weather observations and modeling. It is of great operational importance for tracking objects orbiting in near-Earth space. For low-Earth orbit, variations in neutral density represent the most important uncertainty for propagation and prediction of satellite orbits. An international conference in 2018 conducted under the auspices of the NASA Community Coordinated Modeling Center (CCMC) included a workshop on neutral density modeling, using both empirical and numerical methods, and resulted in the organization of an initial effort of model comparison and evaluation. Here, we present an updated metric for model assessment under geomagnetic storm conditions by dividing a storm in four phases with respect to the time of minimum Dst and then calculating the mean density ratios and standard deviations and correlations. Comparisons between three empirical (NRLMSISE-00, JB2008 and DTM2013) and two first-principles models (TIE-GCM and CTIPe) and neutral density data sets that include measurements by the CHAMP, GRACE, and GOCE satellites for 13 storms are presented. The models all show reduced performance during storms, notably much increased standard deviations, but DTM2013, JB2008 and CTIPe did not on average reveal a significant bias in the four phases of our metric. DTM2013 and TIE-GCM driven with the Weimer model achieved the best results taking the entire storm event into account, while NRLMSISE-00 systematically and significantly underestimates the storm densities. Numerical models are still catching up to empirical methods on a statistical basis, but as their drivers become more accurate and they become available at higher resolutions, they will surpass them in the foreseeable future

Capabilities of the CTIPe model to reproduce storm conditions

Since the ionosphere is impacting various communication and navigation applications relying on radio signal transmission, accurate monitoring and forecasting of the ionosphere is of great importance. For this purpose, physics based modelling of the coupled thermosphere ionosphere system is rather important, because good forecasts of ionospheric variability especially during storms need to consider the various physical driving processes in the thermosphere and ionosphere. This includes the energy transmission from the solar wind to the magnetosphere and ionosphere-thermosphere, as well as the electric field modifications, enhancement of ionosphere currents and thermosphere circulation and composition disturbances. One of the state of the art numerical models is the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model. The implementation of the model has been enhanced step by step over decades. Recent developments improved the capabilities of reproducing thermosphere ionosphere conditions during storms. These capabilities will be demonstrated based on comparison of CTIPe results during the St. Patrick’s Day storm on 17 March 2015 with ground and space based observations. We will make use of Swarm measurements, ionosondes and GNSS based TEC estimations. The validation results show a rather good reproduction of thermosphere conditions with CTIPe. Especially radiative cooling has improved significantly. The deviations between model and observations are larger for the ionosphere. Based on the validation results the limitations of the model are discussed and next steps for implementation are proposed

On the difference between operational and research simulations with CTIPe

Understanding the thermosphere and ionosphere conditions is crucial for spacecraft operations and many applications using radio signal transmission e.g. in communication and navigation applications. In this sense, physics based modelling plays an important role, since it can adequately reproduce the complex coupling mechanisms in magnetosphere-ionosphere-thermosphere (MIT) system. Next to the capacity of the model itself, the accuracy of the model results depends on the quality of the input data (forcing). In this study, we analyze the impact of input data uncertainties on the model results. We use the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics model (CTIPe), which requires satellite based solar wind, interplanetary field and hemispheric power data from ACE and TIROS/NOAA missions. To identify the impact of the forcing uncertainties, two model runs are compared against each other. The first run uses the input data that were available in real-time (operational run) and the second run uses the best estimate obtained in post-processing (research run). The analysis is performed in a case study on the $20^{th}$ November 2003 extreme geomagnetic storm, that caused significant perturbations in the MIT system. This paper validates the thermosphere and ionosphere response to this storm over Europe comparing both CTIPe model runs with measurements of Total Electron Content (TEC) and thermosphere neutral density. In general, CTIPe results show a good agreement with measurements. However, the deviations between the model and observations are larger in the ionosphere than in the thermosphere. Comparing the two model runs, it has been shown that the deviations between model results and measurements are larger for the operational run than the research run. It is evident for the storm analyzed here, that the best estimate of the forcing has substantially improved the model performance. The consistency between simulation and measurements allows the interpretation of the physical mechanisms behind the ionosphere perturbations and the changes in neutral composition during this event. Joule heating in the Auroral region, generating meridional winds and large scale surges, is suggested to be the main driver of the positive ionospheric storm over central Europe. In the polar cap and Auroral region, convection processes dominate the thermosphere-ionosphere conditions.This study does not only illustrate the importance of working with a good estimate of the model forcings, but also indicates the necessity of using measurements and models, to get a better understanding of the most likely responsible processes for the observed storm effects

