She-Ping Shang

Relationship between strong range spread F and ionospheric scintillations observed in Hainan from 2003 to 2007

Data from a DPS-4 Digisonde and an ionospheric scintillation monitor, both located at the low-latitude station Hainan (109.1 degrees E, 19.5 degrees N; dip latitude 9 degrees N), were analyzed to study the strong range spread F (SSF) and its correlation with ionospheric scintillations observed in the period of declining solar cycle 23 from 2003 to 2007. The results show that the maximum and minimum of the occurrence of SSF appeared in nearly the same months as those of the GPS L band scintillations. The variations in SSF occurrence were also similar to those of the scintillations. From 2003 to 2007, both the SSF and the scintillation occurrences decreased from the high solar activity year to the low solar activity year. The correlation coefficient between the occurrences of the SSF and the GPS L band scintillation was as high as 0.93, suggesting associated mechanisms producing SSF and scintillations. Electron density depletions extending from the bottomside to the topside ionosphere are the likely cause explaining the high correlation.

Optimal assimilation for ionospheric weather – Theoretical aspect

Observation, specification and prediction of ionospheric weather are the key scientific pursuits of space physicists, which largely based on an optimal assimilation system. The optimal assimilation system, or commonly called data assimilation system, consists of dynamic process, observation system and optimal estimation procedure. We attempt to give a complete framework in this paper under which the data assimilation procedure carries through. We discuss some crucial issues of data assimilation as follows: modeling a dynamic system for ionospheric weather; state estimation for static or steady system in sense of optimization and likelihood; state and its uncertainty estimation for dynamic process. Meanwhile we also discuss briefly the observability of an observation system; system parameter identification. Some data assimilation procedures existed at present are reviewed in the framework of this paper. As an example, a second order dynamic system is discussed in more detail to illustrate the specific optimal assimilation procedure, ranging from modeling the system, state and its uncertainty calculation, to the quantitatively integration of dynamic law, measurement to significantly reduce the estimation error. The analysis shows that the optimal assimilation model, with mathematical core of optimal estimation, differs from the theoretical, empirical and semi-empirical models in assimilating measured data, being constrained by physical law and being optimized respectively. The data assimilation technique, due to its optimization and integration feature, could obtain better accurate results than those obtained by dynamic process, measurement or their statistical analysis alone. The model based on optimal assimilation meets well with the criterion of the model or algorithm assessment by ‘space weather metrics’. More attention for optimal assimilation procedure creation should be paid to transition matrix finding, which is usually not easy for practical space weather system. High performance computing hardware and software studies should be promoted further so as to meet the requirement of large storage and extensive computation in the optimal estimation. The discussion in this paper is appropriate for the static or steady state or transition process of dynamic system. Many phenomena in space environment are unstable and chaos. So space environment study should include and integrate these two branches of learning.

Distribution of ionospheric O + ion in synchronous altitude region

Based on satellite observation data, using dynamics equation, the ionospheric O+ ion’s distribution in the synchronous altitude region for different geomagnetic activity indexKp is studied by theoretical modeling and numerical analyzing, and semi-empirical models for the O+ ion’s density and flux versus longitude in the synchronous altitude region for differentKp are given. The main results show that in the synchronous altitude region: (i) The O+ ion’s density and flux in day-side are larger than those in nightside. (ii) With longitude changing, the higher the geomagnetic activity indexKpis, the higher the O+ ion’s density and flux, and their variation amplitude will be. The O+ ion’s density and flux whenKp ≥ 6 will be about ten times as great as that whenKp= 0. (iii) WhenKp = 0 orKp ≥ 6, the O+ ion’s density reaches maximum at longitudes 120° and 240° respectively, and minimum in the magnetotail. WhenKp = 3−5, the O+ ion’s density gets to maximum at longitude 0°, and minimum in the magnetotail. However, the O+ ion’s flux reaches maximum at longitude 120° and 240° respectively, and minimum in the magnetotail for anyKp value.

On Property of Scattering Structure in Random Continua

This paper attempt to study scattering property of turbulently advected medium. A new concept, scattering structure, is defined. A cross section is given and discussed. The results revealed that the scattering structure results from Bragg and time sampling actions. Properties of the scattering structure, e.g. scattering efficiency, aspect sensitivity, frequency dependence and discreteness, are analyzed. Some of them are demonstrated by Digital ionosonde measurement. The particle‐behaved scattering structures within resolution volume are the scattering sources of pixels of the radar interferometry image.

Optimal Assimilation for Ionospheric Weather

Observation, specification and prediction of ionospheric weather are the key scientific pursuits of space physicists, which largely based on an optimal assimilation system. The optimal assimilation system, or commonly called data assimilation system, consists of dynamic process, observation system and optimal estimation procedure. We attempt to give a complete framework in this paper under which the data assimilation procedure carries through. We discuss some crucial issues of data assimilation as follows: modeling a dynamic system for ionospheric weather; state estimation for static or steady system in sense of optimization and likelihood; state and its uncertainty estimation for dynamic process. Meanwhile we also discuss briefly the observability of an observation system; system parameter identification. Some data assimilation procedures existed at present are reviewed in the framework of this paper. As an example, a second order dynamic system is discussed in more detail to illustrate the specific optimal assimilation procedure, ranging from modeling the system, state and its uncertainty calculation, to the quantitatively integration of dynamic law, measurement to significantly reduce the estimation error. The analysis shows that the optimal assimilation model, with mathematical core of optimal estimation, differs from the theoretical, empirical and semi-empirical models in assimilating measured data, being constrained by physical law and being optimized respectively. The data assimilation technique, due to its optimization and integration feature, could obtain better accurate results than those obtained by dynamic process, measurement or their statistical analysis alone. The model based on optimal assimilation meets well with the criterion of the model or algorithm assessment by ‘space weather metrics’. More attention for optimal assimilation procedure creation should be paid to transition matrix finding, which is usually not easy for practical space weather system. High performance computing hardware and software studies should be promoted further so as to meet the requirement of large storage and extensive computation in the optimal estimation. The discussion in this paper is appropriate for the static or steady state or transition process of dynamic system. Many phenomena in space environment are unstable and chaos. So space environment study should include and integrate these two branches of learning.