Statistical characterization of ionosphere anomalies and their relationship to space weather events

Wang, Chunming; Rosen, I. G.; Tsurutani, B. T.; Verkhoglyadova, O. P.; Meng, Xing; Mannucci, A. J.

doi:10.1051/swsc/2015046

Cited by 23 publications

(21 citation statements)

References 31 publications

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“…However, the relationship between terrestrial and space weather is not well represented in the literature at this time. A novel approach to assessing ionospheric variability (Wang et al 2016) demonstrates that variability is not always associated with geomagnetically active periods. This may be a signature of lower atmosphere forcing and is deserving of further study.…”

Section: Discussionmentioning

confidence: 99%

Scientific challenges in thermosphere-ionosphere forecasting – conclusions from the October 2014 NASA JPL community workshop

Mannucci

Hagan

Vourlidas

et al. 2016

J. Space Weather Space Clim.

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Interest in forecasting space weather in the thermosphere and ionosphere (T-I) led to a community workshop held at NASA's Jet Propulsion Laboratory in October, 2014. The workshop focus was ''Scientific Challenges in Thermosphere-Ionosphere Forecasting'' to emphasize that forecasting presumes a sufficiently advanced state of scientific knowledge, yet one that is still evolving. The purpose of the workshop, and this topical issue that arose from the workshop, was to discuss research frontiers that will lead to improved space weather forecasts. Three areas are discussed in some detail in this paper: (1) the role of lower atmosphere forcing in the response of the T-I to geomagnetic disturbances; (2) the significant deposition of energy at polar latitudes during geomagnetic disturbances; and (3) recent developments in understanding the propagation of coronal mass ejections through the heliosphere and prospects for forecasting the north-south component of the interplanetary magnetic field (IMF) using observations at the Lagrangian L 5 point. We describe other research presented at the workshop that appears in the topical issue. The possibility of establishing a ''positive feedback loop'' where improved scientific knowledge leads to improved forecasts is described (Siscoe 2006, Space Weather, 4, S01003; Mannucci 2012, Space Weather, 10, S07003).

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Section: Discussionmentioning

confidence: 99%

Scientific challenges in thermosphere-ionosphere forecasting – conclusions from the October 2014 NASA JPL community workshop

Mannucci

Hagan

Vourlidas

et al. 2016

J. Space Weather Space Clim.

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“…Herein, the K j 1 −1 × 1 and K j 2 −1 × 1 scaling vectors φ j 1 −1 (ϕ) and φ j 2 −1 (ϕ) as well as the L j 1 −1 × 1 and L j 2 −1 × 1 wavelet vectors ψ j 1 −1 (ϕ) and ψ j 2 −1 (λ) can be calculated by means of the two-scale relations with L j 1 −1 = K j 1 − K j 1 −1 and L j 2 = K j 2 − K j 2 −1 . The numerical entries of the K j 1 × K j 1 −1 matrix P j 1 and the K j 1 × L j 1 −1 matrix Q j 1 can be taken from Stollnitz et al (1995b) or Zeilhofer (2008); the corresponding entries of the K j 2 × K j 2 −1 matrix P j 2 and the K j 2 × L j 2 −1 matrix Q j 2 are provided by Lyche and Schumaker (2001).…”

Section: Pyramid Algorithmmentioning

confidence: 99%

Authors comments

Goss¹

2019

Preprint

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For more than 2 decades the IGS (International GNSS Service) ionosphere associated analysis centers (IAACs) have provided global maps of the vertical total electron content (VTEC). In general, the representation of a 2-D or 3-D function can be performed by means of a series expansion or by using a discretization technique. While in the latter case, pixels or voxels are usually chosen for a spherical function such as VTEC, for a series expansion spherical harmonics (SH) are primarily used as basis functions. The selection of the best suited approach for ionosphere modeling means a trade-off between the distribution of available data and their possibility of representing ionospheric variations with high resolution and high accuracy.Most of the IAACs generate global ionosphere maps (GIMs) based on SH expansions up to the spectral degree n = 15 and provide them with a spatial resolution of 2.5 • ×5 • with respect to the latitudinal and longitudinal directions, respectively, and a temporal sampling interval of 2 h. In recent years, it has frequently been claimed that the spatial resolution of the VTEC GIMs has to be increased to a spatial resolution of 1 • ×1 • and to a temporal sampling interval of about 15 min. Enhancing the grid resolution means an interpolation of VTEC values for intermediate points but with no further information about variations in the signal. n = 15 in the SH case, for instance, corresponds to a spatial sampling of 12 • × 12 • . Consequently, increasing the grid resolution concurrently requires an extension of the spectral content, i.e., to choose a higher SH degree value than 15.Unlike most of the IAACs, the VTEC modeling approach at Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM) is based on local-izing basis functions, namely tensor products of polynomial and trigonometric B-splines. In this way, not only can data gaps be handled appropriately and sparse normal equation systems be established for the parameter estimation procedure, a multi-scale representation (MSR) can also be set up to determine GIMs of different spectral content directly, by applying the so-called pyramid algorithm, and to perform highly effective data compression techniques. The estimation of the MSR model parameters is finally performed by a Kalman filter driven by near real-time (NRT) GNSS data.Within this paper, we realize the MSR and create multiscale products based on B-spline scaling, wavelet coefficients and VTEC grid values. We compare these products with different final and rapid products from the IAACs, e.g., the SH model from CODE (Berne) and the voxel solution from UPC (Barcelona). In contrast to the abovementioned products, DGFI-TUM's products are based solely on NRT GNSS observations and ultra-rapid orbits. Nevertheless, we can conclude that the DGFI-TUM's high-resolution product ("othg") outperforms all products used within the selected time span of investigation, namely September 2017.

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“…Translating the uncertainty from initialization into probabilistic forecasts using different ensemble techniques (Schunk et al, 2014;Elvidge et al, 2016;Knipp, 2016;Owens et al, 2017) is getting standard. Dependence on initial conditions can be chaotic (as in NWP, e. g., magnetosphere/ionosphere/thermosphere Horton et al, 2001;Mannucci et al, 2016;Wang et al, 2016) or non-chaotic (e.g., CME propagation toward Earth Cash et al, 2015; Lee et al, 2013Lee et al, , 2015Pizzo et al, 2015). In the later case, the accuracy of a prediction will strongly depend on the quality of the underlying initial data, for example the parameters of a CME and the characterization of the solar wind to be passed.…”

Section: Modeling Aspectsmentioning

confidence: 99%

Climate, weather, space weather: model development in an operational context

Folini

2018

J. Space Weather Space Clim.

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-Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of "operational stability" versus "dynamic development" of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between "pure research" and "operational forecast" people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the operational code remains with the forecast provider. The pace of model development reflects operational lead times. The points are illustrated with selected examples, many of which reflect the author's background and personal contacts, notably with the Swiss Weather Service and the Max Planck Institute for Meteorology, Hamburg, Germany. In view of current and future challenges, large collaborations covering a range of expertise are a must À within and across climate, weather, and space weather. To profit from and cope with the rapid progress of computer architectures, supercompute centers must form part of the team.

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Statistical characterization of ionosphere anomalies and their relationship to space weather events

Cited by 23 publications

References 31 publications

Scientific challenges in thermosphere-ionosphere forecasting – conclusions from the October 2014 NASA JPL community workshop

Scientific challenges in thermosphere-ionosphere forecasting – conclusions from the October 2014 NASA JPL community workshop

Authors comments

Climate, weather, space weather: model development in an operational context

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