The Low-latitutde ionosphere: monitoring its behaviour with GPS

Since the late 1980’s various research groups have been investigating the behaviour of the ionosphere using Global Positioning System (GPS) data. These investigations are based on the total electron content (TEC) measurements derived from dual-frequency GPS observations taking advantage of the dispersive nature of the ionospheric medium. Currently, there is a large number of GPS receivers in continuous operation worldwide. Even though large in number, these stations are unevenly distributed, being situated mostly in the northern hemisphere region. The relatively smaller number of GPS receivers in the southern hemisphere, and consequently the reduced number of available TEC measurements, causes ionospheric modelling to be less accurate for this region. GPS data from the Brazilian Network for Continuous Monitoring by GPS (RBMC) have been used for the first time to obtain TEC values in order to monitor the ionospheric behaviour in the South American region. For this task, we are using the University of New Brunswick (UNB) Ionospheric Modelling Technique which uses a spatial linear approximation of the vertical TEC above each station using stochastic parameters in a Kalman filter estimation to describe the local time and geomagnetic latitude dependence of the TEC. The utilisation of the RBMC GPS data to monitor the ionosphere over South America can help us to obtain a better understanding of many important low latitude ionospheric phenomena, such as the Appleton Equatorial Anomaly and the South Atlantic Anomaly as well as more accurate and representative regional and global ionospheric models. Furthermore, the effect of geomagnetic storms on the equatorial and low-latitude ionosphere is discussed, as well as the integrity of GPS data obtained in equatorial and low-latitude regions

Modeling the ionosphere-thermosphere response to a geomagnetic storm using physics-based magnetospheric energy input: OpenGGCM-CTIM results

The magnetosphere is a major source of energy for the Earth’s ionosphere and thermosphere (IT) system. Current IT models drive the upper atmosphere using empirically calculated magnetospheric energy input. Thus, they do not sufficiently capture the storm-time dynamics, particularly at high latitudes. To improve the prediction capability of IT models, a physics-based magnetospheric input is necessary. Here, we use the Open Global General Circulation Model (OpenGGCM) coupled with the Coupled Thermosphere Ionosphere Model (CTIM). OpenGGCM calculates a three-dimensional global magnetosphere and a two-dimensional high-latitude ionosphere by solving resistive magnetohydrodynamic (MHD) equations with solar wind input. CTIM calculates a global thermosphere and a high-latitude ionosphere in three dimensions using realistic magnetospheric inputs from the OpenGGCM. We investigate whether the coupled model improves the storm-time IT responses by simulating a geomagnetic storm that is preceded by a strong solar wind pressure front on August 24, 2005. We compare the OpenGGCM-CTIM results with low-earth-orbit satellite observations and with the model results of Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe). CTIPe is an up-to-date version of CTIM that incorporates more IT dynamics such as a low-latitude ionosphere and a plasmasphere, but uses empirical magnetospheric input. OpenGGCM-CTIM reproduces localized neutral density peaks at ~ 400 km altitude in the high-latitude dayside regions in agreement with in situ observations during the pressure shock and the early phase of the storm. Although CTIPe is in some sense a much superior model than CTIM, it misses these localized enhancements. Unlike the CTIPe empirical input models, OpenGGCM-CTIM more faithfully produces localized increases of both auroral precipitation and ionospheric electric fields near the high-latitude dayside region after the pressure shock and after the storm onset, which in turn effectively heats the thermosphere and causes the neutral density increase at 400 km altitude