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This work develops a method by which a kilometer-spaced array of Global Navigation Satellite System (GNSS) scintillation receivers can be used to estimate the ionospheric irregularity layer height and thickness and associated uncertainties on those estimates. Spectra of filtered signal power and phase data are used to estimate these quantities by comparing the observed ratio of the log of the power spectrum to the phase spectrum with the Rytov weak scatter theoretical ratio. A Monte Carlo simulation of noise on the input signal and the irregularity drift velocity is used to quantify the error in estimates of height and thickness. The method is tested using data from the Scintillation Auroral Global Positioning System (GPS) Array (SAGA) sited in the auroral zone at Poker Flat Research Range, Alaska. For the 30-min scintillation period studied, the technique identifies ionospheric scattering from a thick F layer, which correlates well with on-site incoherent scatter radar measurements of peak electron density, for an event previously identified in the literature as likely due to F layer.

During extreme ionospheric storms, plasma behaves very differently than current physics models predict, such as by forming a nighttime ionospheric localized enhancement (NILE). Aiming to decrease the gap between the real measurements and background model estimation, we propose using a data assimilation tool: Estimating Model Parameters Reverse Engineering (EMPIRE) with Kalman filter implementation to ingest ion drift measurements to enable the estimation of electric potential and neutral winds. In this preliminary work, we show results for one storm-time event of EMPIRE estimating global winds and electric potential when augmenting the linear system of observations with measurements of LOS ion drift. We first assimilate Global Navigation Satellite System (GNSS)-based total Electron Content (TEC) measurements with the Sami3 is Another Model of the Ionosphere (SAMI3) first-principles background using the Ionospheric Data Assimilation 4-Dimensional (IDA4D) algorithm. IDA4D yields estimates of plasma density globally at 3-degree resolution and at 5-minute intervals. We then time difference the plasma densities and assimilate those, plus observations of ion drifts speeds from incoherent scatter radar (ISR) located at Massachusetts institute of Technology (MIT) Haystack Observatory. We compare the difference in results between a solution that does not assimilate ion drift (relying on density rates alone versus a solution that does assimilate the ion drift). Three analysis locations are selected: the location of ISR, the location of the NILE, and a site near the magnetic equator along the same longitude line. From the assimilated results, we show that new augmentation of ion velocity measurements impacts the estimated ion motions at all three locations. Further investigation is required to determine the extent to which the ingestion improves the estimation.

The Ionospheric Data Assimilation Four-Dimensional (IDA4D) technique has been coupled to Sami3 is Another Model of the Ionosphere (SAMI3). In this application, ground- 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.

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.

During geomagnetic storms, the space environment can be drastically altered as the plasma in the upper atmosphere, or ionosphere, moves globally. This plasma redistribution is mainly caused by storm-time electric fields, but another important driver of the velocity of the ions in the plasma is the neutral winds. These winds refer to the movement of the neutral particles that are part of the thermospheric layer of the atmosphere, that can drag the plasma. Geomagnetic storms increase the neutral winds, due to the heating of the thermosphere that comes from the storm. In this study we want to understand how these ionospheric drivers affect the ionosphere behavior because, among other reasons, during geomagnetic storms the plasma can refract and diffract trans-ionospheric signals and, consequently, can cause problems in the navigation systems such as GNSS (Global Navigation Satellite System)/GPS (Global Positioning System) that use the information from the signals.In this work, our objective is to estimate the electric fields and neutral winds globally during a geomagnetic storm. Global GNSS TEC (total electron content) measurements are ingested by the Ionospheric Data Assimilation 4-Dimensional (IDA4D) algorithm [1], whose output is the electron density rate over a grid at different time steps during a geomagnetic storm. The density rates are treated as “observations” in EMPIRE (Estimating Model Parameters from Ionospheric Reverse Engineering), which is a data assimilation algorithm based on the plasma continuity equation [2,3,4]. Then, the EMPIRE “observations” are used to estimate corrections to the electric field and neutral winds by solving a Kalman filter. To study these drivers with EMPIRE, basis functions are used to describe them. For the global potential field, spherical harmonics are used.To have a global estimation of the neutral winds, we introduce vector spherical harmonics as the basis function for the first time in EMPIRE. The vector spherical harmonics are used to model orthogonal components of neutral wind in the zonal (east-west) and meridional (north-south) directions. EMPIRE’s Kalman filter needs the error covariance of the vector spherical harmonics decomposition. To calculate it, the basis function is fitted to the model HWM14 (Horizonal Wind Model) values of the neutral winds and the error between the fitting and the model is studied. Later, we study the global potential field and global neutral winds over time to understand how much each driver contributes to the plasma redistribution during the geomagnetic storm on October 25th 2011. We compare the results to FPI (Fabry-Perot Interferometer) neutral winds measurements to validate the results. [1] G.S.Bust, G.Crowley, T.W.Garner, T.L.G.II, R.W.Meggs, C.N.Mitchell, P.S.J.Spencer, P.Yin, and B.Zapfe, Four-dimensional gps imaging of space weather storms, Space Weather, 5 (2007), doi:10.1029/2006SW000237.[2] D.S.Miladinovich, S.Datta-Barua, G.S.Bust, and J.J.Makela, Assimilation of thermospheric measurements for ionosphere-thermosphere state estimation, Radio Science, 51 (2016).[3] D.S.Miladinovich, S.Datta-Barua, A.Lopez, S. Zhang, and G.S.Bust, Assimilation of gnss measurements for estimation of high-latitude convection processes, Space Weather, 18 (2020).[4] G.S.Bust and S.Datta-Barua, Scientific investigations using ida4d and empire, in Modeling the Ionosphere-Thermosphere System, J. Huba, R. Schunk, and G. Khazanov, eds., John Wiley & Sons, Ltd, 1 ed., 2014.

