Assimilation Algorithm Research Articles

Generating a Spatio-Temporal Complete 30 m Leaf Area Index from Field and Remote Sensing Data

The leaf area index (LAI) is an important parameter for vegetation monitoring and land surface ecosystem research. Although a variety of LAI products have been generated, the moderate to coarse spatial resolution and low temporal resolution of these products are insufficient for regional-scale analysis. In this study, a modified ensemble Kalman filter model (MEnKF) was proposed to generate spatio-temporal complete 30 m LAI data. High-quality, filtered historical Moderate-resolution Imaging Spectroradiometer (MODIS) LAI data were used to obtain the LAI background, and an LAI temporal dynamic model was constructed based on it. An improved back-propagation (BP) neural network based on a simulated annealing algorithm (SA-BP) was constructed with paired Landsat surface reflectance data and field LAI data to generate a 30 m LAI. The MEnKF was used to estimate the spatio-temporal complete LAI beginning from the LAI peak value position where Landsat observations were available. The spatio-temporal 30 m LAI was estimated in farmland (Pshenichne), grassland (Zhangbei), and woodland (Genhe) sites. The results indicate that the MEnKF-estimated LAI is consistent with the field measurements for all sites (the coefficient of determination ( R 2 ) = 0.70; root mean squared error (RMSE) = 0.40) and is better than that of the conventional sequence data assimilation algorithm ( R 2 = 0.40; RMSE = 0.78). The regional LAI captures the vegetation growth pattern and is consistent with the Landsat LAI, with an R 2 larger than 0.65 and an RMSE less than 0.51. The proposed MEnKF algorithm, which effectively avoids error accumulation in the data assimilation scheme, is an efficient method for spatio-temporal complete 30 m LAI estimation.

ECONOMIC ALGORITHM FOR EVALUATING THE PARAMETER FOR THE GLOBAL MODEL OF TRANSFER AND DIFFUSION

The paper considers the data assimilation algorithm for the global model of transport and diffusion. An algorithm is proposed for finding an estimate of an unknown parameter for the transport and diffusion problem of a passive impurity. Various options for data assimilation algorithms with unknown parameters are described: searching for a joint assessment of a system and a parameter and evaluating only a parameter. The problems of implementing data assimilation algorithms and methods for solving them are shown. The ensemble algorithm of the Kalman filter is given, the economical use of it is argued. An important property of the proposed algorithm is its locality - the algorithm can be applied locally in subdomains. The results of numerical experiments with model data for estimating the unknown emission of a passive impurity from concentration data are presented. A comparative analysis of the results is carried out

Observability analysis and state observer design for a cardiac ionic cell model

To gain insights into cardiac arrhythmias, researchers have developed and employed various measurement techniques, such as electrocardiography, optical mapping, and patch clamping. However, there are no measurement methods that allow simultaneous recording of all cellular quantities, including intracellular ionic concentrations and gating states, that may play an important role in arrhythmia formation. To help address this shortcoming, we applied observability analysis, a method from control theory, to the Luo–Rudy dynamic (LRd) model of a cardiac ventricular myocyte. After linearizing the time-integrated LRd model about selected periodic orbits, we computed the observability properties of the model to determine whether past system states could be reconstructed from different hypothetical sets of measurements. Under the simplifying assumption that only one dynamical variable could be measured periodically, we found that intracellular potassium concentration generally yielded the largest observability values and thus contained the most information about the dominant modes of the system. The impacts on observability of measurement timings, inter-stimulus interval length, and an alternans-promoting parameter shift were also studied. Pole-placement state observer algorithms were designed and tested in simulations for several scenarios, and we found that it is possible to infer unmeasured variables from potassium-concentration measurements, and to an extent from membrane-potential measurements, both for longer periods that represent normal rhythms and shorter periods associated with tachyarrhythmias. Our results could lead to improved data assimilation algorithms that combine model predictions with measurements to estimate quantities that are difficult or impossible to measure during in vitro experiments.

Error analysis of fully discrete mixed finite element data assimilation schemes for the Navier-Stokes equations

In this paper we consider fully discrete approximations with inf-sup stable mixed finite element methods in space to approximate the Navier-Stokes equations. A continuous downscaling data assimilation algorithm is analyzed in which measurements on a coarse scale are given represented by different types of interpolation operators. For the time discretization an implicit Euler scheme, an implicit and a semi-implicit second-order backward differentiation formula are considered. Uniform-in-time error estimates are obtained for all the methods for the error between the fully discrete approximation and the reference solution corresponding to the measurements. For the spatial discretization we consider both the Galerkin method and the Galerkin method with grad-div stabilization. For the last scheme error bounds in which the constants do not depend on inverse powers of the viscosity are obtained.

