This study presents a new methodology to automatically classify perturbations in the lower ionosphere using GNSS radio occultation (RO) observations collected using Spire's constellation of CubeSats. This methodology combines signal processing techniques with semi-supervised machine learning by applying spectral clustering in a metric space of wavelet spectra. A "bottom-up" algorithm was applied to extract E layer information directly from Spire's high-rate (50 Hz) GNSS-RO profiles by subtracting the effect of the F layers. This processing algorithm has been implemented in our ground segment to operationally produce high rate sTEC profiles with a vertical resolution of better than 100 m. The key idea behind the semi-supervised classification is to produce a database of labeled clusters that can be used to classify new unlabeled data by determining which cluster it belongs to. A dataset of more than 12,000 GNSS-RO profiles collected in 2019 containing sTEC perturbations is used to find the initial clusters. This dataset is used to represent the climatology of ionospheric perturbations, such as MSTIDs and sporadic Es. The wavelet power spectrum (WPS) is computed for these profiles, and a metric space is defined using the Earth mover's distance (EMD) between the WPS. A self-tuning spectral clustering algorithm is used to cluster the profiles in this metric space. These clusters are used as a reference database of perturbations to classify new sTEC profiles by finding the cluster of the closest profile of the clustered dataset in the EMD metric space. This new methodology is used to construct an automated system to monitor ionospheric perturbations on a global scale.

Abstract Severe space weather was identified as a risk to the UK in 2010 as part of a wider review of natural hazards triggered by the societal disruption caused by the eruption of the Eyjafjallajökull volcano in April of that year. To support further risk assessment by government officials, and at their request, we developed a set of reasonable worst‐case scenarios and first published them as a technical report in 2012 (current version published in 2020). Each scenario focused on a space weather environment that could disrupt a particular national infrastructure such as electric power or satellites, thus, enabling officials to explore the resilience of that infrastructure against severe space weather through discussions with relevant experts from other parts of government and with the operators of that infrastructure. This approach also encouraged us to focus on the environmental features that are key to generating adverse impacts. In this paper, we outline the scientific evidence that we have used to develop these scenarios, and the refinements made to them as new evidence emerged. We show how these scenarios are also considered as an ensemble so that government officials can prepare for a severe space weather event, during which many or all of the different scenarios will materialize. Finally, we note that this ensemble also needs to include insights into how public behavior will play out during a severe space weather event and hence the importance of providing robust, evidence‐based information on space weather and its adverse impacts.

Radio occultation (RO) provides a cost-effective component of the overall sensor mix required to characterise the ionosphere over wide areas and in areas where it is not possible to deploy ground sensors. The paper describes the RO constellation that has been developed and deployed by Spire Global. This constellation and its associated ground infrastructure are now producing data that can be used to characterise the bulk ionosphere, lower ionosphere perturbations, and ionospheric scintillation.

The ionosphere can affect a wide range of radio frequency (RF) systems operating below 2 GHz. One option for mitigating these effects is to produce assimilative models of the ionospheric density from which products can be derived for specific systems. Such models aim to optimally combine a background model of the ionospheric state with measurements of the ionosphere. This approach is analogous to the use of numerical weather prediction in the meteorological community, and has been evolving for ionospheric use for the last 10 to 15 years. Published research has demonstrated the utility of this approach. However, obstacles to providing effective data products remain due to the sparseness of ionospheric data over large parts of the world and the timeliness with which data are available. Spire is working to overcome these issues through the use of its large constellation of satellites that can measure Total Electron Content (TEC) data in both zenith looking and radio occultation (RO) geometries and its large ground station network that will allow low data latency. Spire data will be combined with an innovative data assimilation model (the Spire TEC Environment Assimilation Model, STEAM) to provide accurate and actionable ionospheric products. Data assimilation is required to overcome the limitations and assumptions of the traditional Abel Transform analysis of RO data (i.e., spherical symmetry; transmitter and receiver in free space and the same plane) and to effectively combine RO data, topside data, ground-based GNSS data, and other sources of ionospheric information (i.e., ionosondes). STEAM uses a 4D Local ensemble transform Kalman Filter (LETKF). As with other ensemble methods, the LETKF uses an ensemble of models to approximate the background error covariance matrix. However, the LETKF provides a more efficient way to solve the ensemble equations. Furthermore, 4D operation permits the use of data with varying latency. Localisation means that grid points are only modified by data within a local volume; this restricts spurious long-range spatial correlations and means that the ensemble only has to span the space locally. The LETKF transforms the problem into ensemble space which makes each grid point independent, resulting in an algorithm that is easily parallelised. This paper will describe the data collection and processing chain, the data assimilation model, and plans for the ongoing development of the combined system.

