Global Assimilation Of Ionospheric Measurements Research Articles

Variable Responses of Equatorial Ionosphere During Undershielding and Overshielding Conditions

AbstractThis study tests the common assumption that the whole of the equatorial ionospheric F region responds uniformly to both undershielding prompt penetration and overshielding fields. Few recent studies have reported that the bottomside response to geomagnetic storms can differ significantly from that of the topside ionosphere. In this investigation, using vertical drift measurements from Jicamarca incoherent scatter radar in the altitude range of about 200 to 900 km, F region response to the space weather event on 9 November 2004 is studied. It is found that the F region showed large altitudinal variations in vertical drifts only under the influence of overshielding field. Such variations were negligible during undershielding prompt penetration conditions. In a first of its kind of study, direct observations of continuous altitudinal variations in vertical plasma drifts have been used to understand the response of F region ionosphere. This differential response to external drivers has a direct impact on the plasma transportations in the ionosphere and thus on estimating satellite drags. The observations are compared with the widely used ionosphere/thermosphere models: Thermosphere Ionosphere Electrodynamics General Circulation Model and Utah State University Global Assimilation of Ionospheric Measurements. It is found that contrary to observations, the Thermosphere Ionosphere Electrodynamics General Circulation Model shows very small altitudinal gradients in vertical drifts during both undershielding and overshielding conditions. Also, significant difference in the electron transportation especially at the bottomside between two models is adverted. Based on the observations, possible underlying physical processes of asymmetric interhemispheric potentials, different scale sizes of R1 and R2 current systems and different propagation modes for penetrating undershielding and overshielding fields are proposed.

Evaluation of the performance of ionospheric models at solar maximum using COSMIC slant TEC measurements

AbstractWe report the results of a model validation study that assessed how well several ionospheric models captured the slant total electron content, especially at low latitudes near the equatorial ionization anomaly, where horizontal and vertical density gradients are large. We assessed NeQuick, IRI‐2007, IRI‐2012, SAMI‐3, and the Utah State University version of the Global Assimilation of Ionospheric Measurements (GAIM) model. We used slant total electron content measurements made by the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) constellation during 5 May to 20 June 2012 to test GAIM, NeQuick, IRI‐2007, and IRI‐2012 and during 1 October 2011 to 31 December 2011 to test SAMI‐3, as the SAMI‐3 model runs were not available for the 2012 time frame. We found that the GAIM data assimilation model showed the lowest biases, although all of the models typically agreed with the COSMIC measurements to ~8% in the worst case. One area of concern with all of the models was that the mean percentage difference between the COSMIC measurements and the calculated total electron content (TEC) showed significant scatter, >15% at the 1 sigma level; this was attributed to all of the models not capturing the density gradients near the equatorial ionization anomaly (EIA). All of the models underestimated the topside electron density and thus also the ionospheric slab thickness. Since ionospheric models are often validated using near‐vertical TEC measurements and the vertical TEC is the product of the electron density at the F region peak and the slab thickness, our results suggest that the peak density values in the models may be too high.

Regional 3‐D ionospheric electron density specification on the basis of data assimilation of ground‐based GNSS and radio occultation data

AbstractIn this paper, a regional 3‐D ionospheric electron density specification over China and adjacent areas (70°E–140°E in longitude, 15°N–55°N in latitude, and 100–900 km in altitude) is developed on the basis of data assimilation technique. The International Reference Ionosphere (IRI) is used as a background model, and a three‐dimensional variational technique is used to assimilate both the ground‐based Global Navigation Satellite System (GNSS) observations from the Crustal Movement Observation Network of China (CMONOC) and International GNSS Service (IGS) and the ionospheric radio occultation (RO) data from FORMOSAT‐3/COSMIC (F3/C) satellites. The regional 3‐D gridded ionospheric electron densities can be generated with temporal resolution of 5 min in universal time, horizontal resolution of 2° × 2° in latitude and longitude, and vertical resolution of 20 km between 100 and 500 km and 50 km between 500 and 900 km. The data assimilation results are validated through extensive comparison with several sources of electron density information, including (1) ionospheric total electron content (TEC); (2) Abel‐retrieved F3/C electron density profiles (EDPs); (3) ionosonde foF2 and bottomside EDPs; and (4) the Utah State University Global Assimilation of Ionospheric Measurements (USU‐GAIM) under both geomagnetic quiet and disturbed conditions. The validation results show that the data assimilation procedure pushes the climatological IRI model toward the observation, and a general accuracy improvement of 15–30% can be expected. Thecomparisons also indicate that the data assimilation results are more close to the Center for Orbit Determination of Europe (CODE) TEC and Madrigal TEC products than USU‐GAIM. These initial results might demonstrate the effectiveness of the data assimilation technique in improving specification of local ionospheric morphology.

