Deciphering the Solar Wind at Saturn

2020 ◽  
Author(s):  
Wayne Gould ◽  
Licia Ray ◽  
Chris S. Arridge

<p>The effects of the solar wind on Saturn’s magnetosphere are poorly constrained as there are no consistent solar wind monitors upstream of the planet. This has limited previous studies of the solar wind’s influence on the Saturnian magnetosphere to case studies and time dependant analyses of intervals of the Cassini data. While useful and enlightening, these methods assume a priori, a relationship between the solar wind and magnetospheric driving or are biased due to their selection based on particular events detected within the magnetosphere.  </p> <p> </p> <p>Mutual information is a measure of information gain and is measured by the change in uncertainty, after the reception of an input variable in relation to a related output variable. The more mutual information in a system between two variables, the stronger the relationship between the two. We apply Mutual Information Theory to investigate the statistical relationship between solar wind parameters e.g. density, magnetic field strength, velocity, and magnetospheric driving. We consider the entire Cassini dataset, identifying intervals where the Tao et al. [2005] solar wind propagation model is valid. This robust statistical analysis determines magnetospheric proxies for the solar wind and, crucially, how much information these proxies provide about the state of the solar wind. Finding and confirming the relation of these indirect proxies to solar wind propagation models presents the opportunity to open long time scale data to interpretation with respect to the solar wind’s behaviour at the outer planets, using data from past missions. Initial results indicate that the direction of the IMF plays a stronger role in driving Saturn’s magnetosphere than previously thought and identifies potentially new solar wind parameters that effect Saturn’s magnetosphere. </p>

2014 ◽  
Vol 45 (6) ◽  
pp. 868-892 ◽  
Author(s):  
Timothy D. Jones ◽  
Nick A. Chappell

With the aim of quantifying the purely hydrological control on fast water quality dynamics, a modelling approach was used to identify the structure (and dynamic response characteristics or DRCs) of the relationship between rainfall and hydrogen ion (H+) load, with reference to rainfall to streamflow response. Unlike most hydrochemistry studies, the method used makes no a priori assumptions about the complexity of the dynamics (e.g., number of flow-paths), but instead uses objective statistical methods to define these (together with uncertainty analysis). The robust models identified are based on continuous-time transfer functions and demonstrate high simulation efficiency with a constrained uncertainty allowing hydrological interpretation of dominant flow-paths and behaviour of H+ load in four upland headwaters. Identified models demonstrated that the short-term dynamics in H+ concentration were closely associated with the streamflow response, suggesting a dominant hydrological control. The second-order structure identified for the rainfall to streamflow response was also seen as the optimal model for rainfall to H+ load, even given the very dynamic concentration response, possibly indicating the same two flow-paths being responsible for both integrated responses.


2020 ◽  
Author(s):  
Carlos Larrodera ◽  
Consuelo Cid

<p>The main goal of this work is to separate the behavior of the two types of quiet solar wind at 1 AU: fast and slow.<br>Our approach is a bi-Gaussian distribution function, formed by the addition of two Gaussian distribution functions, where each one represents one type of wind. We check our approach by fitting the bi-Gaussian to data from ACE spacecraft. We use level 2 data measured during solar cycles 23 and 24 of different solar wind parameters, including proton speed, proton temperature, density and magnetic field. Our results show that the approach is fine and only transient events departs from the proposed function. Moreover, we can show bi modal behavior of the solar wind at 1 AU, not only for the proton speed, but also for the other analyzed parameters. We also check the solar cycle dependence of the different fitting parameters.</p>


1994 ◽  
Vol 99 (A9) ◽  
pp. 17199 ◽  
Author(s):  
H. Rosenbauer ◽  
M. I. Verigin ◽  
G. A. Kotova ◽  
S. Livi ◽  
A. P. Remizov ◽  
...  

2021 ◽  
Vol 880 (1) ◽  
pp. 012010
Author(s):  
S N A Syed Zafar ◽  
Roslan Umar ◽  
N H Sabri ◽  
M H Jusoh ◽  
A Yoshikawa ◽  
...  

Abstract Short-term earthquake forecasting is impossible due to the seismometer’s limited sensitivity in detecting the generation of micro-fractures prior to an earthquake. Therefore, there is a strong desire for a non-seismological approach, and one of the most established methods is geomagnetic disturbance observation. Previous research shows that disturbances in the ground geomagnetic field serves as a potential precursor for earthquake studies. It was discovered that electromagnetic waves (EM) in the Ultra-Low Frequency (ULF) range are a promising tool for studying the seismomagnetic effect of earthquake precursors. This study used a multiple regression approach to analyse the preliminary study on the relationship between Pc4 (6.7-22 mHz) and Pc5 (1.7-6.7 mHz) ULF magnetic pulsations, solar wind parameters and geomagnetic indices for predicting earthquake precursor signatures in low latitude regions. The ground geomagnetic field was collected from Davao station (7.00° N, 125.40° E), in the Philippines, which experiences nearby earthquake events (Magnitude <5.0, depth <100 km and epicentre distance from magnetometer station <100 km). The Pc5 ULF waves show the highest variance with four solar wind parameters, namely SWS, SWP, IMF-Bz, SIE and geomagnetic indices (SYM/H) prior to an earthquake event based on the regression model value of R2 = 0.1510. Furthermore, the IMF-Bz, SWS, SWP, SWE, and SYM/H were found to be significantly correlated with Pc5 ULF geomagnetic pulsation. This Pc5 ULF magnetic pulsation behaviour in solar winds and geomagnetic storms establishes the possibility of using Pc5 to predict earthquakes.


Author(s):  
YUHUA QIAN ◽  
JIYE LIANG

Based on the intuitionistic knowledge content nature of information gain, the concepts of combination entropy and combination granulation are introduced in rough set theory. The conditional combination entropy and the mutual information are defined and their several useful properties are derived. Furthermore, the relationship between the combination entropy and the combination granulation is established, which can be expressed as CE(R) + CG(R) = 1. All properties of the above concepts are all special instances of those of the concepts in incomplete information systems. These results have a wide variety of applications, such as measuring knowledge content, measuring the significance of an attribute, constructing decision trees and building a heuristic function in a heuristic reduct algorithm in rough set theory.


1998 ◽  
Vol 16 (4) ◽  
pp. 388-396 ◽  
Author(s):  
M. I. Pudovkin ◽  
B. P. Besser ◽  
S. A. Zaitseva

Abstract. A model of the magnetosheath structure proposed in a recent paper from the authors is extended to estimate the magnetopause stand-off distance from solar wind data. For this purpose, the relationship of the magnetopause location to the magnetosheath and solar wind parameters is studied. It is shown that magnetopause erosion may be explained in terms of the magnetosheath magnetic field penetration into the magnetosphere. The coefficient of penetration (the ratio of the magnetospheric magnetic field depression to the intensity of the magnetosheath magnetic field Bm⊥z=–Bmsin2Θ/2, is estimated and found approximately to equal 1. It is shown that having combined a magnetosheath model presented in an earlier paper and the magnetosheath field penetration model presented in this paper, it is possible to predict the magnetopause stand-off distance from solar wind parameters.Key words. Magnetospheric physics · Magnetopause · Cusp and boundary layers-Magnetosheath


2010 ◽  
Vol 115 (A12) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jianpeng Guo ◽  
Xueshang Feng ◽  
Jeffrey M. Forbes ◽  
Jiuhou Lei ◽  
Jie Zhang ◽  
...  

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