Unscented Kalman filter (UKF)–based nonlinear parameter estimation for a turbulent boundary layer: a data assimilation framework

A turbulent boundary layer is a ubiquitous element of fundamental and applied fluid mechanics. Unfortunately, accurate measurements of turbulent boundary layer parameters (e.g. friction velocity and wall shear τw) are challenging, especially for high-speed flows (Smits et al ). Many direct and/or indirect diagnostic techniques have been developed to measure wall shear stress (Vinuesa et al ). However, based on various principles, these techniques generally give different results with varying uncertainties. The current study introduces a nonlinear data assimilation framework based on the unscented Kalman filter (UKF) that can fuse information from (i) noisy and discretized measurements from stereo particle image velocimetry (SPIV), a Preston tube, and a MEMS shear stress sensor, as well as (ii) the uncertainties of the measurements to estimate the parameters of a turbulent boundary layer. A direct numerical simulation of a fully developed turbulent channel flow is used first to validate the data assimilation algorithm. The algorithm is then applied to experimental boundary layer data at Mach 0.3 obtained in a blowdown wind tunnel facility. Drag coefficients from control volume analysis of the SPIV and wall pressure data and laser interferometer skin friction measurements are used for independent cross-validation. The UKF-based data assimilation algorithm is robust to the uncertain and discretized experimental data and is able to provide accurate estimates of turbulent boundary layer parameters with quantified uncertainty.

Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies

Assimilating remote sensing data with crop growth model is a promising method to estimate crop yields over a large area. However, the method is always subject to the problems with biases in remote sensing products and assimilation weights in practical applications. In this study, we demonstrated the robustness of a ‘spatial assimilation’ method in dealing with the biases in three different remote sensing leaf area index (LAI) products. We further explored assimilation strategies for determining the assimilation weights when using ‘spatial assimilation’ method. Three different remote sensing LAI products were assimilated with MCWLA-Wheat model in the North China Plain during 2008–2015. The results demonstrated that the ‘spatial assimilation’ method was robust in mitigating the influences of biased LAI values and easily coupled with various LAI products based on different sources and retrieving algorithms. Furthermore, we found that the historical experiences of the optimal assimilation weights were not suitable to directly drive data assimilation in the coming seasons. Thus data-assimilation strategies to estimate crop yields without prior knowledge on the optimal assimilation weights were investigated. Two ensemble-mean-based assimilation strategies were recommended, which could reach 84 ~ 98% of yield estimation accuracy using the optimal assimilation weights. This study provides reliable and promising solutions for yield estimation over a large area using data assimilation without being limited by the biased state variables in remote sensing products and the lack of prior knowledge on the optimal assimilation weights. The ‘spatial assimilation’ method and the proposed ensemble-mean-based assimilation strategies have great potentials for wide applications, laying solid foundations for developing crop growth monitoring and yield forecasting system.

The analysis of assumptions’ error sources on assimilating ground-based/spaceborne ionospheric observations

In this paper, GNSS ionospheric observations from ground-based and spaceborne systems were simulated, and the global 3D ionospheric density field was reconstructed by Kalman-Filter algorithm. Various errors and influences in the assimilation of the ionospheric data were analyzed, and corresponding improvement methods were proposed, which were verified from the following two aspects based on simulation analysis:1. From the statistics of slant total electron content(TEC): (a) the influence of the shell with altitude between 800 km and 20000 km was 20̃30%, and could be reduced by the two-step assimilation method; (b) the influence of ionospheric time variations could reach ̃10%, and was reduced to ̃5% by the correction proposed in this paper; (c) the influence of the ionospheric grid representation was ̃2.5%, and was reduced to 0.9% by the method of the bilinear interpolation at intercept midpoint. The three errors mentioned above are called assumptions’ error, for they always be ignored or deduced by theoretical assumptions.2. The results of electron density reconstruction showed algorithm with assumptions errors corrections was better than origin one, which confirmed the effectiveness of the correction algorithms to the assumptions errors. And it was also showed that the Kalman filter assimilation algorithm was better than the Abel-Retrieved method, especially in ionospheric F2 region, for there is no ionospheric symmetric assumption.

