Ionospheric Data Assimilation Research Articles

2‐D Total Electron Content and 3‐D Ionospheric Electron Density Variations During the 14 October 2023 Annular Solar Eclipse

AbstractThis study investigates the ionospheric total electron content (TEC) responses in the 2‐D spatial domain and electron density variations in the 3‐D spatial domain during the annular solar eclipse on 14 October 2023, using ground‐based Global Navigation Satellite System (GNSS) observations, a novel TEC‐based ionospheric data assimilation system (TIDAS), ionosonde measurements, and satellite in situ data. The main results are summarized as follows: (a) The 2‐D TEC responses exhibited distinct latitudinal differences. The mid‐latitude ionosphere exhibited a more substantial TEC decrease of 25%–40% along with an extended recovery time of 3–4 hr. In contrast, the equatorial and low‐latitude ionosphere experienced a smaller TEC reduction of 10%–25% and a faster recovery time of 20–50 min. The minimal eclipse effect was observed near the northern equatorial ionization anomaly crest region. (b) The ionospheric electron density variations during the eclipse were effectively reconstructed by TIDAS data assimilation in the 3‐D domain, providing important altitude information with validity. (c) The ionospheric electron density variations showed a notable altitude‐dependent feature. The eclipse led to a substantial electron density reduction of 30%–50%, with the maximum depletion occurring around the ionospheric F2‐layer peak height (hmF2) of 250–350 km. The post‐eclipse recovery of electron density exhibited a relatively slower pace near the F2‐layer peak height than that at lower and higher altitudes.

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)

AbstractThis study has developed a new TEC‐based ionospheric data assimilation system for 3‐D regional ionospheric imaging over the South American sector (TIDAS‐SA) (45°S–15°N, 35°–85°W, and 100–800 km). The TIDAS‐SA data assimilation system utilizes a hybrid Ensemble‐Variational approach to incorporate a diverse set of ionospheric data sources, including dense ground‐based Global Navigation Satellite System (GNSS) line‐of‐sight Total Electron Content (TEC) data, radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2), and altimeter TEC data from the JASON‐3 satellite. TIDAS‐SA can produce a reanalyzed three‐dimensional (3‐D) electron density spatial variation with a high time cadence, yielding spatial‐temporal resolution of 1° (latitude) × 1° (longitude) × 20 km (altitude) × 5 min. This allows us to reconstruct and study the 3‐D ionospheric morphology with multi‐scale structures. The performance of the data assimilation system is validated against independent ionosonde and in situ measurements through an experiment for a strong geomagnetic storm event on 03–04 November 2021. The results demonstrate that TIDAS‐SA can provide detailed and altitude‐resolved information that accurately characterizes the storm‐time ionospheric disturbances in vertical and horizontal domains over the equatorial and low‐latitude regions of South America.

3-D Ionospheric Electron Density Variations during the 2017 Great American Solar Eclipse: A Revisit

This paper studies the three-dimensional (3-D) ionospheric electron density variation over the continental US and adjacent regions during the August 2017 Great American Solar Eclipse event, using Millstone Hill incoherent scatter radar observations, ionosonde data, the Swarm satellite measurements, and a new TEC-based ionospheric data assimilation system (TIDAS). The TIDAS data assimilation system can reconstruct a 3-D electron density distribution over continental US and adjacent regions, with a spatial–temporal resolution of 1∘× 1∘ in latitude and longitude, 20 km in altitude, and 5 min in universal time. The combination of multi-instrumental observations and the high-resolution TIDAS data assimilation products can well represent the dynamic 3-D ionospheric electron density response to the solar eclipse, providing important altitude information and fine-scale details. Results show that the eclipse-induced ionospheric electron density depletion can exceed 50% around the F2-layer peak height between 200 and 300 km. The recovery of electron density following the maximum depletion exhibits an altitude-dependent feature, with lower altitudes exhibiting a faster recovery than the F2 peak region and above. The recovery feature was also characterized by a post-eclipse electron density enhancement of 15–30%, which is particularly prominent in the topside ionosphere at altitudes above 300 km.

