C. F. Minter

Global Assimilation of Ionospheric Measurements (GAIM)

Abstract : Our primary goal is to construct a real-time data assimilation model for the ionosphere-plasmasphere system that will provide reliable specifications and forecasts. A secondary goal is to validate the model for a wide range of geophysical conditions, including different solar cycle, seasonal, storm, and substorm conditions.

US‐TEC: A new data assimilation product from the Space Environment Center characterizing the ionospheric total electron content using real‐time GPS data

[1]xa0The potential of data assimilation for operational numerical weather forecasting has been appreciated for many years. For space weather it is a new path that we are just beginning to explore. With the emergence of satellite constellations and the networks of ground-based observations, sufficient data sources are now available to make the application of data assimilation techniques a viable option. The first space weather product at Space Environment Center (SEC) utilizing data assimilation techniques, US-TEC, was launched as a test operational product in November 2004. US-TEC characterizes the ionospheric total electron content (TEC) over the continental United States (CONUS) every 15 min with about a 15-min latency. US-TEC is based on a Kalman filter data assimilation scheme driven by a ground-based network of real-time GPS stations. The product includes a map of the vertical TEC, an estimate of the uncertainty in the map, and the departure of the TEC from a 10-day average at that particular universal time. In addition, data files are provided for vertical TEC and the line-of-sight electron content to all GPS satellites in view over the CONUS at that time. The information can be used to improve single-frequency GPS positioning by providing more accurate corrections for the ionospheric signal delay, or it can be used to initialize rapid integer ambiguity resolution schemes for dual-frequency GPS systems. Validation of US-TEC indicates an accuracy of the line-of-sight electron content of between 2 and 3 TEC units (1 TECU = 1016 el m−2), equivalent to less than 50 cm signal delay at L1 frequencies, which promises value for GPS users. This is the first step along a path that will likely lead to major improvement in space weather forecasting, paralleling the advances achieved in meteorological weather forecasting.

A comprehensive evaluation of the errors inherent in the use of a two-dimensional shell for modeling the ionosphere

[1]xa0Accurately modeling the ionosphere is a critical component to many radionavigation applications. However, in a significant number of cases, these models assume the ionosphere is compacted into a thin shell surrounding the Earth, rather than a full three-dimensional field. While such models allow for ease of use and small storage needs, they are necessarily lacking in detailed information on the actual three-dimensional distribution of electrons in the ionosphere. This paper attempts to quantify all geometric and numerical errors made through the use of a shell model. Such errors can reach as high as 14% on days of no strong ionosphere activity. Ultimately, this paper concludes that the highest levels of accuracy require the total electron content of the ionosphere be modeled three-dimensionally. However, for those who must continue to use a shell model, a new mapping function has been derived which removes as much as 50% of the total errors seen using the previous, standard mapping function for shell models.

Differential validation of the US-TEC model

[1]xa0This paper presents a validation and accuracy assessment of the total electron content (TEC) from US-TEC, a new product presented by the Space Environment Center over the contiguous United States (CONUS). US-TEC is a real-time operational implementation of the MAGIC code and provides TEC maps every 15 min and the line-of-sight electron content between any point within the CONUS and all GPS satellites in view. Validation of TEC is difficult since there are no absolute or true values of TEC. All methods of obtaining TEC, for instance, from GPS, ocean surface monitors (TOPEX), and lightning detectors (FORTE), have challenges that limit their accuracy. GPS data have interfrequency biases; TOPEX also has biases, and data are collected only over the oceans; and FORTE can eliminate biases, but because of the lower operating frequency, the signals suffer greater bending on the rays. Because of the difficulty in obtaining an absolute unbiased TEC measurement, a “differential” accuracy estimate has been performed. The method relies on the fact that uninterrupted GPS data along a particular receiver-satellite link with no cycle slips are very precise. The phase difference (scaled to TEC units) from one epoch to the next can be determined with an accuracy of less than 0.01 TEC units. This fact can be utilized to estimate the uncertainty in the US-TEC vertical and slant path maps. By integrating through US-TEC inversion maps at two different times, the difference in the slant TEC can be compared with the direct phase difference in the original RINEX data file for nine receivers not used in the US-TEC calculations. The results of this study, for the period of April–September 2004, showed an average root mean square error of 2.4 TEC units, which is equivalent to less than 40 cm of signal delay at the GPS L1 frequency. The accuracy estimates from this “differential” method are similar to the results from a companion paper utilizing an “absolute” validation method by comparing with FORTE data.

Data assimilation for neutral thermospheric species during geomagnetic storms

[1]xa0During a geomagnetic storm, Joule heating heats the neutral gas and drives horizontally divergent winds which force upwelling of the neutral atmosphere. The heavier molecular species N2 and O2, abundant in the lower thermosphere, are transported to high altitude where they increase the loss rate of the F region ionosphere. The “bulge” of enhanced molecular species, or depleted atomic oxygen, is long-lived, returning to equilibrium mainly through the slow process of molecular diffusion. Its longevity, of the order of a day, enables the global wind system to transport the composition disturbance over thousands of kilometers, driven by the combination of quiet and storm-time wind fields. In a stand-alone physical model the formation and subsequent movement of the composition features depend on accurate specification of the spatial and temporal distribution of the Joule heating from the magnetosphere and knowledge of the time-dependent wind fields to define the transport. Neither is sufficiently well known given current observational capability. An alternative approach is to combine the knowledge contained in a physical model with observations of the thermospheric composition. It has been demonstrated that FUV images can provide a reliable estimate of the magnitude and structure of oxygen-depleted regions on the sunlit side of Earth. A Kalman filter data assimilation method has been developed to combine FUV observations with a physical model in order to optimally define the global distribution of neutral thermosphere composition. This distribution is used as one of the important drivers in a model for Global Assimilation of Ionospheric Measurements (GAIM) in order to improve specification and forecast of the response of the ionosphere to geomagnetic storms.