Magnetic meridional winds in the thermosphere obtained from Global Assimilation of Ionospheric Measurements (GAIM) model

AbstractThermospheric neutral winds play an important part in the dynamics of ionospheric plasma and represent one of the key inputs for ionospheric physics‐based models. Yet wind measurements are scarce and generally lack global coverage and continuity. To help mitigate this shortcoming, a data assimilation model was used to estimate neutral winds in the low‐ and middle‐latitude thermosphere. Seasonal global maps of NmF2 and hmF2 were generated from Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation measurements for geomagnetically quiet and low solar flux conditions. The maps were assimilated into the Utah State University Global Assimilation of Ionospheric Measurements–Full Physics (GAIM‐FP) model. GAIM‐FP, which uses the physics‐based ionosphere‐plasmasphere model (IPM) and employs an ensemble Kalman filter technique, significantly improved the agreement between the modeled and measured NmF2 and hmF2 globally compared to the IPM. Global quiet time magnetic meridional winds were derived for December and June solstices and March equinox. The morphology of the derived winds was analyzed and validated by comparing with ground‐based measurements and with wind values from the empirical horizontal wind model. GAIM‐FP‐estimated winds were shown to be in good agreement with the wind observations. Furthermore, the sensitivity of the derived winds to uncertain parameters, including the O+‐O collision frequency, neutral composition, number of radio occultations, and data errors, was investigated. The uncertainties were found to have only small effects on the derived winds. The results of this work indicate that thermospheric wind estimation from GAIM‐FP is a valuable tool for wind specification over regions where limited or no wind measurements exist.

Problems associated with uncertain parameters and missing physics for long‐term ionosphere‐thermosphere forecasting

Data assimilation models like the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) models use physics‐based models of the ionosphere, ionosphere‐plasmasphere, or thermosphere and a Kalman filter as a basis for assimilating a diverse set of measurements. With a sufficient amount of data and with multiple data types, the data assimilation models can provide reliable specifications and near‐term forecasts. However, for long‐term forecasts (5 days or longer) stand‐alone or coupled physics‐based models are needed. Unfortunately, the various physics‐based models contain several uncertain parameters and processes as well as missing physics. Further complications arise for coupled physics‐based models because of coupling issues and error propagation from model to model. Some of the problems are associated with the magnetosphere and lower atmosphere drivers, the adopted set of physics‐based equations, the parameterization of physical processes, the values adopted for the transport coefficients, the numerical techniques used, the spatial and temporal resolutions adopted, and the uncertainties in the initial and boundary conditions. Examples of the type of problems the space weather community faces in its attempt at long‐term ionosphere‐thermosphere forecasting are given.

Typhoon Melor and ionospheric weather in the Asian sector: A case study

The Space Environment Group of the National Institute of Information and Communications Technology, Tokyo, Japan, operates four ionosondes at Okinawa, Yamagawa, Kokubunjii, and Wakkanai in the Asian sector. Okinawa is located at the lowest latitude and lies at the northern edge of the northern equatorial anomaly, while Wakkanai is located at the higher latitudes of the midlatitude ionosphere. For this study, ionograms obtained from the Internet are analyzed using the automated Expert System for Ionogram Reduction (ESIR). An anomalous 3 day foF2enhancement observed by the Wakkanai ionosonde from 9 to 11 October 2009 forms the basis for this study. The scientific question being addressed pertains to the remarkably quiescent geomagnetic activity experienced during the extended solar minimum between cycles 23 and 24 that enables a search for the ionospheric response to weather in the lower atmosphere. The analysis of the ionograms from these four stations using the proprietary ESIR technique provides an extended database of electron density profiles that describes the ionospheric variability as a function of latitude, local time, and season. In addition, independent observations of the ionospheric TEC used by the USU Global Assimilation of Ionospheric Measurements (GAIM) model verify the anomalous ionospheric behavior as well as establishing its extent. Typical solar minimum conditions were seen during this study, with geomagnetic activity restricted to well‐characterized corotating interaction region (CIR) events. After eliminating geomagnetic and solar disturbances as drivers of the October 2009 anomaly, the presence of Typhoon Melor is suggested as a possible source mechanism for the ionospheric anomaly.