Multiple Longitude Sector Storm‐Enhanced Density (SED) and Long‐Lasting Subauroral Polarization Stream (SAPS) During the 26–28 February 2023 Geomagnetic Storm

AbstractThis paper conducts a multi‐instrument analysis and data assimilation study of midlatitude ionospheric disturbances over the European and North American longitude sectors during a strong geomagnetic storm on 26–28 February 2023. The study uses a set of ground‐based (GNSS receivers, ionosondes) observations, space‐borne (DMSP, GOLD) measurements, and a new TEC‐based ionospheric data assimilation system (TIDAS). We observed a series of distinct storm‐time features with regard to storm‐enhanced density (SED) and subauroral polarization stream (SAPS) as follows: (a) Under multiple ring current intensifications, the storm‐time subauroral ionosphere produced long‐lasting duskside SAPS for ∼36 hr along with considerable dawnside SAPS for several hours. (b) Associated with long‐lived SAPS, strong SED occurred consecutively in the European longitude sector near local noon during a positive ionospheric storm and later in the North American longitude sector near local dusk during a negative ionospheric storm. (c) The 3‐D morphology of SED in multiple longitude sectors was reconstructed using TIDAS data assimilation technique with fine‐scale details, which revealed a narrow ionospheric plasma channel with electron density enhancement and layer uplift.

Assimilating GNSS TEC with an LETKF over Yunnan, China

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

Validation of Ionospheric Modeled TEC in the Equatorial Ionosphere During the 2013 March and 2021 November Geomagnetic Storms

AbstractThis paper presents the validation of modeled total electron content (TEC) from 14 ionospheric models, including empirical, physics‐based, and data assimilation (DA) models, hosted by the NASA/NSF Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL). This study aims to assess the current progress and capability of the CCMC‐hosted ionospheric models in capturing the storm time ionosphere during the low and moderate solar flux years. We focus on the low‐latitude ionosphere (i.e., ±40° in magnetic latitude) and compare the modeled TEC with the Madrigal TEC during the 2013 March and 2021 November storms. Multiple metrics are used to quantitatively assess the models' accuracy, precision, association, bias, and capability in capturing the TEC changes in response to the storms. The skill score based on the metric scores is further proposed to evaluate the overall performance of ionospheric models against the reference model (International Reference Ionosphere 2016; IRI‐2016). The results indicate that the DA model GLObal Total Electron Content and JPL Global Ionospheric Map models show good performance in modeling the TEC and reasonably reflect the storm time TEC changes spatially and temporally. The empirical models IRI‐2016 and 2020 show relatively good performance compared with the physics‐based models regarding the model‐data comparison; however, it is difficult to characterize the TEC changes caused by storms. The physics‐based models can simulate the storm effect in spatial and temporal TEC variations better than the empirical model. The performance of ionospheric models in capturing the storm time TEC anomaly is presented and discussed.

A‐CHAIM: Near‐Real‐Time Data Assimilation of the High Latitude Ionosphere With a Particle Filter

AbstractThe Assimilative Canadian High Arctic Ionospheric Model (A‐CHAIM) is an operational ionospheric data assimilation model that provides a 3D representation of the high latitude ionosphere in Near‐Real‐Time (NRT). A‐CHAIM uses low‐latency observations of slant Total Electron Content (sTEC) from ground‐based Global Navigation Satellite System (GNSS) receivers, ionosondes, and vertical TEC from the JASON‐3 altimeter satellite to produce an updated electron density model above 45° geomagnetic latitude. A‐CHAIM is the first operational use of a particle filter data assimilation for space environment modeling, to account for the nonlinear nature of sTEC observations. The large number (>104) of simultaneous observations creates significant problems with particle weight degeneracy, which is addressed by combining measurements to form new composite observables. The performance of A‐CHAIM is assessed by comparing the model outputs to unassimilated ionosonde observations, as well as to in‐situ electron density observations from the SWARM and DMSP satellites. During moderately disturbed conditions from 21 September 2021 through 29 September 2021, A‐CHAIM demonstrates a 40%–50% reduction in error relative to the background model in the F2‐layer critical frequency (foF2) at midlatitude and auroral reference stations, and little change at higher latitudes. The height of the F2‐layer (hmF2) shows a small 5%–15% improvement at all latitudes. In the topside, A‐CHAIM demonstrates a 15%–20% reduction in error for the Swarm satellites, and a 23%–28% reduction in error for the DMSP satellites. The reduction in error is distributed evenly over the assimilation region, including in data‐sparse regions.