Particle filtering for chaotic dynamical systems using future right-singular vectors

In this paper, we combine tools from the study of chaotic dynamical systems with nonlinear non-Gaussian data assimilation algorithms to produce novel particle filtering algorithms, where the use of dynamical information improves both the accuracy of estimation of the unknown state as well as improves the diversity of the particles that collectively represent the posterior distribution. Unlike past efforts of assimilation in the unstable subspace, we focus not on the deterministic signal and low observation noise case, but instead on the case of moderate signal and observation noise. Filtering algorithms using finite-time Lyapunov vectors, left-singular vectors, and a novel concept of future right-singular vectors, to project observations onto reduced subspaces are developed and tested against two regimes of the single scale Lorenz 1996 model—a weakly chaotic, non-Gaussian regime and a strongly chaotic, near-Gaussian regime. As modeling in the geosciences continue to improve resolution and fidelity of finer physical processes, the models are requiring data assimilation techniques that can handle the fully nonlinear, non-Gaussian case and represent multimodal distributions in high-dimensional spaces. This paper contributes to pushing the boundary of nonlinear data assimilation by aiming to improve particle filtering algorithms for high-dimensional chaotic systems.

Using petro-elastic proxy model to integrate 4D seismic in ensemble based data assimilation

4D seismic data provide valuable information for petroleum engineers to update simulation models using different data assimilation algorithms. To assimilate 4D seismic data, the conventional approach is to use a petro-elastic model (PEM) to bring simulation models to the same domain as the 4D seismic data, or vice-versa. PEMs are constructed using rock and fluid models with uncertainty in both input parameters and models. Apart from their uncertainty, PEMs’ inclusion in data assimilation algorithms requires an interdisciplinary team of rock physicists and petroleum engineers to develop and implement them. A method was developed in this research to replace the PEM with petro-elastic proxy model (PEM-Proxy) for 4D seismic data assimilation in ensemble based algorithms. PEM-Proxy used here was a linear equation with two coefficients linking water saturation and pore pressure changes to the 4D signal, in our case 4D acoustic impedance (4DAI). Two versions of the PEM-Proxy were developed namely PFC and PUC with fixed and uncertain coefficients, respectively. Eventually, PEM was replaced by the PEM-Proxies in Ensemble Smoother with Multiple Data Assimilation (ES-MDA) algorithm to integrate 4D seismic data. Our method was applied to a benchmark case study, using various data assimilation cases to compare the PEM-Proxies application with the PEM. Results showed that the implementation of the PEM-Proxies did not change the behavior of well and 4D seismic objective functions through data assimilation iterations. Interestingly the PUC implementation was satisfactory in terms of well objective functions and even provided better data assimilation cases for 4D seismic objective function. Production forecast reliability for various cases were analyzed and wells forecast showed similar behavior comparing PEM and PEM-Proxies cases. Our method helps petroleum engineers avoid demanding rock and fluid physics equations and replace them with a straightforward and computationally less expensive model for 4D seismic data assimilation.

Vosstanovleniye kontsentratsii vzveshennogo veshchestva v verkhnem sloye Azovskogo morya na osnove variatsionnogo algoritma assimilyatsii dannykh izmereniy

Vosstanovleniye kontsentratsii vzveshennogo veshchestva v verkhnem sloye Azovskogo morya na osnove variatsionnogo algoritma assimilyatsii dannykh izmereniy

Research advances in model simulation and data assimilation of water and carbon fluxes in land surface vegetation

Accurately estimating water and carbon fluxes is of great significance for the research in land surface water and carbon cycles. However, it is very challenging. The estimation accuracy needs further improvement. Both traditional model simulation and site observation methods have advantages and disadvantages, which need to be examined in combination. Data assimilation integrates observations into models based on physics laws to obtain the optimal estimates of model state variables and parameters as much as possible, and provides an effective way for their combination. In this review, we traced the research progress for process models assimilated with multi-source observational data of land surface water carbon fluxes and analyzed the domestic and foreign research status of land surface process models focused on water carbon fluxes, data assimilation algorithms, and assimilation of land surface carbon flux data. We summaried problems in this research area, including insufficient coordination of multi-source observation data, relatively simple assimilation strategy, lacking fusion of assimilation models, and limited assimilation scale. The future development directions and trends were analyzed and prospected from five aspects, including assimilation strategy, model selection, data expansion, scale effect, and scientific calculation. This work would provide more comprehensive background information for scholars in this field, and arouse common concerns.

