Modeling And Data Assimilation Of Mid-and Low-Latitude Ionosphere

The Earth's ionosphere locates between the outer space and middle atmosphere and is an important part and key layer in the whole sun-earth environment system. The research on the ionosphere can enrich our knowledge of the sun-earth system and serve the human's space activities. Therefore, it is significantly for us to continue the study on the ionosphere. In the recent years, with the increase of the human's space activities and communication systems, there is a growing need to more accurately represent and forecast the ionospheric climate and disturbances. Several groups attempt to incorporate the observations into ionospheric models by using optimization schemes, which is known as data assimilation methods, to give specific representations of the ionosphere. This technique has manifested potential ability in ionospheric nowcast and forecast. In this paper, considering the geographic location of our country, we concentrate on the investigations of middle and low latitude ionospheric modeling and data assimilation. We constructed several empirical and theoretical ionospheric models and investigated several basic physical problems using these models. In addition, we also studied several important techniques of ionospheric data assimilation. The main contents are as follows:1, Constructing empirical models based on observations and analyzing ionospheric long term trend.Wuhan ionospheric observatory is one of the most long lasting ionosphere stations in the world. Based on its observations, several empirical ionospheric models have been developed, such as TEC, foF2 and hmF2 empirical models. In this paper, we first constructed an empirical foE model with high precision over Wuhan using fifty years'observations of foE. This model is an important complement to the local empirical ionospheric model over Wuhan. A comparison between our model and IRI and Titheridge's model shows that our model has a better performance. By using Artifical Neural Network (ANN) method, we constructed empirical models of foF2 over Asia/Australia sector based on ionosonde observations. The model has been validated by comparison with Wuhan station's observations and the results showed that the ANN model has high precision. Using this ANN model, we systematically analyzed the long term trends of foF2 over this area for the first time. Results illustrated that the foF2 in the Asia/Australia sector has an average decrease of 0.05% per year in the past fifty years. This trend can not be interpreted only by greenhouse effect. Many other factors which can influence the ionosphere, such as solar and geomagnetic activities and neutral background gas, might also contribute to the trend.2, Constructing theoretical ionospheric model and modeling several ionospheric phenomena.Our group has done many works on the ionospheric modes, including neutral wind, ionospheric electric field, middle latitude theoretical model and low latitude two dimensional theoretical model. On the basis of these works, we developed a middle and low latitude theoretical ionospheric model with high precision, high space and time resolution, speediness and flexibility, through solving plasma continuity, momentum and energy equations simultaneously. The model is named Theoretical Ionospheric Model of the Earth in Institute of Geology and Geophysics, Chinese Academy of Sciences (TIME-IGGCAS). TIME-IGGCAS was validated by comparisons with several other typical empirical ionospheric models and multi-observations. The results showed that the modeled electron density and plasma temperatures are quantitatively and qualitatively in good agreement with those of empirical models and observations. TIME-IGGCAS can reproduce most anomalistic features including equatorial anomaly, winter anomaly and semiannual anomaly. The model results have relatively large deviations near sunrise time and sunset time and at the low altitudes. There results give us a reference to improve the model and develop the data assimilation model in the future. We did several modeling studies based on the developed theoretical model.(1) Modeling the climate features of the equatorial ionization anomaly (EIA). Our analysis concentrated on the locations and the corresponding TEC values of northern crest, equatorial trough and southern crest, and also the width of the trough and the ratio of crest to trough. The modeling results showed that the EIA has typical local time, seasonal and solar variations. The equatorial trough usually lies on the two sides of the geomagnetic equator and varies with seasons. The two crests have obvious asymmetries in solstice. The EIA is fully developed around midday in winter, postnoon in equinoxes and late afternoon in summer. The width of the trough and the ratio of crest to trough have obvious seasonal variations. These seasonal dependences of EIA can generally be interpreted by the seasonal variations of the equator ward wind, the transequatorial neutral wind, and the subsolar point.(2) Modeling the response of EIA to the disturbed electric field. When the disturbance of the E×B vertical drift velocity is upward (downward), electron content has negative (positive) disturbance in the area with geomagnetic latitude less than 15 degree and positive (negative) in the area around geomagnetic latitude 20 degree. There is a transitional area with 3 degree's width between positive and negative disturbance. In the area of geomagnetic latitude larger than 30 degree, there is no significant ionospheric disturbance. The effect of disturbed electric field would last for several hours after the disappearance of the disturbed electric field. The disturbance of EIA linearly increases with the increase of disturbed electric field during or after the disturbance of electric field.(3) Modeling the validation of the method of using hmF2 to derive electric field vertical drift velocity. Since the lack of direct observations of ionospheric electric field, many indirect methods have been put forward to obtain it. Several researchers use the time rate of change of hmF2 to represent the E×B vertical drift. In this paper, we used the TIME-IGGCAS model to confirm its validation from a view of theory and study the local time, seasonal and solar variations of its validation of this method. In addition, we also tested the validation of this method during disturbed conditions. Our modeling results indicated that this method is usable near sunrise, sunset and post sunset (0600-0730, 1700-2100 LT). The derived velocity by this method is smaller than observations in the rest local times. During disturbed conditions, the variations of hmF2 can be used to determine the occurrence of intense electric field disturbance.