3‐D Regional Imaging of Ionosphere Over Africa Through Assimilating Satellite and Ground‐Based Data

AbstractIn this study, the first high‐resolution regional ionospheric model over Africa and adjacent areas (−40–40°N latitude, 30°W–60°E longitude, and 80–1,400 km in altitude) is constructed by assimilating ground‐based slant total electron content (STEC) from 40 GPS (Global Positioning System) receiver stations and space‐based NmF2 (ionospheric F2 peak density) data from C2 (Constellation Observing System for Meteorology, Ionosphere, and Climate‐2) into the International Reference Ionosphere (IRI‐2016) model. An Ionospheric Data Assimilation Four‐Dimensional (IDA4D) technique was used to estimate electron densities as high as 1.5° 3° in latitude and longitude, 10 km in altitude in the E and F regions, and 15 min in universal time. Two experiments were run for the following data sets: (a) GPS‐STECs only and (b) GPS‐STECs and NmF2s from C2 during geomagnetically quiet (6–11 May 2021) and storm periods (12–14 May 2021). The IDA4D assimilation results are validated using independent C2 control group, ionosonde, and JASON‐3 observations. Results for the storm period show that experiment 2 reduces the average root‐mean‐square error (RMSE) of NmF2, foF2, and VTEC by 34%, 31%, and 34%, respectively, and increases the associated correlations by 10%, 14%, and 2% over IRI, respectively. Using IDA4D, we observed enhancement of the northern crest equatorial ionization anomaly in the late evening that was caused by upward and northward plasma transport.

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

AbstractA new TEC‐based ionospheric data assimilation system (TIDAS) over the continental US and adjacent area (20°–60°N, 60°–130°W, and 100–600 km) has been developed through assimilating heterogeneous ionospheric data, including dense ground‐based Global Navigation Satellite System (GNSS) Total Electron Content (TEC) from 2,000+ receivers, Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation data, JASON satellite altimeter TEC, and Millstone Hill incoherent scatter radar measurements. A hybrid Ensemble‐Variational scheme is utilized to reconstruct the regional 3‐D electron density distribution: a more realistic and location‐dependent background error covariance matrix is calculated from an ensemble of corrected NeQuick outputs, and a three‐dimensional variational (3DVAR) method is adopted for measurement updates to obtain an optimal state estimation. The spatial‐temporal resolution of the reanalyzed 3‐D electron density product is as high as 1° × 1° in latitude and longitude, 20 km in altitude, and 5 min in universal time, which is sufficient to reproduce ionospheric fine structure and storm‐time disturbances. The accuracy and reliability of data assimilation results are validated using ionosonde and other measurements. TIDAS reanalyzed electron density is able to successfully reconstruct the 3‐D morphology and dynamic evolution of the storm‐enhanced density (SED) plume observed during the St. Patrick's day geomagnetic storm on 17 March 2013 with high fidelity. Using TIDAS, we found that the 3‐D SED plume manifests as a ridge‐like high‐density channel that predominantly occurred between 300 and 500 km during 19:00–21:00 UT for this event, with the F2 region peak height being raised by 40–60 km and peak density enhancement of 30%–50%.

A regional ionospheric assimilation study with GPS and COSMIC measurements using a 3D-var algorithm (IDA4D)

The Ionospheric Data Assimilation Four-Dimension (IDA4D) is, developed by Bust et al. (2007) , a continuous time and three-dimension variational (3D-Var) algorithm that can optimally estimate the ionosphere from a model and measured data. In this study, we utilized three different data types into IDA4D for a regional ionosphere estimation: slant total electron contents (STEC) obtained from a Global Positioning System (GPS) network, NmF2 (peak electron density of the F2 layer) and STEC from Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) satellite. These multiple type data were assimilated into a background model, the International Reference Ionosphere (IRI – 2016). To evaluate the effect of each data type, the assimilation was performed on the following data combinations (cases): (1) GPS-STEC’s only; (2) GPS-STEC’s and NmF2′s from COSMIC; (3) GPS-STEC’s and COSMIC-STEC’s; and (4) all three data types. For each case computed foF2′s (F2 layer critical frequency) from IDA4D were compared with measured values from five ionosondes (I-Cheon, Jeju, Okinawa, Kokubunji, and Wakkanai) in the region of Korea and vicinities for the test periods of March, June, September and December in 2015. The comparison shows that computed foF2′s for all cases have higher correlation coefficients (CC) and less root mean square errors (RMSE) from measured values than IRI estimated values. In Cases 2, 3 and 4, IDA4D performance progressively improved in areas where GPS measurements were not covered but COSMIC STEC and NmF2 data were available. Furthermore, the IDA4D assimilation yields better results for electron density profiles and TEC values in comparison with observed values than the IRI model does. Thus, our study suggests that the IDA4D model with multiple data types can provide a reliable estimation of the regional ionosphere over the Korean Peninsula and vicinities.

