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

Abstract

[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.

Observing System Simulation Experiment Study on Imaging the Ionosphere by Assimilating Observations From Ground GNSS, LEO-Based Radio Occultation and Ocean Reflection, and Cross Link

“…Aside from driving the theoretical model by as much real data as possible, another potential way that can enhance the shortterm forecast capability is trying to optimize those ionospheric drivers by assimilating electron density observations based on their correlations [32], [33], [41], [42]. The most frequently used assimilation method is called ensemble Kalman filter [32], [41], [42]. As indicated in this study, the COSMIC-2 mission will provide incredible observation number with good spatial and temporal coverage globally for the first time.…”

“…5 gives a sketch map of the grid division of this data assimilation model. The background model error covariance is assumed to be the square of the background, and the error is spatially Gaussian correlated [32], [33]. Specifically, the correlation distance is two times larger in the daytime (12:00) than at night (00:00).…”

“…We developed a global ionospheric data assimilation model based on some previous studies [32], [33], [35], [37]. The key parameters of the model, including spatial and temporal resolutions, background and observation covariances, and background models, can be customized by the user.…”

“…The observation error is assumed to be uncorrelated. The error covariance of the background model and observations are assumed to be proportional to the square of their values, respectively [32]. The proportion coefficient is 0.1 and 0.01 for the model and observations, respectively, since we prefer to trust the observation more than the model.…”

Three‐dimensional ionospheric tomography reconstruction using the model function approach in Tikhonov regularization

“…The observation error of each sTEC is assumed to be independent [e.g., Bust et al, 2004;Schunk et al, 2004;Wang et al, 2004;Yue et al, 2007] and is considered as a percent of its measured value. The observation error of each sTEC is assumed to be independent [e.g., Bust et al, 2004;Schunk et al, 2004;Wang et al, 2004;Yue et al, 2007] and is considered as a percent of its measured value.…”