We present analysis methods for studying the structuring and motion of ionospheric irregularities at the sub-kilometer scale sizes that produce L-band scintillations. Spaced receiver methods are used for Global Navigation Satellite System (GNSS) receivers' phase measurements over approximately sub-kilometer to kilometer-length baselines for the first time. The quantities estimated by these techniques are: plasma drift velocity, diffraction anisotropy magnitude and orientation, and characteristic velocity. Uncertainties are quantified by ensemble simulation of noise on the phase signals carried through to the observations of the spaced-receiver linear system. These covariances are then propagated through to uncertainties on drifts through linearization about the estimated values of the state. Five receivers of the Scintillation Auroral Global Positioning System (GPS) Array (SAGA) provide 100 Hz power and phase data for each channel at L1 frequency. The array is sited in the auroral zone at Poker Flat Research Range, Alaska. A case study of a single scintillating satellite observed by the array is used to demonstrate the spaced receiver and uncertainty estimation process. A second case study estimates drifts as measured by multiple scintillating channels. These scintillations are correlated with auroral activity, based on all-sky camera images. Measurements and uncertainty estimates made over a 30-minute period are made and compared to a collocated incoherent scatter radar, and show good agreement in horizontal drift speed and direction during periods of scintillation for which the characteristic velocity is less than the drift velocity.

Past extreme geomagnetic storms have been known to produce not only dayside storm-enhanced density (SED) and SED plumes, but also in a number of cases an nighttime ionospheric localized enhancement (NILE), also known as the “Florida effect.” In this work, we apply data assimilation to adjust a background model to better capture the NILE enhancement. We use the SAMI3 (SAMI3 is Another Model of the Ionosphere) model for an extreme storm as the background model in Ionospheric Data Assimilation 4-Dimensional (IDA4D) to produce plasma density estimates across the NILE feature. IDA4D ingests GPS-based total electron content (TEC) to reconstruct ionospheric plasma densities over space and time. Simulating the November 20-21, 2003, extreme geomagnetic storm, we show that the SAMI3 model alone underestimates the magnitude of positioning errors for fictitious receivers in the Caribbean, Gulf coast, and Rocky Mountains, subjected to the dayside SED by as much as a factor of 3. During the nighttime data assimilation of SAMI3 with IDA4D shows uplift in the peak densities. The day following the storm shows a negative phase (depressed TEC) relative to SAMI3 alone.

When Global Navigation Satellite System (GNSS) signals traverse the ionosphere, variations in plasma density cause the received signal at the ground to scintillate, i.e., exhibit a rapid fluctuation in power and/or phase. Scintillation at high latitudes near the auroral zone can be frequent, and are typically observed, with standard GNSS receiver processing, as phase scintillations. Scintillation is an undesirable effect for navigation purposes, because it degrades signal quality and can lead to poor tracking, cycle slips, and loss of lock on satellites needed for robust positioning. One of the challenges to understanding the irregularities producing scintillation is that they are not directly sensible with established sensing methods such as incoherent scatter radar. A motivating premise behind this work is that, while undesirable for navigation, scintillation of a received signal is beneficial for ionospheric study because it can be used to investigate the properties of and the physical mechanisms producing exactly those irregularities that scatter the signal.