Multi‐Instrument Investigation of the Polar Holes

AbstractDuring the solar minimum the F‐region plasma density can become extremely low, and in some exceptional cases the F‐layer can completely disappear. These F‐region density depletions are particularly deep during geomagnetically quiet nights, when slow plasma convection creates polar holes, extending 100–1000 km in size, where the electron density is several orders of magnitude lower than the background values. Polar holes are believed to be formed during the periods of the very slow anti‐sunward convection, when the plasma is trapped just poleward of the auroral oval in the absence of any ionization sources. At the same time, fast convection with rapid vertical plasma transport has also been associated with the polar hole formation. Combining the electron density measurements from the Resolute Bay Incoherent Scatter Radar (RISR) and Poker Flat ISR (PFISR), the state of art variational data assimilation tool Ionospheric Data Assimilation Four‐Dimensional (IDA4D), and the convection data from the Super Dual Auroral Radar Network (SuperDARN) array, the polar hole formation and evolution are experimentally investigated. The F‐region plasma is traced back in time prior to the polar hole formation to analyze the favorable conditions for the formation of the polar holes. The role of plasma convection in the formation of the polar holes is experimentally investigated. The results show that the polar holes can be formed in the twilight condition and without being trapped in the closed convection loops. The formation of the polar holes were equally possible during slow and strong convection flows, predominantly with a standard two‐cell convection configuration.

Ionospheric TEC data assimilation based on Gauss–Markov Kalman filter

Ionospheric Total Electron Content (TEC) is an important parameter for space weather research. Based on GNSS data from China Crustal Movement Observation Network (CMONOC) and International Reference Ionosphere (IRI) model, we develop a TEC model in China and adjacent region (15°N − 55°N and 70°E − 140°E) with spatial and temporal resolution of 1°×1° and 30 min. This model is developed by using Kalman filter algorithm. In the covariance matrix, the monthly average is used as the background reference field of the error matrix, and the spatial variation of TEC deviating from the reference background field in each day of the month is statistically analyzed. The assimilative results show that the data assimilation method can effectively combine the observational data with the background model to make up for the temporal and spatial limitations of the observations. In addition, the data assimilation algorithm can produce a short-term TEC forecast. The predicted results are consistent to the observations in the early stages of the forecast. With the increase of predicting time, the impact of the observational data on the predicted results weakens. The results become more biased toward the background data, and the biases can be corrected by observation data.

Night-Time Ionospheric Localized Enhancements (NILE) Observed in North America Following Geomagnetic Disturbances.

The Ionospheric Data Assimilation Four‐Dimensional (IDA4D) technique has been coupled to Sami3, which is another model of the ionosphere (SAMI3). In this application, ground‐based and space‐based GPS total electron content (TEC) data have been assimilated into SAMI3, while in‐situ electron densities, autoscaled ionosonde NmF2, and reference GPS stations have been used for validation. IDA4D/SAMI3 shows that night‐time ionospheric localized enhancements (NILE) are formed following geomagnetic storms in November 2003 and August 2018. The NILE phenomenon appears as a moderate, longitudinally extended enhancement of NmF2 at 30°–40°N MLAT, occurring in the late evening (20–24 LT) following much larger enhancements of the equatorial anomaly crests in the main phase of the storms. The NILE appears to be caused by upward and northward plasma transport around the dusk terminator, which is consistent with eastward polarization electric fields. Independent validation confirms the presence of the NILE, and indicates that IDA4D is effective in correcting random errors and systematic biases in SAMI3. In all cases, biases and root‐mean‐square errors are reduced by the data assimilation, typically by a factor of 2 or more. During the most severe part of the November 2003 storm, the uncorrected ionospheric error on a GPS 3D position at 1LSU (Louisiana) is estimated to exceed 34 m. The IDA4D/SAMI3 specification is effective in correcting this down to 10 m.

