| 1. |
- Menietti, J. D., et al.
(författare)
-
Survey of Saturn Z-mode emission
- 2015
-
Ingår i: Journal of Geophysical Research - Space Physics. - 2169-9380 .- 2169-9402. ; 120:8, s. 6176-6187
-
Tidskriftsartikel (refereegranskat)abstract
- Because of the role of Z-mode emission in the diffusive scattering and resonant acceleration of electrons, we conduct a survey of intensity in the Saturn inner magnetosphere. Z mode is primarily observed as 5kHz narrowband emission in the lower density regions where the ratio of cyclotron to plasma frequency, f(c)/f(p)>1 to which we limit this study. This occurs at Saturn along the inner edge of the Enceladus torus near the equator and at higher latitudes. We present profiles and parametric fits of intensity as a function of frequency, radius, latitude, and local time. The magnetic field intensity levels are lower than chorus, but the electric field intensities are comparable. We conclude that Z-mode wave-particle interactions may make a significant contribution to electron acceleration in the inner magnetosphere of Saturn, supplementing acceleration produced by chorus emission.
|
|
| 2. |
- Branduardi-Raymont, G., et al.
(författare)
-
Exploring solar-terrestrial interactions via multiple imaging observers
- 2022
-
Ingår i: Experimental astronomy. - : Springer Nature. - 0922-6435 .- 1572-9508. ; 54:2-3, s. 361-390
-
Tidskriftsartikel (refereegranskat)abstract
- How does solar wind energy flow through the Earth's magnetosphere, how is it converted and distributed? is the question we want to address. We need to understand how geomagnetic storms and substorms start and grow, not just as a matter of scientific curiosity, but to address a clear and pressing practical problem: space weather, which can influence the performance and reliability of our technological systems, in space and on the ground, and can endanger human life and health. Much knowledge has already been acquired over the past decades, particularly by making use of multiple spacecraft measuring conditions in situ, but the infant stage of space weather forecasting demonstrates that we still have a vast amount of learning to do. A novel global approach is now being taken by a number of space imaging missions which are under development and the first tantalising results of their exploration will be available in the next decade. In this White Paper, submitted to ESA in response to the Voyage 2050 Call, we propose the next step in the quest for a complete understanding of how the Sun controls the Earth's plasma environment: a tomographic imaging approach comprising two spacecraft in highly inclined polar orbits, enabling global imaging of magnetopause and cusps in soft X-rays, of auroral regions in FUV, of plasmasphere and ring current in EUV and ENA (Energetic Neutral Atoms), alongside in situ measurements. Such a mission, encompassing the variety of physical processes determining the conditions of geospace, will be crucial on the way to achieving scientific closure on the question of solar-terrestrial interactions.
|
|
| 3. |
- Liemohn, Michael W., et al.
(författare)
-
Model Evaluation Guidelines for Geomagnetic Index Predictions
- 2018
-
Ingår i: Space Weather. - 1542-7390. ; 16:12, s. 2079-2102
-
Tidskriftsartikel (refereegranskat)abstract
- Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.
|
|
