Takashi Okuzawa

HF-Doppler observations of acoustic waves excited by the Urakawa-Oki earthquake on 21 March 1982

Ionospheric disturbances caused by the Urakawa-Oki earthquake at 0232 UT on 21 March 1982 have been detected by a network of HF-Doppler sounders in central Japan. The HF-Doppler data, together with the seismic data, have been used to formulate a mechanism whereby ionospheric disturbances are produced by an event of relatively small epicentral distance. Comparison of the dynamic spectra of these data has revealed experimentally that the atmosphere acts as a low-pass filter for the upward-propagating acoustic waves. The cut-off periods of this filter are estimated by applying a digital filter technique to the up-down component of the seismograms and are found to be 10 s from the ground up to 156 km and 20 s from 156–195 km. Considering the transfer function of the atmosphere derived from the theory of Pitteway and Hines, the observed result does not contradict the prediction that the atmospheric filter mechanism is mainly attributable to viscosity.

Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm

In order to enhance the reproduction of the recovery phase Dst index of a geomagnetic storm which has been shown by previous studies to be poorly reproduced when compared with the initial and main phases, an artificial neural network with one hidden layer and error back-propagation learning has been developed. Three hourly Dst values before the minimum Dst in the main phase in addition to solar wind data of IMF southward-component Bs, the total strength Bt and the square root of the dynamic pressure,

Horizontal velocity dispersion of medium-scale travelling ionospheric disturbances in the F-region

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A study of the numerical heating in electrostatic particle simulations

, for the minimum Dst, i.e., information on the main phase was used to train the network. Twenty carefully selected storms from 1972–1982 were used for the training, and the performance of the trained network was then tested with three storms of different Dst strengths outside the training data set. Extremely good agreement between the measured Dst and the modeled Dst has been obtained for the recovery phase. The correlation coefficient between the predicted and observed Dst is more than 0.95. The average relative variance is 0.1 or less, which means that more than 90% of the observed Dst variance is predictable in our model. Our neural network model suggests that the minimum Dst of a storm is significant in the storm recovery process.

Characteristics of TEC variations observed at Chofu for geomagnetic storms

Abstract Daytime observations of the horizontal velocity dispersion of medium-scale travelling ionospheric disturbances (TID) near the F2 peak height have been carried out using an array of HF Doppler sounders in central Japan. Cross-correlation analysis of sample records has shown that the horizontal trace velocity is a decreasing function of the period of fluctuations in the range 13.3–40 min. The theoretical dispersion of the atmospheric gravity waves is also calculated using Klostermeyer s (1974) method. Comparison between the observed and the calculated results suggests the possibility that the components of the lower period of the observed velocity dispersion may be a remnant of the quasi-evanescent mode pertinent to lower-height levels.

Consequences of the neural network investigation for Dst-AL relationship

We study the nonphysical increase of kinetic energy observed in electrostatic particle simulations. Quantitative analysis of the numerical heating is of vital importance in obtaining reliable results in simulation studies. The numerical heating caused by electrostatic fields is discussed with an emphasis on a couple of simulation parameters, such as the ratios of the number of particles Np to that of grid points Nx, and Debye length λD to the grid size Δx. In this paper a measure called “heating rate” is used for this purpose, which is defined by the relative magnitude of the kinetic energy increment in unit time to the initial energy. The present numerical experiments have revealed that the heating rate may vary in proportion to (NpNx)-1 and (λDΔx)-3. The heating mechanisms are discussed in terms of nonphysical electric fields as observed in the fluctuation spectra of electric fields.

Global Pc5 caused by a DP 2–type ionospheric current system

Since the middle of 1996, we have made a routine observation of TEC using GPS in Chofu (35.65°N, 139.54°E), Tokyo. We examine in this paper the relationship between the perturbation components of TEC and geomagnetic field variations during 11 storm events for 1997–1998 to clarify the mid-latitude characteristics of TEC variations at the time of magnetic storms. The perturbation components of TEC were derived every 20 seconds by subtracting quiet-time TEC values which are estimated as the average of both 3 days just before and after the storm period. The magnetic field data from Memambetsu Magnetic Observatory (45.92°N, 144.20°E) are used for the identification of the storm-time variations. Our results show that (1) the amplitude level of the TEC variation tends to increase during the first 24 hr of storm and then decrease below its usual-day level with recovery in one or two days later for the typical magnetic storm, and (2) during a negative-value phase of the TEC variation, which follows the initial positive hump structure, the perturbation amplitude of TEC shows a remarkable reduction in summer compared to in winter.

Measurements of temperature and velocity distribution of thermal electrons by the Akebono (EXOS-D) satellite-Experimental setup and preliminary results-

Several recent studies have suggested that most of the Dst main phase variations and of the AL variations similarly respond to a certain type of solar wind condition although the processes are independent of each other. This similarity suggests that some consistency between the Dst main phase development and AL variations exists, regardless of the existence of causality. In what situations this consistent relationship really exists or collapses has been examined with the technique of an Elman recurrent neural network. The network was trained with the Dst and hourly averaged AL indices for 70 storm events from 1967 to 1981, and tested for nine storms that occurred in 1982. The result shows that the Dst-AL relationship can be categorized into two types: high correlative mapping for which 80% and more of the Dst peak in the main phase is reproduced by AL, and partially correlative mapping where only about a half of the Dst peak is reproduced. It is found that whether the correlation is high or partial is determined by whether the Dst main phase develops smoothly or with a discontinuity, i.e., for storms having a discontinuity in the main phase, the coherency collapses. The discontinuity in the Dst main phase is associated with the rapid southward IMF change after the northward excursion. We suggest that it is this IMF variation to which storms and/or substorms respond in a highly complex manner and that such a complex response can be associated with about a half of the maximum ring current intensity.