Evaluation of the New Background Covariance Model for the Ionospheric Data Assimilation

AbstractThis paper presents the evaluation of the recently developed covariance model for the Ionospheric Data Assimilation Four‐Dimensional (IDA4D) technique. The ionospheric data are generated using the Observation System Simulation Experiment Tool from the known ionospheric state produced by the physics‐based Thermosphere‐Ionosphere‐Mesosphere Electrodynamics General Circulation Model. Several experiments are conducted to assess performance of IDA4D with data‐driven vertical and horizontal covariance matrices. We show that the vertical part of the covariance model plays the most important role because it preserves the vertical structure of the F‐region density layer and helps to correct a tomographic issue that arises when the slant total electron content is assimilated along the intersecting rays. The results show that the new covariance model improves the fidelity of IDA4D algorithm, making it more suitable for the regional assimilation with dense ground‐based Global Positioning System data coverage.

Comparison of Low‐Latitude Ionospheric Scintillation Forecasting Techniques Using a Physics‐Based Model

AbstractThe temporal change in height of a specific electron density can be used as a proxy for vertical plasma drift (PVPD) at the magnetic equator. The use of PVPDs as a predictor of low‐latitude ionospheric scintillation during the subsequent evening has previously been shown to have forecasting skill when using ionosonde data. The implementation of this approach using a physics‐based model is proposed to provide greater forecast antecedence without the need for local ionosondes. For the first time, the physics‐based model PVPD method is compared to another forecasting approach that uses a physics‐based model to calculate Rayleigh‐Taylor growth rates (RTGRs). In equinoctial test cases considered, when appropriate scintillation observation thresholds are selected, PVPD forecasting is shown to have skill similar to or better than the RTGR method using the same physics‐based model. PVPD forecasting requires only electron densities and corresponding altitudes. Therefore, this approach could be applied using an ionospheric data assimilation model whereas the majority of these models do not provide output for all variables required for RTGR forecasting. The forecasting skill in these test cases, the simplicity of physics‐based PVPD forecasting, and the suitability of this method for use of ionospheric data assimilation model output make this method attractive as a forecasting tool in an operational setting if skill can be further demonstrated for a wide range of conditions. However, both PVPD and RTGR forecasting skill are shown to be limited during solstitial months with high scintillation activity. This may be improved by using a data assimilation model.

Ionospheric Vertical Correlation Distances: Estimation From ISR Data, Analysis, and Implications For Ionospheric Data Assimilation

AbstractThe construction of the background covariance matrix is an important component of ionospheric data assimilation algorithms, such as Ionospheric Data Assimilation Four‐Dimensional (IDA4D). It is a matrix that describes the correlations between all the grid points in the model domain and determines the transition from the data‐driven to model‐driven regions. The vertical component of this matrix also controls the shape of the assimilated electron density profile. To construct the background covariance matrix, the information about the spatial ionospheric correlations is required. This paper focuses on the vertical component of the model covariance matrix. Data from five different incoherent scatter radars (ISR) are analyzed to derive the vertical correlation lengths for the International Reference Ionosphere (IRI) 2016 model errors, because it is the background model for IDA4D. The vertical distribution of the correlations is found to be asymmetric about the reference altitude around which the correlations are calculated, with significant differences between the correlation lengths above and below the reference altitude. It is found that the correlation distances not only increase exponentially with height but also have an additional bump‐on‐tail feature. The location and the magnitude of this bump are different for different radars. Solar flux binning introduces more pronounced changes in the correlation distances in comparison to magnetic local time (MLT) and seasonal binning of the data. The latitudinal distribution of vertical correlation lengths is presented and can be applied to the construction of the vertical component of the background model covariance matrix in data assimilation models that use IRI or similar empirical models as the background.

The Global Analysis of the Ionospheric Correlation Time and Its Implications for Ionospheric Data Assimilation

AbstractIonospheric correlation time is an important parameter that contains information about the temporal variability, structures, and dynamics of the ionosphere. This parameter is also important in forecasting of the ionospheric state. Ionospheric data assimilation algorithms employing empirical background models, such as Ionospheric Data Assimilation Four‐Dimensional (IDA4D), apply Gauss‐Markov approximation for the propagation of temporal updates from one time stamp to the next. In this process, the relaxation parameter, or the correlation time, determines to what degree the projected state depends on the background model and analysis density. An ad hoc approach is usually applied to choose this user‐defined parameter. This paper focuses on the estimation of the ionospheric correlation time using high temporal resolution global ionospheric maps (GIMs). It is found that the correlation time changes significantly with latitude. The longest correlation time is observed in the equatorial region, whereas the shortest correlation time is observed at high latitudes and in the polar cap regions. The global distribution of the correlation time exhibits seasonal variation and depends on the solar flux conditions. The correlation time at the equatorial and mid‐latitude regions increases with increasing solar and geomagnetic activity, whereas the correlation time at high‐latitude regions decreases. The results of this study can be directly applied to improve ionospheric data assimilation models.

