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NOAA's Space Weather Prediction Center (SWPC) has responsibility for continuously monitoring, forecasting, and alerting on conditions in the Earth's space environment that impact our technological infrastructure and human health and safety. One of the main space weather products is the ionosphere specification. Ionospheric density changes impact our technologies utilizing global navigation satellite systems (GNSS) and signals propagating between satellites and the ground stations as it can cause signal delays and disruptions. SWPC focuses on improving existing and creating new real-time, operational products of the two main ionospheric descriptive quantities: total electron content (TEC) and ionospheric scintillations. SWPC is also one of the centers that are responsible for space weather services identified by the International Civil Aviation Organization (ICAO). The ionospheric models developed at SWPC target GNSS and HF communication advisories. A new real-time specification tool being developed will support the decision making at the forecast office on more accurate GNSS advisories in the near future. The current ionospheric TEC and scintillation products, such as 2D TEC maps and rate of TEC index (ROTI) maps, have been derived from ground-based GNSS measurements. The exclusively ground based approach has its advantages and disadvantages. One of the prominent disadvantages is that it cannot provide observations over, e.g., oceans and remote areas. Empirical ionospheric models and various smoothing and interpolating techniques have been used to fill these coverage gaps. The introduction of GNSS receivers to low-earth-orbit (LEO) satellites represented a breakthrough, since they allowed GNSS observations to be performed anywhere globally regardless of ground conditions, permitting to perform radio occultation (RO) type remote sensing of the ionosphere, in a so-called limb-sounding geometry (GNSS-to-LEO satellites). The computed excess phase can be used for deriving TEC, ROTI, and phase scintillation (σφ) calculations, while the rapid changes in the signal's carrier-to-receiver noise density ratio or signal-to-noise characteristics allow for the calculation of the amplitude scintillation index (S4 ). Ground-based (disc-sounding geometry) GNSS measurements can also be used to infer the ionospheric TEC and scintillation indices. Therefore, the combination of these limb- and disc-geometry observations should, in theory, provide a more complete global coverage than any of the single geometries alone. The existing ground GNSS networks in combination with the increasing number of RO-capable LEO satellites and constellations may provide better ionospheric products and finer resolutions both spatially and temporally. RO data covering low- and mid-latitudes are available from the COSMIC-2 constellation, while commercial data providers are able to deliver global data from polar orbiting satellites. However, the combination of these two measurement geometries is not trivial, and some fundamental physical questions need to be answered before it can be implemented into a practical application. SWPC's plan is to combine ground- and space-based measurements to build improved real-time operational products of ionospheric TEC and scintillations. These products will be used to monitor the ionosphere and provide alerts for potential disruptions to radio communication and navigation, thus providing society with actionable space weather information. The main challenges are currently being addressed in close collaboration with Boston College and Aerospace Corp.: 1) the validation of ground-based scintillation measurements, 2) geolocation of RO scintillation events and 3) the determination of the limb-to-disc conversion algorithm between ground- and RO-based scintillation magnitudes. There are established methods to geolocate scintillation events along a given signal-path for ground observations, but the magnitude can depend significantly on the direction of the signal propagation in relation to the plasma structures. On the other hand, the RO geolocation algorithm that could cover all latitudes is still being researched and developed. Furthermore, performing a physically sound limb-to-disc conversion of the measurement magnitudes is necessary due to the different geometries of the measurements, which cannot be interpreted together without such a conversion. This has not been completed yet and requires additional work, especially for high-latitudes. Further challenges arise from the fact that the ionosphere is a non-linear, dynamic, and complex system, which makes its modeling, forecasting and even nowcasting quite an intricate task. Both the RO and ground-based GNSS measurements are subject to errors which need to be taken into account and continuously estimated. Such a complex, real-time system and operational product must be updated frequently because data and processing latencies can significantly affect the quality of the service and the ability to base decisions on its output. Despite the challenges, the benefits of using a real-time operational ionospheric product outweigh the costs. This product can be used to improve the safety and reliability of radio communications and navigation globally. It can lead to improved situational awareness of the ionosphere, early warning capability and improved decision-making for mitigation of ionospheric disruptions.