An accurate and efficient algorithm for Faraday rotation corrections for spaceborne microwave radiometers

Faraday rotation changes the polarization plane of linearly polarized microwaves which propagate through the ionosphere. To correct for ionospheric polarization error, it is necessary to have electron density profiles on a global scale that represent the ionosphere in real time. We use raytrace through the combined models of ionospheric conductivity and electron density (ICED), Bent, and Gallagher models (RIBG model) to specify the ionospheric conditions by ingesting the GPS data from observing stations that are as close as possible to the observation time and location of the space system for which the corrections are required. To accurately calculate Faraday rotation corrections, we also utilize the raytrace utility of the RIBG model instead of the normal shell model assumption for the ionosphere. We use WindSat data, which exhibits a wide range of orientations of the raypath and a high data rate of observations, to provide a realistic data set for analysis. The standard single‐shell models at 350 and 400 km are studied along with a new three‐shell model and compared with the raytrace method for computation time and accuracy. We have compared the Faraday results obtained with climatological (International Reference Ionosphere and RIBG) and physics‐based (Global Assimilation of Ionospheric Measurements) ionospheric models. We also study the impact of limitations in the availability of GPS data on the accuracy of the Faraday rotation calculations.

Assimilation of ionosonde profiles into a global ionospheric model

The Utah State University Global Assimilation of Ionospheric Measurements (GAIM) ionospheric model has been run for multiday intervals in November 2008 and February/March 2009, to investigate the model's ability to assimilate plasma frequency profiles provided by Digisondes. Ionosondes are currently the only type of assimilation data that can provide information on the profile below the peak of the F2 layer. Attention has been focused on the Republic of South Africa, which has four Digisondes and thus offers a unique validation environment. The model has been run for multiple assimilation data scenarios, some of which include GPS total electron content (TEC) observations, in order to provide benchmarks for testing the profile assimilation. The Hermanus Digisonde was set aside to provide the ground truth, in particular the values of foF2, hmF2, and the width of the F2 layer. The values of these characteristics were also tested at the other three ionosonde sites, since the data assimilation procedures do not usually reproduce the assimilated data exactly. It was found that assimilation of ionosonde data did not improve the accuracy of the GAIM values of foF2 at Hermanus (or Grahamstown) beyond that provided by the GPS TEC data, but these TEC‐only errors were already relatively small. However, the ionosonde‐only errors were smaller than the relatively large TEC‐only errors for Louisvale and Madimbo. Assimilation of ionosonde data did not provide any significant increases in the accuracy of the model values of hmF2 and width of the F2 layer.

Longitudinal structure in the CHAMP electron densities and their implications for global ionospheric modeling

[1] Detailed examination of the electron densities in the equatorial ionosphere observed by the CHAMP satellite at ∼400 km and mapped for a fixed local time has revealed a 90°-wide longitudinal structure with a maximum to minimum ratio that can exceed two or more. To date, the role of these large density variations has not been considered in either the development or validation of current global assimilative models. The best defined structure has nodes near 60°, 150°, 240°, and 330°, and is variously called the 4-node or wave number 4 structure. Unless a global model includes the physics that leads to the 4-node structure, or has the structure imposed upon it by the assimilation of data that includes the structure, a very significant part of the physics of the equatorial ionosphere will be missing. The version of the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) model currently used by the U.S. Air Force takes the Ionospheric Forecast Model (IFM) as its background ionosphere. The IFM does not produce a 4-node structure. However, USU-GAIM is driven mainly by the assimilation of slant GPS total electron content observations, which could impose a 4-node structure, given sufficient equatorial GPS sites. The UV radiances from the Special Sensor Ultraviolet Spectrographic Imager instrument on DMSP also have a 4-node structure, and this is maintained in the GAIM global specifications. In the absence of sufficient assimilated data to impose a 4-node structure, such a structure could be imposed on the IFM by using a model of the equatorial E × B drift that contains a 4-node structure.

