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We present a metric for detecting clouds in auroral all-sky images based on single-wavelength keograms made with a collocated meridian spectrograph. The coefficient of variation, the ratio of the sample standard deviation to the sample mean taken over viewing angle, is the metric for cloud detection. After calibrating and flat-field correcting keogram data, then excluding dark sky intervals, the effectiveness of the coefficient of variation as a detector is tested compared to true conditions as determined by Advanced Very High Resolution Radiometer (AVHRR) satellite imagery of cloud cover. The cloud mask, an index of cloud cover, is selected at the corresponding nearest time and location to the site of a meridian spectrograph at Poker Flat Research Range (PFRR). We use events that are completely cloud-free or completely cloudy according to AVHRR to compute the false alarm and missed detection statistics for the coefficient of variation of the greenline 557.7 nm emission and of the redline 630.0 nm emission. For training data of the years 2014 and 2016, we find a greenline threshold of 0.51 maximizes the percent of events correctly identified at 75%. When applied to testing data of the years 2015 and 2017, the 0.51 threshold yields an accuracy of 77%. There is a relatively shallow and wide minimum of mislabeled events for thresholds spanning about 0.2 to 0.8. For the same events, the minimum is narrower for the redline, spanning roughly 0.3-0.5, with a threshold of 0.46 maximizing detector accuracy at 78-79%.

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.

The polar vortices play a central role in vertically coupling the Sun-Earth system by facilitating the descent of reactive odd nitrogen (NOx = NO + NO2) produced in the atmosphere by energetic particle precipitation (EPP-NOx). Downward transport of EPP-NOx from the mesosphere-lower thermosphere (MLT) to the stratosphere inside the winter polar vortex is particularly impactful in the wake of prolonged sudden stratospheric warming (SSW) events. This work is motivated by the fact that state-of-the-art global climate models severely underestimate EPP-NOx abundances in the polar MLT. It is not clear whether this deficiency is due to a missing NOx source or to inadequate transport processes. As a step toward understanding the transport pathways by which MLT air enters the top of the polar vortex, we explore the extent to which planetary waves impact the geographic distribution of NO near the polar winter mesopause in the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension combined with data assimilation using the Data Assimilation Research Testbed (WACCMX+DART). We present planetary wave-driven NO patterns near the polar winter mesopause during 16 case studies from the Arctic winters of 2005/2006 through 2018/2019. During all cases the model is in reasonable agreement with Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) derived zonal winds and Solar Occultation For Ice Experiment (SOFIE) and Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) NO measurements. Superposed Epoch Analysis is employed to diagnose typical mesopause planetary wave behavior and vertical transport characteristics during 10 minor and 6 major SSW events. Results show that descent of NO into the top of the polar vortex is enhanced by about a factor of 4 in traveling planetary wave troughs vs. in ridges and that this planetary wave-driven enhanced NO descent occurs during both minor and major SSW events. These results present a new conceptual model of zonally varying, vs. zonally uniform, polar descent in the MLT.

The polar vortices play a central role in vertically coupling the Sun-Earth system by facilitating the descent of reactive odd nitrogen (NOx = NO + NO2) produced in the atmosp...

This paper analyzes the surface reflectivity (SR) over time of GPS L1 signals scattered off a partially frozen lake surface. The SR for a given GPS satellite is compared with the mean red value (MRV) of pixels inside the Fresnel zone taken by collocated cameras, with the MRV serving as a truth reference for water (low MRV) or ice (high MRV). GPS signals are collected during a data campaign at Lake Michigan in Chicago, at a time when the lake surface consisted of a mixture of ice and water. A portable ground-based sensor suite is set up at the lake front to collect both surface-scattered GPS signals and independent validation data (lidar and camera) from the surface. The camera pixel locations and the Fresnel zone of the GPS satellites are mapped into an east-north-up (ENU) coordinate system centered at the reflected antenna. The MRV of the Fresnel zone is computed for each satellite. Satellites whose Fresnel zones sweep across both water and ice over the lake surface are selected. To process the GPS reflected signals of chosen satellites, a software defined receiver is customized. In order to find the SNR, one ms coherent correlation power is summed incoherently for one minute for reflected signals. The distance from the satellites to reflection point (Fresnel zone) and from the reflection point to the reflected antenna are computed in order to estimate the SR. Then the SR is correlated with the mean red value. High correlation is seen between SR and MRV for PRN 26, while for PRN 16 only low-to-moderate correlation is found. The low correlation is attributed to a lack of overlap between the Fresnel zone of PRN 16 and the cameras’ fields of view.

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.