Ionospheric Horizontal Correlation Distances: Estimation, Analysis, and Implications for Ionospheric Data Assimilation

AbstractThe background covariance matrix establishes the transition from the data‐ to model‐driven regions in the ionospheric data assimilation algorithms. To construct the background covariance matrix, the information about the spatial ionospheric correlations of model errors is required. This paper focuses on the horizontal component of the covariance matrix. It is the first study that presents global maps of zonal and meridional ionospheric correlation lengths derived for IRI‐2016 model errors. The model errors were calculated using 20 years of GPS total electron content (TEC) values from the Jet Propulsion Laboratory Global Ionospheric Maps (GIMs) for different seasons, geomagnetic conditions, and universal times. The correlation lengths derived from IRI model errors were analyzed and compared to correlation lengths derived from day‐to‐day ionospheric variability calculated from GIM. It was found that the global distributions of the zonal and meridional correlation lengths between the two approaches are very different and that the correlation lengths derived from day‐to‐day TEC variability cannot be used as a proxy for the construction of covariance matrix for ionospheric data assimilation. A new method is proposed for the modeling of azimuthal distribution of the correlation distances that considers the nonisotropic nature of the distribution of correlations around the reference point.

The Early Results and Validation of FORMOSAT‐7/COSMIC‐2 Space Weather Products: Global Ionospheric Specification and Ne‐Aided Abel Electron Density Profile

AbstractThe FORMOSAT‐7/COSMIC‐2 (F7/C2) satellite mission was launched on 25 June 2019 with six low‐Earth‐orbit satellites and can provide thousands of daily radio occultation (RO) soundings in the low‐latitude and midlatitude regions. This study shows the preliminary results of space weather data products based on F7/C2 RO sounding: global ionospheric specification (GIS) electron density and Ne‐aided Abel and Abel electron density profiles. GIS is the ionospheric data assimilation product based on the Gauss‐Markov Kalman filter, assimilating the ground‐based Global Positioning System and space‐based F7/C2 RO slant total electron content, providing continuous global three‐dimensional electron density distribution. The Ne‐aided Abel inversion implements four‐dimensional climatological electron density constructed from previous RO observations, which has the advantage of providing altitudinal information on the horizontal gradient to reduce the retrieval error due to the spherical symmetry assumption of the Abel inversion. The comparisons show that climatological structures are consistent with each other above 300 km altitude. Both the Abel electron density profiles and GIS detect electron density variations during a minor geomagnetic storm that occurred within the study period. Moreover, GIS is further capable of reconstructing the variation of equatorial ionization anomaly crests. Detailed validations of all the three products are carried out using manually scaled digisonde NmF2 (hmF2), yielding correlation coefficients of 0.885 (0.885) for both Abel inversions and 0.903 (0.862) for GIS. The results show that both GIS and Ne‐aided Abel are reliable products in studying ionosphere climatology, with the additional advantage of GIS for space weather research and day‐to‐day variations.

Assimilation of GNSS Measurements for Estimation of High‐Latitude Convection Processes

AbstractGeomagnetic storms produce significant electrodynamics at midlatitudes. Strong ion convection can affect thermospheric neutral wind motion. The converse is also true, such that both fields and winds can drive ionospheric plasma movement. This work adjusts a background modeled high‐latitude electrostatic potential to estimate the storm time electric field based on data‐derived plasma densities and measurements of neutral wind. Electron densities are derived from global navigation satellite system (GNSS) total electron content measurements using Ionospheric Data Assimilation Four‐Dimensional (IDA4D). These are input to Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) to estimate electric potential corrections to the background Weimer 2000 potential model. The EMPIRE basis functions for electric potential are spherical harmonics in dipole magnetic colatitude and longitude, enforced to be constant along a magnetic field line. For the 17 March 2015 storm, EMPIRE electric potential produces westward zonal ion drifts that more closely agree with incoherent scatter radar (ISR) measurements made at Millstone Hill than the background Weimer 2000 model alone, when electric potential and meridional neutral winds are both corrected. Additionally ingesting northward line‐of‐sight neutral wind measurements from a Fabry‐Perot interferometer at Millstone Hill makes little difference in the agreement between zonal ion drift predictions and measurements. Estimation of only electric potential reduces the agreement between the assimilated prediction of the field‐perpendicular zonal drifts and ISR measurements significantly.