Operational Space Weather Entering a New Era

U.S. operational space weather maturing because of two competing factors. On one hand, directed agency funding at about $1 billion for model development over the past decade has brought modeling maturity to !ve broad Sun-toEarth domains, i.e., Sun, heliosphere, magnetosphere, ionosphere, and thermosphere. On the other hand, agency funding for transitioning these models into operations has been a small fraction of the level provided for model development. is situation has le# implementation of operational space weather largely unfunded and woefully undirected, with the exception of a few U.S. Air Force Weather Agency projects. A new vision needed so that operational space weather can help solve 21st-century challenges. e current paradigm for operational space weather began in the 1990s, when the idea was to build complete systems at single facilities. ese rapid prototyping centers were envisioned as a means for implementing new operational systems, but this path was unsuccessful. Although increased funding for operational space weather would have bene!ted the community, this did not happen, and in the lopsided funding pro!le, operational space weather was forced to develop an unusual but robust architecture. is architecture consists of distributed networks—automated systems of models, data streams, and algorithms at multiple, geographically dispersed facilities linked by operational servers. A central server manages the network and uses a database enabling remote nodes to deposit output data and access input data at their geophysical or measurement cadences and latencies. e emergence of distributed networks represents a paradigm shi# from 1995, when the National Space Weather Program (NSWP) strategic plan was !rst published. At that time, very few models existed that could be even considered for use in operations. Model development increased due to the funding surge, and by 2000 seventy-three models characterizing 15 space environment domains were listed in the NSWP implementation plan (2nd edition). During the past decade some of these models were implemented operationally and became important components of distributed networks. Early examples were the Magnetospheric Spec!cation Model (MSM; 2000), SOLAR2000 (S2K; 2001), and High Accuracy Satellite Drag Model (HASDM; 2004). More recently, systems of coupled models and data streams have been created, including Global Assimilation of Ionospheric Measurements (GAIM; 2006), HakamadaAkasofuFry (HAF; 2007), and Communication Alert and Prediction System (CAPS; 2008). Coupled model/ data systems that are currently being implemented include the JacchiaBowman 2008 (JB2008), the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS), U.S. Geological Survey Dst, ENLIL with Cone, and Ionosphere Operational System (IONS; i.e., the commercial version of GAIM at the Utah Science Technology and Research (USTAR) Center for Space Weather). Of added note that the Space Weather Modeling Framework (SWMF) and the Center for Integrated Space Weather Modeling (CISM) have developed multi domain systems to represent the entire Sunto-Earth environment, in which some of the models have been running for years. e need to test coupled systems existed even without funding, and the lack of funds drove small businesses, universities, and the multiagency Community Coordinated Modeling Center to create innovative, cost-e$ective solutions. Basically, these organizations had to !nd cheaper ways to accomplish testing and operational implementation. e result was the development of distributed networks to close the Technology Readiness Level (TRL) 7–9 gap. TRL the concept used by agencies and companies to assess the maturity of evolving technologies. TRL 7 models and data can operate in stand-alone mode within the context of an operational environment. TRL 8 signi!es the completion of a fully functional system prototype, and TRL 9 describes a fully delivered, operational system. e distributed network systems at TRL 7–9 are linked in a way that allows developers to maintain versioning and proprietary control over their models, permits data streams to be integrated at their own measurement cadence, results in testing %exibility, enables rapid

Plasmasphere and upper ionosphere contributions and corrections during the assimilation of GPS slant TEC

