TEC

About: TEC is a research topic. Over the lifetime, 5119 publications have been published within this topic receiving 84696 citations.

Ionospheric storms—A challenge for empirical forecast of the total electron content

Abstract: Since the last decades, the functioning of society depends more and more on well-functioning communication and navigation systems. As the availability and reliability of most of these satellite-based systems can be severely impacted by ionospheric storms, the accurate forecast of these events becomes a required task for mitigating social and economic risks. Here we aim to make initial steps toward an empirical model for ionospheric perturbations related to space weather events that are observable in the total electron content (TEC). The perturbation TEC forecast model will be a fast and robust approach, improving TEC forecasts based on climatological models during storm conditions. The derivation of such a model is a challenging task, because although a general dependence of the storm features (enhancement or depletion of electron density) on the storm onset time, local time, season and geomagnetic latitude is well known, there is a large deviation from the mean behavior. For a better understanding of storm conditions, this paper presents analyses of ionospheric storms observed in the TEC, broken down into diverse classes of storms. It provides a detailed characterization of the typical ionospheric storm behavior over Europe from high to midlatitudes, beyond case studies. Generally, the typical clear strong TEC enhancement starting in high latitudes and propagating equatorward is found to be strongest for storms starting in the morning hours independent of the season. In midlatitudes, it is strongest during noon. In addition, a clear difference between summer and winter storms is reported. While only winter storms develop high-latitude TEC enhancements, only summer storms typically exhibit TEC depletions during the storm recovery phase. During winter storms TEC enhancements can also occur the day following the storm onset, in contrast to summer storms. Strong correlation of TEC perturbation amplitudes to the Bz component of the interplanetary magnetic field and to a proxy of the polar cap potential are shown especially for summer midlatitude TEC enhancements during storms with and onset in the morning hours (6 to 12 UT over Europe) and for winter high-latitude TEC enhancements (around 60∘N). The results indicate the potential to derive improved predictions of maximum TEC deviations during space weather events, based on solar wind measurements.

Ionospheric total electron content and slab thickness determined in Australia

Abstract: Ionospheric total electron content (TEC) and slab thickness (τ) have been determined for southern Australia from July 1991 to June 1995 using Global Positioning System (GPS) satellite reception and ionograms (at 5-min intervals) recorded at Salisbury, South Australia, and other Australian ionograms of the Ionospheric Prediction Service Radio and Space Services, Australian Department of Administrative Services. Seasonal, diurnal, and latitudinal variations in TEC and slab thickness are investigated. The removal of possible error sources in GPS measurements such as satellite and receiver biases is considered. Preliminary procedures are outlined in which protonospheric electron content is separated from GPS TEC measurements (up to 20,000 km height) by subtracting Navy Navigation Satellite System (NNSS) measurements (up to 1000 km height). Although slab thickness is substantially constant, there is a trend for increased values at times of reduced solar influence, such as when approaching sunspot minimum. A preliminary report is given of an extension of the current work at Salisbury by using Australian Surveying and Land Information Group GPS receivers at other Australian locations. A comparison is made between the experimental data and values derived from ionospheric models. The two models considered are the international reference ionosphere model (IRI90) and the parameterized ionospheric model ((PIM) version 1.4, February 1996). The GPS TEC measurements near solar minimum were consistently of the order of 5–10 TEC units greater than the model TEC predictions. This difference is attributed to the inclusion of protonospheric TEC in the GPS measurements but not in the model predictions.

A Physics-Based Improved Cauer-Type Thermal Equivalent Circuit for IGBT Modules

Abstract: A physics-based Cauer-type thermal equivalent circuit (TEC) can be constructed for an insulated-gate bipolar transistor (IGBT) module based on its geometry. In the conventional Cauer-type TEC, each layer of the IGBT module is modeled as a lump with the uniformly distributed temperature. However, this method oversimplified the transient thermal behavior of the IGBT module, leading to unsatisfactory transient junction temperature estimation. Based on a new concept of lumped-capacitance approximation error, this letter proposes a method to determine the number of sublayers that a layer in an IGBT module should be subdivided. For the bulky-baseplate layer, an analytical expression of its thermal impedance is derived and simplified to a first-order transfer function, which can be represented by a thermal resistance and capacitance pair in the TEC. The proposed Cauer-type TEC model is much more accurate than the conventional Cauer-type TEC model for the transient junction temperature estimation of IGBT modules with a slightly increased order only. The improvement of the proposed model over the conventional Cauer-type TEC model is validated by comparing with a finite element analysis model for a commercial IGBT module using simulation studies.

The Comparison of Predicting Storm-time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq

Abstract: Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.