The Global-scale Observations of Limb and Disk (GOLD) imager scans the Earth in the far ultraviolet wavelengths. Spectral measurements from GOLD are used to retrieve column integrated thermospheric neutral temperature (Tdisk) and oxygen to molecular nitrogen column density ratio (O/N2) over about one fourth of the globe. The present investigation assesses the impact of GOLD disk temperatures and O/N2 on the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCMX) using Data Assimilation Research Testbed (DART) ensemble Kalman filter. Observing System Simulation experiments (OSSEs), run with and without GOLD measurements, show that assimilating the two GOLD observations improves the root mean square error (RMSE) and bias of forecast and analysis states. Assimilation of actual GOLD Tdisk observations also shows improvement when compared to independent thermosphere and ionosphere measurements. Using a Specified Dynamics WACCMX (SD-WACCMX) run as the control simulation, we found that the RMSE improvements in the analysis effective temperature and O/N2 are about 10.8% and 22.6%, respectively. Also, RMSE improvement in analysis electron column density is about 10% compared to the control simulation. Moreover, improvement in TEC RMSE is about 25% as observed from OSSE experiments that assimilated synthetic GOLD O/N2 observations. These results demonstrate that assimilation of GOLD temperature and O/N2 observations have a significant impact on the ionosphere-thermosphere model results., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Simulations of the ionospheric response to solar flux changes driven by the 27 d solar rotation have been performed using the global 3-D Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) physics-based numerical model. Using the F10.7 index as a proxy for solar extreme ultraviolet (EUV) variations in the model, the ionospheric delay at the solar rotation period is well reproduced and amounts to about 1 d, which is consistent with satellite and in situ measurements. From mechanistic CTIPe studies with reduced and increased eddy diffusion, we conclude that the eddy diffusion is an important factor that influences the delay of the ionospheric total electron content (TEC). We observed that the peak response time of the atomic oxygen to molecular nitrogen ratio to the solar EUV flux changes quickly during the increased eddy diffusion compared with weaker eddy diffusion. These results suggest that an increase in the eddy diffusion leads to faster transport processes and an increased loss rate, resulting in a decrease in the ionospheric time delay. Furthermore, we found that an increase in solar activity leads to an enhanced ionospheric delay. At low latitudes, the influence of solar activity is stronger because EUV radiation drives ionization processes that lead to compositional changes. Therefore, the combined effect of eddy diffusion and solar activity leads to a longer delay in the low-latitude and midlatitude region.

Complex dynamical changes in the ionosphere and thermosphere during geomagnetic storm times could have a significant impact on our communication and navigation applications, as well as satellite orbit determination and prediction activities. Because of the complex electrodynamics coupling processes during storms, which cannot be fully described with the sparse set of thermosphere-ionosphere (TI) observations, it is crucial to accurately model the state of the TI system. The approximation closest to the true state can be obtained by assimilating relevant measurements into physics-based models. Thermospheric mass density (TMD) derived from satellite measurements is ideal to improve the thermosphere through data assimilation. Given the coupled nature of the TI system, the changes in the thermosphere will also influence the ionosphere state. This study presents for the first time a quantification of the changes and improvement of the model state produced by assimilating TMD not only for the thermosphere density but also for the ionosphere electron density. TMD estimates derived from a single Swarm satellite and the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) physics-based model are used for the data assimilation. The results are presented for a case study during the St. Patrick’s Day storm 2015. It is shown that the TMD data assimilation generates an improvement of the model's thermosphere density of up to 40% (measured along the orbit of the non-assimilated Swarm satellites). The model's electron density during the course of the storm has been improved by approximately 8 and 22% relative to Swarm-A and GRACE, respectively. The comparison of the model's global electron density against a high-quality 3D electron density model, generated through assimilation of total electron content, shows that TMD assimilation modifies the model's ionosphere state positively and negatively during storm time. The major improvement area are the mid-low latitudes. The largest improvements are measured during the storm's recovery phase.

The understanding of the upper atmosphere coupling mechanisms depends to a large extent on an accurate estimate of the true state of the Thermosphere Ionosphere system. We assimilate measurements into physics-based models to provide a better estimate of the state of the system than the one obtained using the model alone. Some of the most important parameters that define the state of the thermosphere are neutral composition and density. Since their changes can modify the production-loss processes and impact the plasma density, in this study we assimilate neutral mass density measurements to evaluate the impact that changes in the thermosphere produce in the ionosphere. The analysis is done during a period of quiet solar geomagnetic conditions (4-8 March 2008) using the physics-based Coupled Thermosphere Ionosphere Plasmasphere electrodynamics model (CTIPe) coupled to an ensemble Kalman filter (EnKF) assimilation scheme, the Thermosphere-Ionosphere Data Assimilation (TIDA). The model results from assimilating accelerometer-derived neutral mass density from CHAMP and GRACE are compared with neutral mass density independent measurements to assess the changes in the thermosphere. Total Electron Content (TEC) observations from the International GNSS Service (IGS) as well as ionosonde data from the GIRO database are used to evaluate the effect over the ionosphere.