We develop a method that uses data assimilation to estimate ionospheric-thermospheric (IT) states during mid-latitude nighttime storm conditions. The algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) uses time-varying electron densities in the F-region, derived primarily from total electron content data, to estimate two drivers of the IT: neutral winds and electric potential. A Kalman filter is used to update background models based on ingested plasma densities and neutral wind measurements. This is the first time a Kalman filtering technique is used with the EMPIRE algorithm and the first time neutral wind measurements from 630.0-nm Fabry-Perot interferometers (FPI) are ingested to improve estimates of storm time ion drifts and neutral winds. The effects of assimilating remotely sensed neutral winds from FPI observations are studied by comparing results of ingesting: electron densities (N) only, N plus half the measurements from a single FPI, and then N plus all of the FPI data. While estimates of ion drifts and neutral winds based on N give estimates similar to the background models, this study's results show that ingestion of the FPI data can significantly change neutral wind and ion drift estimation away from background models. In particular, once neutral winds are ingested, estimated neutral winds agree more with validation wind data and estimated ion drifts in the magnetic field parallel direction are most sensitive as compared to field-perpendicular-zonal and -meridional directions. Also, data assimilation with FPI measurements helps provide insight into the effects of contamination to 630.0-nm emissions experienced during geomagnetic storms.

This paper surveys GPS L1 and L2C scintillations automatically detected, classified, and attributed to a scattering layer from data collected in 2014-2015 by the Scintillation Auroral GPS Array (SAGA) in Poker Flat Research Range, Alaska. The automated method scans scintillation indices recorded by SAGA for array-wide intervals of phase scintillation, amplitude scintillation, or both phase-and-amplitude scintillation at each of L1 and L2 frequencies. For each of the scintillating time intervals found, plasma density data from the collocated Poker Flat Incoherent Scatter Radar (PFISR) are used to attribute the GPS signal scattering to either the E layer or the F layer of the ionosphere, based on whether the peak density is above or below 200 km altitude. The vast majority of scintillation events are phase scintillations, whether at L1 or at L2C. While the majority of them are attributed to the F layer in 2014, the majority of the scintillation events in 2015 are attributed to the E layer, based on the PFISR data. This indicates that E-layer-related scintillation may be quite common at auroral latitudes, and that GPS receivers are sensitive to the irregularities occurring there. We follow the survey with an individual case study of an L1 phase scintillation event associated with the E region. Analysis of 100 Hz detrended and filter power and phase from SAGA show that the phase variations have no time lag across the array, indicating that the irregularities’ drift velocity, anisotropy, scattering layer height and thickness cannot be estimated directly from SAGA for this event. However, the Satellite-beacon Ionospheric-scintillation Global Model of the upper Atmosphere (SIGMA) forward propagation scintillation model is used to search for the parameters that best fit the observed power spectral density (PSD) of the scintillations. A best fit model of a scattering layer in the E region at 120 km in the region shows a reasonable agreement with the observed PSD. Interestingly an F layer irregularity model at 350 km produces a slightly better fit. The PFISR density data show enhanced densities from the E layer through the lower F layer during this time. Further investigation of this event and comparisons to other E layer and F layer events will help resolve this discrepancy in the future.

Global Navigation Satellite System (GNSS) reflectometry leverages signals of opportunity to remotely sense the Earth's surface for a variety of science investigations. However, ionospheric refraction affects GNSS reflections detected at low Earth orbit (LEO). While multifrequency GNSS enables the elimination of most of the ionospheric error, single-frequency missions are still susceptible to this ranging delay. Motivated by the planned launch of Cyclone GNSS (CYGNSS) in 2016, a single-frequency reflectometry mission, this letter presents a simulation of the relative ionospheric delay that will shift the Delay-Doppler Map (DDM) data product. A mathematical model is presented that defines and characterizes signal propagation delay in the DDM. The model differentiates direct and reflected signals as a sum of path lengths, atmospheric refraction effects, and noise. We simulate representative ionospheric delays from the model associated with the direct and reflected ray paths as a function of satellite elevation angle, latitude, and solar activity. Simulation using the International Reference Ionosphere 2007 shows that differential ionospheric content is inversely proportional to satellite elevation angle and that low latitudes present larger ionospheric impacts on the reflected signals. Finally, high solar activity conditions lift the ionospheric density profile to and possibly above the CYGNSS orbit altitude of 500 km. The ionospheric delay will not generally affect the estimation of wind speed but may affect the CYGNSS signal acquisition and open loop tracking process. Implications of the ionospheric delay in other GNSS reflectometry applications are also discussed.

