Modeling investigation of ionospheric storm effects over Millstone Hill during August 4–5, 1992

Lei, Jiuhou; Liu, Libo; Wan, Weixing; Zhang, Shun‐Rong

doi:10.1186/bf03352537

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…Zhang et al [20,21] investigated the local time variations of ionospheric drivers such as EUV radiations, neutral compositions, neutral winds, and neutral temperatures, and the correlation of these drivers by assimilating the Incoherent Scatter Radar observations into a middle latitude theoretical model. Lei et al [22] further estimated the ionospheric drivers during the storm and revealed that storm-time disturbed mechanism by the same method.…”

Section: Introductionmentioning

confidence: 96%

Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment

et al. 2008

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On the basis of previous work, we develop a middle and low latitude theoretical ionospheric model in this paper, named Theoretical Ionospheric Model of the Earth in the Institute of Geology and Geophysics, Chinese Academy of Sciences (TIME-IGGCAS). TIME-IGGCAS solves the equations of mass continuity, motion and energy of electron and ions self-consistently and uses an eccentric dipole field approximation to the Earth's magnetic field. We combine the Eulerian and Lagrangian approaches in the model and take account of the plasma E×B drift velocity. Calculation results reveal that the model is steady and credible and can reproduce most large-scale features of ionosphere. By using TIME-IGGCAS, we carried out an observation system data assimilation experiment. Assimilation results show that the E×B drift velocity can be accurately estimated by ingesting the observed foF2 and hmF2 into the model ap-plying nonlinear least-square fit method. We suggest that this work is of great significance in the development of ionospheric data assimilation model to give better nowcast and forecast of ionosphere. ionosphere, theoretical model, data assimilation, nonlinear least-square fit method

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Section: Introductionmentioning

confidence: 96%

Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment

et al. 2008

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“…Mixing of the upper atmosphere accelerates the reduction of the ionosphere due to the atmospheric composition changes (Matsushita, 1959) by upwelling of the molecular-rich air, because atomic oxygen (O) contributes the production and molecular nitrogen (N 2 ) contributes the reduction of the ionosphere (Torr and Torr, 1979). On the other hand, an important mechanism of positive ionospheric storms at mid latitudes is an uplift of the ionosphere due to the upward E × B drift by penetration of magnetospheric electric fields (Martyn, 1953) or due to an equatorward thermospheric wind (Jones and Rishbeth, 1971;Lei et al, 2004). The vertical drift due to electric fields or neutral air winds displaces the equilibrium position of the peak to a new level, at which the upward forcing balances with the downward diffusion along the magnetic field line (Rishbeth, 1978).…”

Section: Introductionmentioning

confidence: 99%

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

2007

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An operational model was developed for forecasting ionospheric variations and storms at Kokubunji (35 • N, 139• E), 24 hours in advance, by using a neural network. The ionospheric critical frequency (foF2) shows periodic variabilities from days to the solar cycle length and also shows sporadic changes known as ionospheric storms caused by geomagnetic storms (of solar disturbance origin). The neural network was trained for the target parameter of foF2 at each local time and input parameters of solar flux, sunspot number, day of the year, Kindex at Kakioka. The training was conducted using the data obtained for the period from 1960 to 1984. The method was validated for the period from 1985 to 2003. The trained network can be used for daily forecasting ionospheric variations including storms using prompt daily reports of K-index, sunspot number, and solar flux values available on-line.

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“…These ionospheric data assimilation models include the University of Southern California/the Jet Propulsion Laboratory Global Assimilative Ionospheric Model (USC/JPL GAIM) [ Hajj et al , 2000, 2004; Mandrake et al , 2005; Pi et al , 2003, 2004a, 2004b; Wang et al , 2004], the Utah State University Global Assimilation of Ionospheric Measurements (USU GAIM) [ Scherliess et al , 2004, 2006a, 2006b; Schunk et al , 2003, 2004, 2005; Thompson et al , 2006], and the IonoNumerics model [ Khattatov et al , 2004, 2005]. At the same time, the observations including electron densities, plasma drifts, magnetometer measurements, and so on are also assimilated into models to accomplish some scientific researches [ Eccles , 2004; Lei et al , 2004b, 2004c; Retterer et al , 2005; Richmond and Kamide , 1988; Richmond et al , 1988; Sojka et al , 2001, 2003; Zhang et al , 1999, 2001, 2002, 2003]. In the ionospheric data assimilation applications, a majority of the employed data assimilation methods are 3DVAR [ Bust et al , 2004], 4DVAR [ Pi et al , 2003, 2004a, 2004b], and Kalman filter or its approximations [ Fuller‐Rowell et al , 2004; Hajj et al , 2004; Scherliess et al , 2004].…”

Section: Introductionmentioning

confidence: 99%

Data assimilation of incoherent scatter radar observation into a one‐dimensional midlatitude ionospheric model by applying ensemble Kalman filter

et al. 2007

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[1] In this paper, electron densities during 25-28 September 2000 observed by the Millstone Hill incoherent scatter radar (ISR) are assimilated into a one-dimensional midlatitude ionospheric theoretical model by using an ensemble Kalman filter (EnKF) technique. It is found that (1) the derived vertical correlation coefficients of electron density show obvious altitude dependence. These variations are consistent with those from ISR observations. (2) The EnKF technique has a better performance than the 3DVAR technique especially in the data-gap regions, which indicates that the EnKF technique can extend the influences of observations from data-rich regions to data-gap regions more effectively. (3) Both the altitude and local time variations of the root mean square error (RMSE) of electron densities for the ensemble spread and ensemble mean from observation behave similarly. It is shown that the spread of the ensemble members can represent the deviations of ensemble mean from observations. (4) To achieve a better prediction performance, the external driving forces should also be adjusted simultaneously to the real weather conditions. For example, the performance of prediction can be improved by adjusting neutral meridional wind using equivalent wind method. (5) In the EnKF, there are often erroneous correlations over large distance because of the sampling error. This problem may be avoided by using a relative larger ensemble size.

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Modeling investigation of ionospheric storm effects over Millstone Hill during August 4–5, 1992

Cited by 5 publications

References 25 publications

Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment

Development of a middle and low latitude theoretical ionospheric model and an observation system data assimilation experiment

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

Data assimilation of incoherent scatter radar observation into a one‐dimensional midlatitude ionospheric model by applying ensemble Kalman filter

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