(4) Modeling the effects of the variations of geomagnetic field on the ionosphere. When analyzing the ionospheric long term trends from ionosonde observations, we found that just greenhouse effect is insufficient. We suggest that the long term variations of geomagnetic field may be another origin. To confirm this interpretation, we modeled the effects of long term variations of geomagnetic field on the ionospheric long term trend by TIME-IGGCAS for the first time. The modeling results indicate that the variations of geomagnetic field indeed can result in the long term variations of ionosphere because of the variations of the effects from the neutral wind on the ionosphere. Since the geomagnetic field variations differ from place to place, the ionospheric trends induced from geomagnetic field also have location dependence. The modeled ionospheric trends also have obvious seasonal and local time variations. Because the corresponding controlling factors have location dependence, these variations also show typical regional features. By comparison with existing results, we suggest that the changes of the global geomagnetic field may partly contribute to the inconsistent seasonal and local time variation patterns in ionospheric trends from different observations. We conclude that the effects of geomagnetic orientation on the ionospheric long term trend cannot be ruled out, especially in areas with large geomagnetic field variations. The above modeling studies not only investigate the corresponding physical problems, but also illustrate that our model is steady and credible. These results imply a good base for the using of the model and the development of the ionospheric data assimilation model in the future.3, Based on the developed model, we did several data assimilation experiments and studied several important techniques of data assimilation.(1) We carried out an observation system data assimilation experiment using TIME-IGGCAS model and non-linear least square fit method. The experiment showed that the theoretical model is steady and credible and the ionospheric external drivers can be estimated by non-linear least square fit method. Using the same method, we modeled the ionospheric and thermospheric response to the super storm during November 7-9, 2004 in Asia/Australia sector by assimilating the GPS observations from 42 stations. The electric field, neutral meridian wind, and the ratio of O to N2 (O/N2) are estimated through minimizing the differences between modelling results and observations by nonlinear least square fit method. The modeling results including electron density and the estimated drivers have been validated by empirical models and the corresponding observations. Combining observations and modeling results, this storm can generally be understood as follows. During night time of this storm the equator-ward wind and disturbed eastward electric field are the main factor that attribute to the observed ionospheric enhancement. During the daytime northern hemisphere shows mainly positive storm and southern hemisphere is predominated by negative storm. This is probably the combined effects of the asymmetry of O/N2 in two hemispheres, summer-to-winter wind, and disturbed electric field. Under the competitive influences of varied electric field, wind field and neutral compositions, the phase of storm shows complicated variations versus latitudes and local times.(2) We analyzed the spatial correlation of ionosphere to give better background error covariance in data assimilation. When doing the ionospheric data assimilation, the background error covariance has determined effect on the assimilated results. So an accurate correlation model of the ionosphere is very important. In this paper, we statistically investigated the spatial correlation of ionospheric day-to-day variability and the local time, seasonal, and latitudinal variations of correlation distances in three directions using JPL GIM during 2000 and 2005 and one month observations of Millstone Hill incoherent scatter radar observations during October, 2002. We also attempted to interpret these variabilities. At last we discussed the possible reasons of the relative larger correlations between geomagnetic conjugate points, the possible factors of the variations of ionospheric correlation distances, and the effects of ionospheric storm on the ionospheric correlation. Generally, the ionospheric correlation distance increases with the increase of altitude and is larger during daytime than at night, at middle latitude than low latitude, and during high solar activity than low solar activity. The correlations of northern middle latitudes have obvious seasonal variations. Our statistical results confirm that the factors which result in the ionospheric day-to-day variability have different spatial scales. In addition, our results are useful in the constructing of a background covariance matrix in ionospheric data assimilation.(3) Based on a middle latitude theoretical ionospheric model and Millstone Hill incoherent scatter radar observations, we explored the application of Ensemble Kalman filter (EnKF) known as an advanced data assimilation method in ionosphere data assimilation. It is found that the derived vertical correlation coefficients of electron density show obvious altitude dependence. These variations are consistent with those from ISR observations. 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 behaves similarly. It is shown that the spread of the ensemble members can represent the deviations of ensemble mean from observations. 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. 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. 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.This paper indicates that ionospheric modeling not only is the summarization of ionospheric observations and theoretical research, but also can enhance our knowledge of ionosphere structure and variability and the corresponding physical problems. The ionospheric data assimilation can open us a new method to give better ionosphere research and forecast by combing the observations and models. In conclusion, the research results in this paper can advance our knowledge of the ionosphere. The constructed model can be used to model several physical processes and phenomena in the ionosphere. The data assimilation method has potential ability to give better nowcast and forecast of space weather especially ionospheric space through combining the model and observations.