We introduce and analyze the first data from an array of closely spaced Global Positioning System (GPS) scintillation receivers established in the auroral zone in late 2013 to measure spatial and temporal variations in L band signals at 100–1000 m and subsecond scales. The seven receivers of the Scintillation Auroral GPS Array (SAGA) are sited at Poker Flat Research Range, Alaska. The receivers produce 100 s scintillation indices and 100 Hz carrier phase and raw in-phase and quadrature-phase samples. SAGA is the largest existing array with baseline lengths of the ionospheric diffractive Fresnel scale at L band. With an initial array of five receivers, we identify a period of simultaneous amplitude and phase scintillation. We compare SAGA power and phase data with collocated 630.0 nm all-sky images of an auroral arc and incoherent scatter radar electron precipitation measurements, to illustrate how SAGA can be used in multi-instrument observations for subkilometer-scale studies. Key Points A seven-receiver Scintillation Auroral GPS Array (SAGA) is now at Poker Flat, Alaska SAGA is the largest subkilometer array to enable phase/irregularities studies Simultaneous scintillation, auroral arc, and electron precipitation are observed

Scintillations in the Global Navigation Satellite System (GNSS) signals are rapid variations in their amplitude and phase resulting from electron density irregularities in the ionosphere, can be used to study the ionospheric irregularities. We utilize Global Positioning System (GPS) scintillation measurements from scintillation auroral GPS array (SAGA) at the Poker Flat Research Range (PFRR) (Datta-Barua et al., 2015), Alaska on 8 December 2013 in conjunction with ancillary multi-instrument observations combined with physical parameters derived from a forward propagation model, and an inverse method to achieve an improved understanding of the physics of the irregularities and the processes that may produce them.

[1] This paper reviews the effects of geomagnetic activity of solar cycle 24 from 2011 through mid-2013 on the Federal Aviation Administration's Wide Area Augmentation System (WAAS) navigation service in the U.S., to identify (a) major impacts and their severity compared with the previous cycle and (b) effects in new service regions of North America added since last solar cycle. We examine two cases: a storm that reduced service coverage for several hours and ionospheric scintillation that led to anomalous receiver tracking. Using the 24–25 October 2011 storm as an example, we examine WAAS operational system coverage for the conterminous U.S. (CONUS). The WAAS algorithm upgrade to ionospheric estimation, in effect since late 2011, is able to mitigate the daytime coverage loss but not the nighttime loss. We correlate WAAS availability to maps of the storm plasma generated with the data assimilative model Ionospheric Data Assimilation 4-D, which show a local nighttime corotating persistent plume of plasma extending from Florida across central CONUS. We study the effect of scintillation on 9 October 2012 on the WAAS reference station at Fairbanks, Alaska. Data from a nearby scintillation monitor in Gakona and all-sky imaging of aurora at Poker Flat corroborate the event. Anomalous receiver processing triggered by scintillation reduces accuracy at Fairbanks for a few minutes. Users experiencing similar effects would have their confidence bounds inflated, possibly trading off service continuity for safety. The activity to date in solar cycle 24 has had minor effects on WAAS service coverage, mainly occurring in Alaska and Canada.

[1] This paper uses data assimilation to estimate ionospheric state during storm time at subdegree resolution. We use Ionospheric Data Assimilation Four-Dimensional (IDA4D) to resolve the three-dimensional time-varying electron density gradients of the storm-enhanced density poleward plume. By Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), we infer the three-dimensional plasma velocity from the densities. EMPIRE estimates of ExB drift are made by correcting the Weimer 2000 electric potential model. This is the first time electron densities derived from GPS total electron content (TEC) data are being used to estimate field-aligned and field-perpendicular drifts at such high resolution, without reference to direct drift measurements. The time-varying estimated electron densities are used to construct the ionospheric spatial decorrelation in vertical total electron content (TEC) on horizontal scales of less than 100 km. We compare slant TEC (STEC) estimates to actual STEC GPS observations, including independent unassimilated data. The IDA4D density model of the extreme ionospheric storm on 20 November 2003 shows STEC delays of up to 210 TEC units, comparable to the STEC of the GPS ground stations. Horizontal drifts from EMPIRE are predicted to be northwestward within the storm-enhanced density plume and its boundary, turning northeast at high latitudes. These estimates compare favorably to independent Assimilative Mapping of Ionospheric Electrodynamics-assimilated high-latitude ExB drift estimates. Estimated and measured Defense Meteorological Satellite Program in situ drifts differ by a factor of 2–3 and in some cases have incorrect direction. This indicates that significant density rates of change and more accurate accounting for production and loss may be needed when other processes are not dominant.