The lower altitude region of the ionosphere (60-150 km) is characterized by a strong coupling between the neutral atmosphere and ionospheric plasma. Due to the high ion-neutral collision rate the plasma at these altitudes is less constrained to follow the magnetic field lines compared to plasma at higher altitudes in the ionosphere. This both permits the development of the windshear mechanism responsible for the formation of sporadic E (Es) layers and affects the coupling between atmospheric gravity waves (AGWs) and the ionospheric plasma. AGWs transport energy from the lower atmosphere upward to higher altitudes. The wave amplitudes increase with altitude and eventually couple to the ionospheric plasma generating electron density perturbations or travelling ionospheric disturbances (TIDs). Es layers are high-density, narrow-altitude layers of enhanced electron density in the ionosphere’s E region. Contrary to what the name would suggest, Es occurs relatively frequently and its climatology has been characterised through ionosonde studies. Furthermore, the vertical structure of Es has been studied using sounding rockets. However, such measurements are very sparse and cannot be used to routine monitoring or for detecting the Es occurrence at a particular time and location. Monitoring AGW and Es layers is of great interest to many terrestrial applications, such as natural hazard warning systems, radio communications, and global navigation satellite system (GNSS) users. Recently, the coupling between Es layers and AGWs has also seen increased research attention. Spire operates a large constellation of 3U cubesats which carry a radio occultation (RO) GNSS receiver. For ionospheric studies, the satellites measure Total Electron Content (TEC) data in both zenith-looking and RO geometries using dual frequency observations. Furthermore, the high rate (50Hz) phase measurements that are generally used for neutral atmosphere RO can also be used to produce relative TEC profiles of the lower ionosphere with high vertical resolution (approximately 100m at E region altitudes). In this talk, we review recent results describing the coverage and quality of E region ionospheric measurements collected by Spire. Furthermore, we describe Spire's Es and AGW automated detection algorithm that is based on a Hilbert–Huang transform (HHT) of the relative TEC profiles and we compare our results with time coincident and co-located ionosonde data. We also look toward the future and describe how low cost cubesat constellations can be used for global monitoring of AGWs and Es layers. These first results also open the way to near real-time monitoring and classification of more general ionospheric anomalies

In a relatively short time, Spire has grown from a small start-up company to the largest commercial producer of satellite-based GNSS Earth observation products. As of May 2020, and after 21 satellite deployments, Spire now has over 80, 3U (10x10x10 cm) Cubesats satellites operating in a variety of orbit planes, third only to Planet and SpaceX in the size of its satellite constellation and growing with each launch. Spire satellites host three primary payloads: a dual-frequency GNSS science receiver, an automatic identification system (AIS) receiver for ship tracking, and an automatic dependent surveillance—broadcast (ADS–B) receiver for aircraft tracking. The Earth observations produced with Spire's GNSS science receiver include atmospheric profiles performed by radio occultation (RO), space weather observations (slant total electron content (TEC) and scintillation indices), and GNSS reflectometry (GNSS-R) using signals from the GPS, GLONASS, QZSS, and Galileo constellations. Spire was the first commercial company to produce RO observations and has participated in a number of commercial data pilot programs with NOAA, NASA, USAF, and ESA, Over the past few years, Spire has expanded and matured the Earth observation products available and has continued to improve and grow the size and capabilities of its constellation. Spire now produces thousands of low-latency RO profiles and millions of TEC observations each day, with plans for over 100 RO-producing satellites in the full constellation. Additionally, Spire recently added GNSS-R capabilities by launching the first two GNSS-R scatterometer configuration satelliites in December of 2019, with plans for two more GNSS-R satellites to be launched in mid-2020. Due to its agility and rapid launch cycle, averaging launches of four to eight satellites every six weeks, Spire has the unique ability to improve performance and add capabilities on-orbit that are impossible with traditional, risk-averse satellite missions. Spire has provided RO and space weather data to the second NOAA Commercial Weather Data Pilot program, the US Air Force Commercial Weather Data Pilot program, ESA, and numerous NWP centers and research institutions. Spire is also pioneering the provision of Earth observation data to NASA and ESA researchers through unique data purchase programs. In our talk we will present an overview of the status and capabilities of the Spire satellites and describe the collection of GNSS-based Earth observations using the GPS, GLONASS, Galileo, and QZSS constellations. We will additionally outline our plans for expanding and adding products to the Spire Earth observation constellation.