This paper presents estimates of signal-to-noise ratio (SNR) and surface reflectivity (SR) over time of GPS L1 signals scattered off a partially frozen lake surface. A portable ground-based sensor system is used to collect both scattered Global Positioning System (GPS) signals and independent validation data (lidar and camera) from the surface. GPS front-end signals are collected from both a direct receiving antenna facing upward and from a reflection-receiving antenna facing downward. A data campaign is conducted on the Lake Michigan waterfront in Chicago during a period in which the lake surface consisted of a mixture of surface ice and water. The lidar surface scans are mapped with camera images and estimated specular reflection point positions to indicate the surface reflection type and to provide surface height relative to the sensors. A customized software defined receiver processes signals reflected from the lake surface. One ms of coherent integration and 1000 ms of incoherent integration of the reflected signal are used for correlation. The SNR is averaged over one-minute intervals. For two satellites whose reflection points scan across ice and water over time, the SNR and SR are computed over time. The SR is shown to be about 0.5 dB lower for liquid water than lake ice in unambiguous conditions. However, a variable surface type over an area smaller than the scattering zone can reduce the variation in SR. This system concept may be used in the future for more complete mapping of phase changes in the cryosphere.

We show that inter-model variation due to under-constraint by observations impacts the ability to predict material transport in the lower thermosphere. Lagrangian coherent structures (LCSs), indicating regions of maximal separation (or convergence) in a time-varying flow, are derived in the lower thermosphere from models for several space shuttle water vapor plume events. We find that inter-model differences in thermospheric transport manifest in LCSs in a way that is more stringent than mean wind analyses. LCSs defined using horizontal flow fields from the Specified Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (SD-WACCMX) at 109 km altitude are compared to Global Ultraviolet Imager (GUVI) observations of the space shuttle main engine plume. In one case, SD-WACCMX predicts an LCS ridge to produce spreading not found in the observations. LCSs and tracer transport from SD-WACCMX and from data assimilative WACCMX (WACCMX + DART) are compared to each other and to GUVI observations. Differences in the modeled LCSs and tracer positions appear between SD-WACCMX and WACCMX + DART despite the similarity of mean winds. WACCMX + DART produces better tracer transport results for a July 2006 event, but it is unclear which model performs better in terms of LCS ridges. For a February 2010 event, when mean winds differ by up to 50 m/s between the models, differences in LCSs and tracer trajectories are even more severe. Low-pass filtering the winds up to zonal wavenumber 6 reduces but does not eliminate inter-model LCS differences. Inter-model alignment of LCSs improves at a lower 60 km altitude.

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.

The use of ground-based augmentation systems (GBASs) is increasing in the national airspace system and also in many nations to support aircraft precision approaches and landing. An anomalous ionospheric event if undetected can cause a potential threat to users of single-frequency-based global navigation satellite system augmentation systems. Current GBAS utilize the pre-defined “worst case” ionospheric threat model in their computation of user position errors to consider all possible ionospheric conditions. This could lead to an excessive availability penalty by adding conservatism on the resulting error bounds. This paper proposes a methodology of real-time ionospheric threat adaptation that adjusts the ionospheric threat model in real time instead of always using the same threat model. This is done by using predicted values of space weather indices for determining the corresponding threat model based on an established relationship between space weather indices and ionospheric threats. Since space weather prediction itself is not reliable due to prediction errors, an uncertainty model was derived from 17 years of historical data. When applied to Category I GBAS in the Conterminous United States, this method lowered the upper bound of the current threat model about 95% of the time during the 17 years (from 1995 to 2011) using the bounded prediction value of the disturbance-storm time index.

Lagrangian coherent structures (LCSs) in the time-varying flow of the upper atmosphere are found. The Horizontal Wind Model 2014 (HWM14) is used to simulate the neutral winds of the thermosphere at 150, 250, and 350 km altitude, for quiet and active geomagnetic conditions. Then LCS analysis of HWM14 winds at 100 km altitude is used to investigate space shuttle water vapor plume transport. LCSs are found to exist in the lower thermosphere, but are more prominent at 250 km and 350 km, at 40° - 80° latitudes on the dayside. Ionosphere-thermosphere coupling manifests in the response of thermospheric LCSs to geomagnetic activity. The LCSs shift equatorward and eastward, developing a more complex topology in the apparent form of two cells in the northern hemisphere high latitude zone. We also show for the first time that the LCS demarcates the high-latitude boundary of shuttle plume observations after 48 hours of transport.

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.

Numerous characters in Austen’s novels have commissions in the army or militia, and several central characters enlist in the navy. There was an important distinction between these two services. Officers in the army and militia were paid wages. Sailors, on the other hand, were entitled not just to wages but also to a share in the value of any “prizes” that they captured at sea. We theorize that this prize system existed because naval officers and crew needed to be given equity shares in their endeavors in order to create incentives for aggressive action. This distinction between how soldiers and sailors were paid has a profound effect on the development of Austen’s characters. The army is a force for the status quo. The navy, on the other hand, is an avenue for true social advancement. Austen also uses the choice between services to show the character of the individuals who enlist.