Total electron content (TEC) measurements from ground stations to Global Positioning System (GPS) satellites provide a rich source of information about the Earth's ionosphere. These data comprise a significant part of the typical data set used by various data ingestion and assimilation models of the ionosphere. For example, the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) data assimilation model uses slant TEC, along with various other types of data, to obtain a global reconstruction of the ionosphere. There are presently two different USU GAIM models: the Gauss‐Markov Kalman Filter (GAIM‐GM), which is operational at the NASA Community Coordinated Modeling Center and the Air Force Weather Agency; and the Full‐Physics Kalman Filter (GAIM‐FP), which is presently run for scientific studies. TEC is the integrated electron density along the path from the ground to the GPS satellites, which orbit at approximately 20,200 km. The GAIM‐FP modeled space ranges up to 30,000 km in altitude, so the entire TEC raypath is contained within the model space. Many ionosphere models do not model this entire region. For example, the GAIM‐GM extends up to an altitude of 1400 km. It is necessary to account for the portion of TEC that is not modeled by some means. There are basically two simple techniques that are in common use: (1) correct the measured TEC by using a model of the upper ionosphere and plasmasphere to subtract their contributions, or (2) ignore the effect and assign the full measured TEC to the ionosphere within the assimilation model space. We present the effect of assimilating TEC measurements into the GAIM‐GM using both the techniques mentioned above. It is found that derived quantities such as NmF2 are significantly degraded by ignoring the upper ionospheric contribution to TEC. This degradation is seen at all local times. The effect is most pronounced at night, when the F region densities are small and the upper ionospheric contribution to TEC is relatively large.

International Standards Will Enhance Space Weather Management

In the decade since the National Space Weather Program (NSWP) Implementation Plan (see http://www.nswp.gov/images/nswpip2000.pdf) was drafted and then published (2000), and the NSWP Assessment Committee produced its 2006 follow-on report (http://www.nswp.gov/nswp_acreport0706.pdf), an updated vision has been needed to guide our nation's space environment activities as the decade near solar maximum begins. This vision must include two parts: placing space weather management at the forefront of activities to go beyond the ongoing specification and prediction efforts, and working to create international space environment standards so that space activities are conducted with safety, efficiency, and the maximization of space commercialization.

Driving the TING model with GAIM electron densities: Ionospheric effects on the thermosphere

In order to study the effects of ionospheric plasma densities on the thermosphere, the electron and O+ densities in the Thermosphere Ionosphere Nested Grid (TING) model were replaced by electron densities from the Global Assimilation of Ionospheric Measurements (GAIM) model at pressure level z = 0 (about 210 ∼ 230 km altitude) and above. This GAIM‐derived TING model (G‐TING) was then run for the period 1–4 April 2004. The TING model was also run as a stand‐alone coupled model (S‐TING), without using the GAIM electron densities, for the same period. The resulting thermospheric responses from the two simulations were compared at around the F region peak altitude. There was an extended quiet period during this interval followed by a moderate geomagnetic storm (Kp ∼ 6.0). The ingestion of the GAIM electron densities had little effect on the neutral temperature and composition during the quiet period before the storm, but there were noticeable global effects on the neutral winds, mainly as a result of changes in the ion drag force. During disturbed periods, changed electron densities produced much more significant effects on the global thermosphere. The increased auroral electron densities enhanced the Joule heating rate, in addition to the ion drag, which resulted in significant global changes not only in the neutral winds, but also in the neutral temperature and composition. The results of our study confirm that having correct ionospheric plasma densities is critical to accurately predicting the thermosphere in the coupled thermosphere‐ionosphere model.

Accuracy of USU‐GAIM specifications of foF2 and M(3000)F2 for a worldwide distribution of ionosonde locations

The Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) model has replaced the Parameterized Real‐Time Ionospheric Specification Model (PRISM) for operational use at the Air Force Weather Agency (AFWA). Validation of the model by the Air Force Research Laboratory has therefore passed from the sanity checking stage to investigations of the accuracies and limitations of the model when it is used to generate Operational Space Environment Network Display (OpSEND) products. This paper presents the results of an extensive analysis of the errors in the USU‐GAIM specifications of the key ionogram characteristics foF2 and M(3000)F2 for 21 ionosonde locations distributed around the world, 9 in Australia and 12 in other countries, for September 2006. The ground‐truth observations of foF2 are manually scaled hourly values. The ground‐truth observations of M(3000)F2 are manually scaled hourly values for the Australian ionosondes, and filtered autoscaled values for the other 12 ionosondes. The analysis provides representative latitude‐dependent median errors for both characteristics, together with errors in the values of the maximum usable frequency for the standard 3000 km HF communications circuit. In general, GAIM performs well at all locations. The errors seem to be a function of the availability of data from nearby GPS TEC sites, the accuracy of the program's background ionosphere, and the validity of the assimilated TEC data, rather than of any limitations of the assimilation technique.