Ground-based augmentations of the global positioning system demand guaranteed integrity to support aircraft precision approach and landing navigation. To quantitatively evaluate navigation integrity, an aircraft computes vertical and lateral protection levels as position-error bounds using the standard deviation of ionosphere spatial decorrelation. Thus, it is necessary to estimate typical ionospheric gradients for nominal days and to determine an appropriate upper bound to sufficiently cover the differential error due to the ionosphere spatial decorrelation. Both station-pair and time-step methods are used to assess the standard deviation of vertical (or zenith) ionospheric gradients (σ vig ). The station-pair method compares the simultaneous zenith delays from two different reference stations to a single satellite and observes the difference in delay across the known ionosphere pierce point separation. Because these ionosphere pierce point separations limit the observability of the station-pair method, the time-step method is also used to better understand ionospheric gradients at short distance scales (10-40 km). The time-step method compares the ionospheric delay of a single line of sight at one epoch with the delay for the same line of sight at another epoch a short time (a few to tens of minutes) later. This method has the advantage of removing interfrequency bias calibration errors on different satellites and receivers while possibly introducing an estimation error due to temporal ionospheric gradients. The results of this study demonstrate that typical values of σ vig are on the order of 1-3 mm/km for nonstormy ionospheric conditions. As a result, σ vig of 4 mm/km is conservative enough to bound ionosphere spatial decorrelation for nominal days and still leave enough margin for more active days and for non-Gaussian tail behavior.

Assimilation of radio sounding measurements — Global Navigation Satellite System (GNSS) data, ionosondes, beacon satellites, and more — of the ionosphere has been ongoing for over a decade, and has enabled ionospheric plasma mapping at global scales and at unprecedented resolution. Data assimilative technology has allowed us to sense and map the redistribution of plasma in response to stormtime forcing, in the form of storm-enhanced densities (SED), plumes of plasma feeding into the plasmasphere, bubbles, depletions and troughs. However, the drivers behind these formations have had to be measured in situ or indirectly, and with greater sparsity than measurements such as total electron content (TEC) from GNSS.

Variations in plasma density in the ionospheric layer can cause fluctuations in the received amplitude or phase of a trans-ionospherically propagating radio wave. Such fluctuations are termed scintillation, and can lead to loss of continuity in radio-navigation for users of Global Navigation Satellite Systems (GNSS) such as GPS, Beidou, GLONASS, and Galileo. A scintillating signal can cause a user's receiver to lose lock on one or more satellites at a given instant. These satellites are then temporarily unavailable for position, navigation and timing estimates, reducing accuracy. Scintillation is observed most frequently in the equatorial and auroral zones. In these regions, different physical mechanisms are responsible for the formation of ionospheric irregularities that cause scintillation.

[1] Extreme ionospheric anomalies occurring during severe ionospheric activity can pose an integrity threat to users of Global Navigation Satellite System (GNSS) Ground Based Augmentation Systems (GBAS). While most very large spatial gradients in slant ionospheric delay were observed on high-elevation satellites, several extreme gradients were also observed on satellites below 15 degrees elevation. This paper details the study of anomalous ionospheric spatial gradients for low-elevation satellites observed from the 20 November 2003 geomagnetic storm in the Conterminous United States (CONUS). As viewed by a cluster of Continuously Operating Reference Stations (CORS) receivers in northern Ohio, SVN 26 came into view around 20:30 Universal Time (UT) on this day, rose to an elevation angle of about 15 degrees, and set around 22:00 UT. A spatial gradient of 360 mm/km was discovered at 21:20 UT between CORS stations GARF and WOOS, when SVN 26 was at 11 degrees elevation. Ionospheric delay measurements are vulnerable to semi-codeless L2 tracking errors and data post-processing errors, especially when satellites are at low elevation. This paper presents a series of methods to validate observed ionospheric anomaly events using station-wide checks, satellite-wide checks, and manual verification with single-frequency measurements. Spatial gradients discovered at other station pairs and another low elevation satellite with a similar azimuth angle, SVN 29, support that the event of SVN 26 is an ionospheric anomaly as opposed to a receiver fault.