The Advanced Ensemble electron density (Ne) Assimilation System (AENeAS) is a new data assimilation model of the ionosphere/thermosphere. The background model is provided by the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) and the assimilation uses the local ensemble transform Kalman filter (LETKF). An outline derivation of the LETKF is provided and the equations are presented in a form analogous to the classic Kalman filter. An enhancement to the efficient LETKF implementation to reduce computational cost is also described. In a 3 day test in June 2017, AENeAS exhibits a total electron content (TEC) RMS error of 2.1 TECU compared with 5.5 TECU for NeQuick and 6.8 for TIE-GCM (with an NeQuick topside).

The standard approach to remove the effects of the ionosphere from neutral atmosphere GPS radio occultation measurements is to estimate a corrected bending angle from a combination of the L1 and L2 bending angles. This approach is known to result in systematic errors and an extension has been proposed to the standard ionospheric correction that is dependent on the squared L1 / L2 bending angle difference and a scaling term (kappa). The variation of kappa with height, time, season, location and solar activity (i.e. the F10.7 flux) has been investigated by applying a 1-D bending angle operator to electron density profiles provided by a monthly median ionospheric climatology model. As expected, the residual bending angle is well correlated (negatively) with the vertical total electron content (TEC). kappa is more strongly dependent on the solar zenith angle, indicating that the TEC-dependent component of the residual error is effectively modelled by the squared L1 / L2 bending angle difference term in the correction. The residual error from the ionospheric correction is likely to be a major contributor to the overall error budget of neutral atmosphere retrievals between 40 and 80 km. Over this height range kappa is approximately linear with height. A simple kappa model has also been developed. It is independent of ionospheric measurements, but incorporates geophysical dependencies (i.e. solar zenith angle, solar flux, altitude). The global mean error (i. e. bias) and the standard deviation of the residual errors are reduced from -1.3 x 10(-8) and 2.2 x 10(-8) for the uncorrected case to -2.2 x 10(-10) rad and 2.0 x 10(-9) rad, respectively, for the corrections using the kappa model. Although a fixed scalar kappa also reduces bias for the global average, the selected value of kappa (14 rad(-1)) is only appropriate for a small band of locations around the solar terminator. In the daytime, the scalar kappa is consistently too high and this results in an overcorrection of the bending angles and a positive bending angle bias. Similarly, in the nighttime, the scalar kappa is too low. However, in this case, the bending angles are already small and the impact of the choice of kappa is less pronounced.

With support from the NASA sounding rocket program, the Air Force Research Laboratory launched two sounding rockets in the Kwajalein Atoll, Marshall Islands in May 2013 known as the Metal Oxide Space Cloud experiment. The rockets released samarium metal vapor at preselected altitudes in the lower F region that ionized forming a plasma cloud. Data from Advanced Research Project Agency Long-range Tracking and Identification Radar incoherent scatter radar and high-frequency (HF) radio links have been analyzed to understand the impacts of the artificial ionization on radio wave propagation. The HF radio wave ray-tracing toolbox PHaRLAP along with ionospheric models constrained by electron density profiles measured with the ALTAIR radar have been used to successfully model the effects of the cloud on HF propagation. Up to three new propagation paths were created by the artificial plasma injections. Observations and modeling confirm that the small amounts of ionized material injected in the lower F region resulted in significant changes to the natural HF propagation environment.

This paper describes the results of an open coordinated ionospheric model challenge. This challenge has compared empirical, physics-based and data assimilation models during extremely quiet solar conditions. The models have been compared by their ability to specify electron density profile peak parameters (foF2 and hmF2) as well as the total electron content (TEC). It is shown that the data assimilation models perform the best, but all models struggle during the test scenario.