Assessing models for ionospheric weather specifications over Australia during the 2004 Climate and Weather of the Sun-Earth-System (CAWSES) campaign

[1] The Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) program is developing assimilation models to specify ionospheric weather. In this study the Gauss Markov Kalman Filter (GMKF) GAIM model was used. The period 20 March through 19 April 2004, which spanned the Climate and Weather of the Sun-Earth-System (CAWSES) first study period, has been extensively studied to validate the performance of the GAIM model. Although the USU-GAIM model has both regional and global capabilities and can assimilate data from a wide variety of ionospheric observations, for this study the GMKF model was run in a global mode using data only from 162 ground-based GPS slant total electron content (TEC) stations and in situ measurements from three satellites. Using measurements from the 11 ionosonde stations of the Australian Department of Defence sounder network as an independent bottomside ground-truth, the International Reference Ionosphere (IRI), Ionospheric Forecast Model (IFM), and GMKF were compared for (1) monthly mean climatology and (2) the day-to-day weather during the 31 day period. A skill score was developed for the day-to-day weather by defining the IRI as the reference model. IFM is found to be a 10% improvement, while the GMKF is 39% more capable to capture weather variability. However, the study also identifies that this global version of GMKF has difficulty around sunrise, during which time the GMKF performance can be poorer than IRI. Excluding this interval from the skill score analysis increases the GMKF ability to track weather to 48%. The use of more data and different data types should further increase the GMKF's ability to capture weather variations.

Validation of ionospheric weather predicted by Global Assimilation of Ionospheric Measurements (GAIM) models

The ability of the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) model to specify the day‐to‐day variability of the ionosphere is assessed by comparison of specified hourly values of foF2 with observations that were not assimilated by the model. If the specifications are to be an improvement over a climatological model with zero average error, the interdecile width of errors of the specifications at each hour must be less than the interdecile width of the observations. It has been found that the median specified values of foF2 for undisturbed days are quite accurate, and that the interdecile width of errors of the specifications is less than that of the observations most of the time for midlatitude sites, provided there is a GPS TEC site sufficiently close to the ionosonde site. The errors in GAIM values of foF2 for Australian locations tend to be larger for larger distances from a GPS site, and for low latitudes. GAIM also tracks the values of foF2 on disturbed days, but tends to be conservative, not reproducing the full extent of the departures of the disturbed values from the background values.

Validation of the Utah State University Global Assimilation of Ionospheric Measurements (GAIM) model predictions of the maximum usable frequency for a 3000 km circuit

The accuracy of the Utah State University Global Assimilation of Ionospheric Measurements (GAIM) monthly median values of the maximum usable frequency for an HF communications circuit has been determined for 3000 km circuits centered on Australian ionosonde locations for March and April 2004. The accuracy is discussed in terms of foF2, M(3000)F2, and the product MUF(3000)F2 = foF2 * M(3000)F2. Ground truth is provided by hourly values of foF2 and M(3000)F2 that have been hand‐scaled from ionograms. In general, the accuracy of the GAIM predictions of foF2 exceeds that for M(3000)F2. Given that M(3000)F2 is a function of the height and shape of the F2 subpeak, it follows that these parameters of the GAIM F2 peak need to be improved. The GAIM errors in foF2 are quite large at Learmonth during the day, even though Learmonth is a digital ionospheric sounding systems (DISS) station that provides real‐time data to GAIM. We interpret these errors in terms of limitations of the current GAIM model. Comparisons of the GAIM errors in the monthly median MUF(3000)F2 with those given by the propagation prediction program Advanced Stand Alone Prediction System (ASAPS) show that the GAIM errors are generally somewhat greater than the ASAPS errors, but we consider GAIM's performance to be creditable given that most of the data available for assimilation is GPS total electron content observations. Overall, the daytime GAIM errors are 3 MHz or 10%, while the nighttime errors are 3 MHz or 16%. On average, the GAIM errors are about 3/2 the ASAPS errors during the day and twice the ASAPS errors at night.

