Data Assimilation Research Of Mid- And Low-latitude Ionosphere

The earth's ionosphere which displays space weather is the region that is important part and key layer in the space, which is up to Magnetosphere and low to mid/high Atmosphere, with all of them exsiting strong coupling. Therefore there are vital meanings in science that researching ionosphere. The enough freedom electrons existed in ionosphere have critical impacts on radio communication and Satellite navigation and measurement and the human's space actitvty, through affected the propagation of radio waves. So researching ionosphere also has important application values.In the recent years, accompanied our space activities, there are more and more space vehicles that orbit in ionosphere, which suffer from the changed ionosohere. And it also disturbs radio communication. Therefore, there is a growing need to more accurately represent and forecast the ionospheric climate and disturbances.At present, it is the main method, using the history observation data of ionosphere to nowcast or forecast, which used widely in engineers or systems that need the next moment ionospheric information. And ionospheric theoretical model is a powerful tool to study the physic of ionosphere. Not only ionospheric empirical model constructed from history data but also theoretical model has fatal deficiencies. On the one hand, based on observations empirical model essentially is just a mathematical method that simply interpolate when forcasting ionosphere although empirical model is simple and reliable, and can not describe the complex changes of ionosphere; On the other hand, theoretical calculations and initial/boundary-conditions of theoretical model relied on physical laws are incredible. It is the major reasons for incredible forcast of ionosphere that the deficiencies in empirical or theoretical model.For better using observations and models, we attempt to incorporate the observations into ionospheric models by using optimization schemes, which is known as data assimilation methods, to give specific representions 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 data assimilation. We studied several important techniques of ionospheric data assimilation. Main results of this thesis are outlined as follows:First, base on simulated and measured observations, we carry out systematically observation system data assimilation experiments, combined with non-linear least- squares fit.(1) Based on least-squares fit, the experiments we carred out are divided into two types, one that both observations and background simulated by International Reference Ionosphere 90(IRI90), another that observations and background simulated by different models that difference is observations simulated by International Reference Ionosphere 2007(IRI2007) and Theoretical Ionospheric Model of the Earth in Institute of Geology and Geophysics, Chinese Academy of Sciences(IGGCAS1D). The experiments showed that non-linear least square fit is credible in data assimilation and estimating the ionospheric external drivers.(2) In ZiWu Forecast Service Platform for Civil Project, it was introduced that observation system data assimilation experiments at 120 degree of longtitude 10～60 degree of latitude(interval 10 degrees). The observations was provided by model simulation and Ionosondes at Hainan/Beijing Station in ZiWu Project. The results indicates that analysis(experiments'nowcast or forecast) conforms to observations.Next, the newly Optimal Interpolation(OI) platform was constructed by us. Based on the platform, validation test and assimilation experiments was introduced. Using experiments'results, we tried to introduce residual predict.(1) By contrast the development of data assimilation in meteorology, we introduced OI to space physics.The OI and its platform has been validated by simulation experiment.(2) Based on IGGCAS1D model and Xiamen digital ionosonde observations, we explored the application of OI. The detailed time of observations was that, June 15 UTh 1100, July 22/23 UTh 1100, August 6 UTh 1045, August 31 UTh 1115, 2009, respectively. The assimilation experiments'result shows that analysis conforms to observation even if background far away from it.(3) After assimilation experiments, we did residual predict, using experiments'residuals, that the differential between analysis and background, and model background needed prediction. The detailed time prediction was June 15 UTh 1115, July 22/23 UTh 1115, August 6 UTh 1115, August 31 UTh 1130, 2009, respectively. Contrsting to backgrounds, the results of residual predict accord with observations very well. In addition, it shows that this forecast method is worth trying.(4) To give better background error covariance in OI data assimilation, we introduced Ensemble Optimal Interpolation(EnOI) that based on number ensemble samples. Finally, in order to improving nowcast and forecast, Ensemble Kalman Fileter(EnKF) was represented known as an advanced data assimilation method in ionosphere data assimilation. The main content we mentioned is that the theory and equation and flowchart of EnKF. By the way, the virtues and shortcomings of EnKF have been pointed out.