[1] The inversion algorithm for Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE) has been created to gain insight into ionospheric dynamics, particularly when direct measurement is unavailable. We extend the capabilities of EMPIRE here, in order to demonstrate its effectiveness on densities obtained from real data. We apply this method to real Ionospheric Data-Assimilation 4-Dimensional (IDA4D) data from storm time measurements, focusing on the midlatitude F2 layer. EMPIRE is used to estimate midlatitude field-aligned and field-perpendicular drifts. The estimated upward drifts from EMPIRE are validated against measurements obtained from the Millstone Hill incoherent scatter radar zenith antenna. The horizontal × drifts are compared to the assimilative mapping of ionospheric electrodynamics (AMIE) model, which estimates drifts from data sources independent of those used in IDA4D. Results show that the direction and magnitude of the × drifts (and therefore the electric fields) may be deduced from imaging based primarily on total electron content data, although altitude variation is not significantly discernible. We also indicate that the initial uplift of the storm-enhanced density may have been more strongly influenced by field-aligned contributions of neutral winds and diffusion than the electric fields.

[1] We develop a new inversion algorithm for Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE). The EMPIRE method uses four-dimensional images of global electron density to estimate the field-aligned neutral wind ionospheric driver when direct measurement is not available. We begin with a model of the electron continuity equation that includes production and loss rate estimates, as well as E x B drift, gravity, and diffusion effects. We use ion, electron, and neutral species temperatures and neutral densities from the Thermosphere Ionosphere Mesosphere Electrodynamics General Circulation Model (TIMEGCM-ASPEN) for estimating the magnitude of these effects. We then model the neutral wind as a power series at a given longitude for a range of latitudes and altitudes. As a test of our algorithm, we have input TIMEGCM electron densities to our algorithm. The model of the neutral wind is computed at hourly intervals and validated by comparing to the "true" TIMEGCM neutral wind fields. We show results for a storm day: 10 November 2004. The agreement between the winds derived from EMPIRE versus the TIMEGCM "true" winds appears to be time-dependent for the day under consideration. This may indicate that the diurnal variation in certain driving processes impacts the accuracy of our neutral wind model. Despite the potential temporal and spatial limits on accuracy, estimating neutral wind speed from measured electron density fields via our algorithm shows great promise as a complement to the more sparse radar and satellite measurements.

[1] We present terrestrial and space-based dual-frequency observations of a region of enhanced total electron content (TEC) over the southeastern United States at local nighttime during the geomagnetic storm of 29–31 October 2003. The apparently localized, large-amplitude, and nearly Earth-fixed midlatitude ionosphere disturbance contained about 10 m higher delay at Global Positioning System (GPS) L1 frequency than the nighttime background ionosphere TEC. Using the dual-frequency altimeter on board the Jason satellite, we show evidence that nearly all of the electron content was below its orbital altitude of 1300 km at 0000 local time on 31 October 2003. Dual frequency GPS measurements from the receiver on board the SAC-C satellite indicate that some portion of the electron content existed above the 700 km orbit altitude of SAC-C. We develop a horizontally piecewise constant regional model of the enhancement. We compare the model prediction of TEC with the SAC-C satellite GPS data to constrain the altitude of this enhanced TEC region. Our model indicates that the peak density of the anomalous region is at slightly higher altitude and greater in amplitude than that of the background. The TEC enhancement provides a concrete case study of an extreme scenario that both space-based and ground-based GPS augmentation systems must take into account in order to offer high-accuracy, high-integrity corrections to GPS for safety-of-life applications.

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) will detect variations in GNSS signals reflected from ocean surfaces for estimation of surface wind speeds, planned for launch in 2016. The CYGNSS data product, a Delay Doppler Map (DDM), will contain atmospheric propagation errors. In this work, we present a mathematical model to characterize the ionospheric and tropospheric effects. The measurement model represents pseudoranges from the direct and the reflected signals as a sum of path lengths, atmospheric refraction effects, and noise. From the model we can address two different ionospheric problems: one in which the ionospheric electron content is a potential secondary science product, and alternatively, one in which the ionosphere is a nuisance parameter. For these two objectives, respectively, we present an estimation of relative electron content using GNSS reflectometry, and simulation of typical ionospheric delay errors that will remain in the DDM. Distinct configurations for the geometric term are shown in order to analyze the accuracy of the estimated ionospheric delay. To account for adequate buffer in delay for open-loop tracking, we perform simulations for typical ionospheric delay error from CYGNSS against three main factors: satellite elevation angle, latitude, and local time.