Duration of an ionospheric data assimilation initialization of a coupled thermosphere‐ionosphere model

Initial conditions provide a critical input for accurate numerical forecast models in meteorology and oceanography. In this paper, we address this problem in space weather forecast models of the thermosphere‐ionosphere system by using the electron densities from the Global Assimilation of Ionospheric Measurements (GAIM) model to initialize the ionospheric part of the Thermosphere Ionosphere Nested Grid (TING) model. The electron densities from the GAIM‐initialized TING model (G‐TING) are compared with the output from the stand‐alone TING model (S‐TING) for geomagnetically quiet and disturbed times in the early April 2004 period in order to observe how long the effects of the initialization would last. Our study shows that the e‐folding time of the initialization is about 2 ∼ 3 hours for most conditions, although this result would probably be different if the initialization for the thermosphere is also included. However, this relaxation time displays significant variations with latitude, local time, and height, and it may also depend on the initial electron density differences between G‐TING and S‐TING. Furthermore, positive (G‐TING > S‐TING) and negative (G‐TING < S‐TING) density differences have different time durations of the initialization effects. Our study also indicates that there is little variation of the relaxation time with the geomagnetic activity despite the impact of geomagnetic storms on the thermosphere‐ionosphere system.

Utah State University Global Assimilation of Ionospheric Measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation

The Utah State University Gauss‐Markov Kalman Filter (GMKF) was developed as part of the Global Assimilation of Ionospheric Measurements (GAIM) program. The GMKF uses a physics‐based model of the ionosphere and a Gauss‐Markov Kalman filter as a basis for assimilating a diverse set of real‐time (or near real‐time) observations. The physics‐based model is the Ionospheric Forecast Model (IFM), which accounts for five ion species and covers the E region, F region, and the topside from 90 to 1400 km altitude. Within the GMKF, the IFM derived ionospheric densities constitute a background density field on which perturbations are superimposed based on the available data and their errors. In the current configuration, the GMKF assimilates slant total electron content (TEC) from a variable number of global positioning satellite (GPS) ground sites, bottomside electron density (Ne) profiles from a variable number of ionosondes, in situ Ne from four Defense Meteorological Satellite Program (DMSP) satellites, and nighttime line‐of‐sight ultraviolet (UV) radiances measured by satellites. To test the GMKF for real‐time operations and to validate its ionospheric density specifications, we have tested the model performance for a variety of geophysical conditions. During these model runs various combination of data types and data quantities were assimilated. To simulate real‐time operations, the model ran continuously and automatically and produced three‐dimensional global electron density distributions in 15 min increments. In this paper we will describe the Gauss‐Markov Kalman filter model and present results of our validation study, with an emphasis on comparisons with independent observations.

Extreme longitudinal variability of plasma structuring in the equatorial ionosphere on a magnetically quiet equinoctial day

We investigate the extreme longitudinal variability of equatorial scintillation under quiet magnetic conditions during 22–23 March 2002. Scintillation Network Decision Aid (SCINDA) observations show intense activity in the South American–Atlantic sector during local evening hours, whereas an absence of scintillation is seen in the far east Asian sector. Ground‐ and space‐based measurements from SCINDA, the Global Ultraviolet Imager (GUVI), TOPEX, and a chain of GPS receivers are used in combination with the Utah State University Global Assimilation of Ionospheric Measurements (USU‐GAIM) model to explore the relationship between the large‐scale ionization distribution and small‐scale irregularities at low latitudes in both the scintillating and nonscintillating longitude sectors. Our analysis shows that there are significant differences in the evolution of the ionization distributions during the evening hours, which are likely the result of differences in the daytime and postsunset vertical plasma drift in the two sectors. This study demonstrates the importance of USU‐GAIM as a new tool for investigating longitudinal as well as day‐to‐day variability that is observed in the large‐scale distribution of the ionosphere and how this relates to the occurrence of scintillation.