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.

Knowledge about the variability of the Thermosphere – Ionosphere (TI) system is essential for many communication and navigation applications, especially during storm conditions, since the ionospheric storm disturbances will influence the propagation characteristics of radio waves. Ionosphere models and ionospheric observations help to analyse and understand the dynamics of ionospheric storms in the strongly interactive TI system. For that reason, this study will assess the performance of physics based and empirical models of the ionosphere. Within the assessment we compare the model results with observations, in order to demonstrate their capabilities. In this study we use two different models; the first model, the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe), is a global 3D time-dependent physics based nonlinear, numerical code that consist of four distinct components running concurrently and fully coupled with respect to energy momentum and continuity. CTIPe will serve as a background model for a new assimilation scheme named Thermosphere Ionosphere Data Assimilation (TIDA). This new scheme is based on an Ensemble Kalman Filter approach as required by the strongly forced nature of the modeled system. The second model, , the high resolution empirical ionosphere model developed at DGFI-TUM is based on the combination of data from different space-geodetic observation techniques, namely GNSS, Satellite Altimetry, DORIS, Langmuir Probe (LP) and Ionospheric Radio Occultations (IRO). As an additional input data source and to stabilize the electron density key parameter estimation, the separation approach is applied to calculate electron density values on a 3D grid, from the combination of electron density profiles calculated by a given model such as IRI or NeQuick and 2D VTEC maps. As a first step of the data assimilation studies in the INSIGHT – II project, we will use the St. Patrick’s Day storm 2015 as case study, to validate the models against Total Electron Content (TEC) derived from ground based Global Navigation Satellite System (GNSS) measurements, as well as the critical frequency of the F2 layer and the maximum electron density height measured by different ionosondes around the globe.

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.

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 ionospheric variability and its complexity is strongly dependent on continuous varying intense solar extreme ultraviolet (EUV) and UV radiations. We investigate the ionospheric response to the solar activity variations during the solar cycle (SC) 23 (1999-2008) and 24 (2009-2017) by using the F10.7 index, and Total Electron Content (TEC) maps provided by the international GNSS service (IGS). Wavelet cross-correlation method is used to evaluate the correlation between F10.7 and the global mean TEC. The maximum correlation is observed at the solar rotation time scale (16-32 days). There is a significant difference in the correlation at the time scale of 32-64 days. During SC 23, the correlation is stronger than during SC 24. This is probably due to the longer lifetime of active regions during SC 23. The wavelet variance estimation method suggests that the variance during SC 23 is more significant than during SC 24. Furthermore, the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model was used to reproduce the ionospheric delay of about 1-2 days observed in the IGS TEC observations. A strong correlation was modelled as well as observed during a high solar activity year (2013) as compared to low a solar activity year (2008). Die ionosphärische Variabilität ist stark abhängig von der kontinuierlich variierenden intensiven solaren extrem ultravioletten (EUV) und UV-Strahlung. Wir untersuchen die ionosphärische Reaktion auf Variationen der Sonnenaktivität während der Sonnenzyklen (SC) 23 (1999-2008) und 24 (2009-2017) mit Hilfe des F10.7-Radioflussindexes und TEC (Gesamtelektronengehalt, Total Electron Content) -Karten, die vom internationalen GNSS-Dienst (IGS) bereitgestellt werden. Wavelet-Kreuzkorrelation wird verwendet, um die Korrelation zwischen F10.7 und global gemitteltem TEC zu bestimmen. Die maximale Korrelation wird auf der Zeitskala der Sonnenrotation (16-32 Tage) beobachtet. Es gibt einen signifikanten Unterschied in der Korrelation auf der Zeitskala von 32 bis 64 Tagen. Während des SC 23 ist die Korrelation stärker als während SC 24. Dies ist auf die längere Lebensdauer der aktiven Regionen zurückzuführen. Das Wavelet-Varianz-Schätzverfahren legt nahe, dass die Varianz beim SC 23 mehr von Bedeutung ist, als während SC 24. Des Weiteren wurde das gekoppelte Thermosphäre-Ionosphäre-Plasmasphäre-Elektrodynamik (CTIPe) Modell verwendet, um die ionosphärische Verzögerung von 1-2 Tagen zu reproduzieren. Eine starke Korrelation wurde bei hoher Sonnenaktivität (2013) im Gegensatz zu geringer Sonnenaktivität (2008) simuliert und auch beobachtet.