Abstract

Abstract. Although the number of terrestrial global navigation satellite system (GNSS) receivers supported by the International GNSS Service (IGS) is rapidly growing, the worldwide rather inhomogeneously distributed observation sites do not allow the generation of high-resolution global ionosphere products. Conversely, with the regionally enormous increase in highly precise GNSS data, the demands on (near) real-time ionosphere products, necessary in many applications such as navigation, are growing very fast. Consequently, many analysis centers accepted the responsibility of generating such products. In this regard, the primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution. The global VTEC representation developed in this work is based on a series expansion in terms of compactly supported B-spline functions, which allow for an appropriate handling of the heterogeneous data distribution, including data gaps. The corresponding series coefficients and additional parameters such as differential code biases of the GNSS satellites and receivers constitute the set of unknown parameters. The Kalman filter (KF), as a popular recursive estimator, allows processing of the data immediately after acquisition and paves the way of sequential (near) real-time estimation of the unknown parameters. To exploit the advantages of the chosen data representation and the estimation procedure, the B-spline model is incorporated into the KF under the consideration of necessary constraints. Based on a preprocessing strategy, the developed approach utilizes hourly batches of GPS and GLONASS observations provided by the IGS data centers with a latency of 1 h in its current realization. Two methods for validation of the results are performed, namely the self consistency analysis and a comparison with Jason-2 altimetry data. The highly promising validation results allow the conclusion that under the investigated conditions our derived near real-time product is of the same accuracy level as the so-called final post-processed products provided by the IGS with a latency of several days or even weeks.

Highlights

  • The ionosphere constitutes the upper part of the atmosphere, extending from approximately 60 to 1500 km above the Earth’s surface, enriched with free electrons and ions (Schaer, 1999)

  • The primary objective of our work is to develop a near real-time processing framework for the estimation of the vertical total electron content (VTEC) of the ionosphere using proper models that are capable of a global representation adapted to the real data distribution

  • A near real-time processing framework that is capable of automated data downloading, data preprocessing, Kalman filtering and formatted product generation is presented to provide VTEC maps as well as satellite and receiver differential code biases (DCBs) of GPS and GLONASS in near real time

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Summary

Introduction

The ionosphere constitutes the upper part of the atmosphere, extending from approximately 60 to 1500 km above the Earth’s surface, enriched with free electrons and ions (Schaer, 1999). The ionospheric plasma density varies with time and location and exhibits a coupled system with its environment: the Sun, the Earth’s lower atmosphere, the thermosphere and the magnetosphere (Heelis, 2014). Interactions between the thermospheric neutral winds and ionized plasma drive the ionospheric charged particles in motion and lead to separation of charges, resulting in the creation of a polarized electrical field E. At high latitudes, the interaction of the Earth’s magnetosphere with the interplanetary magnetic field attached to the solar winds as well as to the space weather events increases the complexity of the system. GNSS offers an attractive alternative to traditional methods, such as ionosondes, for monitoring the electron content within the ionosphere in terms of volume and global data distribution

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