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.

Global Navigation Satellite System Reflectometry (GNSS-R) is a form of bi-static radar in which the GNSS signal reflected off a surface can be used to remotely sense the surface from which it reflects. It is a low-cost, all-weather sensing method in which a GNSS receiver specifically measures what would typically be considered multipath rather than rejecting it. This technique has the potential to be extremely useful in monitoring phase changes between water and ice in the crysophere, as the signal scattered power and Doppler shift is function of the surface conditions. The power collected as a function of time delay and Doppler shift is often represented on a Delay-Doppler Map (DDM).

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.

In this paper, a summer data campaign is presented to study the freshwater surface using GNSS signals reflected from Lake Michigan. To analyze the scattered power in terms of surface conditions, verification sensors are collocated with the GNSS receiver. The ground-based mobile sensor suite we have developed consists of: a reflected-GNSS antenna and universal software radio peripheral (USRP) front end; a second (direct) GNSS reference antenna and USRP; lidar; camera; weather station and supporting power and electronics. The mobile sensor suite is set up on a dock on the Lake Michigan shore in Chicago, Illinois. Data are post-processed to acquire GPS L1 signals and to estimate the sensor suite location. The weather station and collocated camera data provide the reference surface temperature and wind speed conditions. Almanac information is used to compute the specular points’ (SP) positions in the east-north-up coordinate system (ENU). To determine specular point position on the surface, a LiDAR-SP map is computed. The SP-LiDAR map helps to find the location of the SP on the water. A delay-Doppler map (DDM) is a representation of the power scattered in time delay and Doppler shift, and depends on surface conditions. A DDM is computed for the satellite whose corresponding SP is located on the water and has a high elevation angle, to minimize direct GPS signal interference. We use an incoherent integration signal processing method to compute the DDM due to the signal noise level in this work. The DDMs for both direct and reflected GPS signals are examined. DDMs generated from the directly received signal correctly show the power concentrated at the code chip and Doppler frequency of the acquired satellite, indicating that the incoherent processing is sound. The sensor suite and processing will be used in future data campaigns for detecting seasonal freshwater and surface ice variations.

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.

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

In this paper, we present the results of a controlled GNSS reflectometry experiment to measure the power scattered from the surface and coordinate the power scatter with the surface from which it reflected by use of a collocated lidar and reference GNSS receiver. The ground-based mobile sensor suite we have developed consists of: a reflected-GNSS antenna and universal software radio peripheral (USRP) front end, a second (direct) GNSS reference antenna and USRP, lidar, and supporting power and electronics. A temporary reflecting pool 1 m in diameter is positioned at the anticipated specular reflection point on the ground. Data are post-processed to acquire GPS L1 signals, estimate the sensor suite, satellite, and specular reflection point positions. We map the lidar point cloud with the specular point positions, and compare the surface sites with the scattered power distribution in the form of a delay Doppler map (DDM). Due to an unanticipated delay in data collection, the field campaign occurs after the specular point has moved off the water surface to surrounding grass. So, as anticipated, the DDM of the reflected signal is produced and shows significant scatter but no signature of reflection from water. Though a DDM from grass rather than water is the most likely result, the experiment is a successful end-to-end test of the data collection and post-processing methods. DDMs generated from the directly received signal correctly show power concentrated at the code chip and Doppler frequency of the acquired satellite, indicated the processing is sound. The sensor suite and processing will be used in future data campaigns for detecting seasonal freshwater and surface ice variations.

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.

This effort presents the development of a multi-sensor suite to analyze GNSS reflected signal characteristics as a function of surface conditions of Lake Michigan. The purpose of this sensor system is ultimately to determine how to use GNSS-R to monitor and distinguish liquid freshwater from lake ice, paving the way for routine remote sensing of seasonal ice formation. The sensor suite integrates disparate data sets, comparing the detection of GNSS-R at an elevated ground-based site to auxiliary data sets on collocated weather and surface conditions, using ground-based lidar and optical camera, in addition to satellite-based remote sensing imagery. Initial tests in the lab are conducted on the universal software radio peripherals (USRPs) that are to serve as the GNSS front-ends for data collection campaigns. The test uses a simulated L1 GPS signal, with a GPS antenna connected to serve as a discipline for the USRP’s oscillator. Initial testing of the lidar is also conducted in the lab. This experiment characterizes the lidar range and intensities from a variety of controlled surfaces mounted 1 m away from the lidar: an empty pan, still water 5 cm in depth, the same water disturbed, and ice. The USRP tests produce the power spectral density of the GPS L1 signal, but the time domain digitized data give a bi-modal rather than Gaussian histogram. Lidar range and intensity data are received from both the ice and water surfaces, with variations that correspond to the medium tested.