An ensemble‐type Kalman filter for neutral thermospheric composition during geomagnetic storms

[1]xa0Global circulation models (GCMs) for the thermosphere ionosphere system have been in use for more than 20 years. In the beginning the GCMs were run on supercomputers, were expensive to run, and were used mainly to provide insight into the physics of the region and to interpret measurements. Advances in computer technology have made it possible to run GCMs on desktops and to compare their results with real-time or near-real-time measurements. Todays models are capable of reproducing generic geomagnetic storm effects, but modeling specific storms is still a challenge because accurate descriptions of the energy input during storms are not easy to obtain. One way to compensate for the uncertainty in model inputs for a given period is to assimilate measurements into the model results. In this way, meteorologists have been improving their ability to model tropospheric weather for the last few decades. Data assimilation algorithms have seen an explosive growth in the last few years, and the time has come to apply such techniques to the thermospheric storm effects problem. We present results from an ensemble Kalman filter scheme that determines the best estimate of the global height-integrated O/N2 ratio by combining GCM results and uncertainties with measurements and their errors. We describe the differences that result from the application of an ensemble Kalman filter to an externally forced system (neutral chemical composition) versus a system dominated by the initial condition and internal dynamics (tropospheric weather and ocean models). The results demonstrate that an ensemble of 10 members is able to characterize the state covariance matrix with sufficient fidelity to enable the Kalman filter to operate in a stable mode. Some information about the external forcing was extracted from the estimate of the state. The general trend of the forcing was followed by the filter, but departures were present over some periods.

A comparison of Magic and FORTE ionosphere measurements

[1]xa0To date, no formal validation of the new ionosphere nowcast system, United States–Total Electron Content (US-TEC), at the Space Environment Center in Boulder, Colorado, has been published. This paper therefore lays part of the validation groundwork by comparing solutions from Magic, the analysis version of US-TEC, with total electron content (TEC) data from the Fast Onboard Recording of Transient Events (FORTE) satellite. The Magic system uses ground-based GPS observations to reproduce a four-dimensional model of the electron density in the ionosphere. From this model, the TEC between any two points at any time can be obtained. The FORTE satellite, on the other hand, detects the arrival time versus frequency of a broadband signal from a transmitter at Los Alamos. The FORTE-observed group delay provides the TEC along the line of sight between the transmitter and the satellite. These FORTE line-of-sight observations can be compared with TEC values over the same lines of sight in the Magic model. A root-mean-square error (RMSE) calculation statistically compares 178 lines of sight. The RMSE indicates a statistical error of 2.87 total electron content units (1 TECU = 1016 el/m2) between FORTE and Magic, using the current operational GPS station list in US-TEC. How much FORTE and Magic individually contribute to this error remains indeterminable, although the errors are expected to be unique to either system and uncorrelated. Individual contributions of each method to the RMSE are estimated by eliminating observations most affected by raypath bending in FORTE and by varying the number of stations in Magic.

Validation of the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics model: CTIPE‐Mass Spectrometer Incoherent Scatter temperature comparison

[1]xa0New requirements for specification and forecast of the space environment and the availability of unprecedented amounts of real-time data are now driving the development of data assimilation schemes for the thermosphere and ionosphere. Such schemes require accurate knowledge of any biases affecting the models. Finding the biases is not trivial and requires significant effort. Here we present a first step in the validation of a coupled thermosphere ionosphere general circulation model in preparation for its inclusion in a data assimilation scheme. We present a comparison between the Mass Spectrometer Incoherent Scatter (MSIS) radar empirical model neutral temperatures and the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPE) neutral temperature predictions for three solar cycle conditions (F10.7 = 70, 125, and 200), three geomagnetic activity conditions (Kp = 1, 3, and 7), and three seasons (equinox, summer, and winter). The CTIPE model was run for each case with constant inputs until a diurnally reproducible (“steady state”) global temperature pattern was obtained. MSIS predictions were generated for “perpetually constant” equivalent conditions. The temperature comparisons are performed on a 300 km altitude shell. We present global temperature averages, area-weighted root mean square differences, and zonally averaged temperature comparisons. CTIPE temperatures at 300 km altitude are lower than MSIS if Joule heating calculations do not include small-scale E field variability. This is the first global assessment of a general circulation model for the thermosphere over such a wide range of geomagnetic and solar conditions.

Estimating the state of the thermospheric composition using Kalman filtering

[1]xa0To determine the propagation parameters of high-frequency radio waves, an accurate estimate of the ionosphere is desirable. Estimating the ionosphere, especially during geomagnetic storm times, is strongly dependent on perturbations in the neutral composition. Because of this coupling between the ionosphere and neutral atmospheric chemistry, accurate knowledge of the neutral atmospheric composition is critical in estimating the ionosphere. In the research presented here, a data assimilation system is constructed to optimally estimate the neutral composition, and the necessity for implementing an optimized filtering method, like the Kalman filter, is shown. To demonstrate the data assimilation system, an artificial “truth” thermosphere is created using a physical model. This thermosphere is sampled according to an instrument and satellite simulation algorithm, creating the measurement data set. Noise is then added to the measurement data, to represent observation errors. Data are assimilated, and noise from this data is reduced using a Kalman filter in combination with a state propagation model. Results show that the error in the estimate can be greatly reduced (usually to <6%), even if the observation errors are large (15%), by using a Kalman filter. Best results are obtained by using a Kalman filter together with an accurate physical model.