Hybrid Gain Data Assimilation Using Variational Corrections in the Subspace Orthogonal to the Ensemble

AbstractThe viability of a parameterless hybrid data assimilation algorithm is investigated. As an alternative to the traditional hybrid covariance scheme, hybrid gain data assimilation (HGDA) was proposed to blend the gain matrix derived from the variational method and the ensemble-based Kalman filter (EnKF). A previously proposed HGDA algorithm uses a two-step process applying the EnKF with a variational update. The algorithm is modified here to limit the variational correction to the subspace orthogonal to the ensemble perturbation subspace without the use of a hybrid weighting parameter, as the optimization of such a parameter is nontrivial. The modified HGDA algorithm is investigated with a quasigeostrophic (QG) model. Results indicate that when the climatological background error covariance matrix B and the observation error covariance R are well estimated, state estimates from the parameterless HGDA are more accurate than the parameter-dependent HGDA. The parameterless HGDA not only has potential advantages over the standard HGDA as an online data assimilation algorithm but can also serve as a valuable diagnostic tool for tuning the B and R matrices. It is also found that in this QG model, the empirically best static B matrix for the stand-alone 3DVAR has high variance at larger spatial scales, which degrades the accuracy of the HGDA systems and may not be the best choice for hybrid methods in general. A comparison of defining the orthogonal subspace globally or locally demonstrates that global orthogonality is more advantageous for stabilizing the hybrid system and maintains large-scale balances.

Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters

Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages, but they are not without non-negligible uncertainties, among which inexact input parameters play a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch scheduled for 2021 and with a required lifetime of at least 3 years, will be dedicated to the measuring of water surface elevations, widths and surface slopes of rivers wider than 100 m, at a global scale. SWOT will provide a significant number of new observations for river hydrology and maybe combined, through data assimilation, with global-scale models in order to correct their input parameters and reduce their associated uncertainty. Comparing simulated water depths with measured water surface elevations remains however a challenge and can introduce within the system large bias. A promising alternative for assimilating water surface elevations consists of assimilating water surface elevation anomalies which do not depend on a reference surface. The objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that can assimilate synthetic SWOT observations of water depths and water elevation anomalies to correct the input parameters of a large-scale hydrologic model over a 21 d time window. The study is applied to the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm, tested through a set of observing system simulation experiments (OSSEs), is able to retrieve the true value of the Manning coefficients within one assimilation cycle much of the time (basin-averaged Manning coefficient root mean square error, RMSEn, is reduced from 33 % to [1 %–10 %] after one assimilation cycle) and shows promising perspectives with assimilating water anomalies (basin-averaged Manning coefficient RMSEn is reduced from 33 % to [1 %–2 %] when assimilating water surface elevation anomalies over 1 year), which allows us to overcome the issue of unknown bathymetry.

Site Characterization at Downhole Arrays by Joint Inversion of Dispersion Data and Acceleration Time Series

ABSTRACTWe present a sequential data assimilation algorithm based on the ensemble Kalman inversion to estimate the near-surface shear-wave velocity profile and damping; this is applicable when heterogeneous data and a priori information that can be represented in forms of (physical) equality and inequality constraints in the inverse problem are available. Although noninvasive methods, such as surface-wave testing, are efficient and cost-effective methods for inferring an VS profile, one should acknowledge that site characterization using inverse analyses can yield erroneous results associated with the lack of inverse problem uniqueness. One viable solution to alleviate the unsuitability of the inverse problem is to enrich the prior knowledge and/or the data space with complementary observations. In the case of noninvasive methods, the pertinent data are the dispersion curve of surface waves, typically resolved by means of active source methods at high frequencies and passive methods at low frequencies. To improve the inverse problem suitability, horizontal-to-vertical spectral ratio data are commonly used jointly with the dispersion data in the inversion. In this article, we show that the joint inversion of dispersion and strong-motion downhole array data can also reduce the margins of uncertainty in the VS profile estimation. This is because acceleration time series recorded at downhole arrays include both body and surface waves and therefore can enrich the observational data space in the inverse problem setting. We also show how the proposed algorithm can be modified to systematically incorporate physical constraints that further enhance its suitability. We use both synthetic and real data to examine the performance of the proposed framework in estimation of the VS profile and damping at the Garner Valley downhole array and compare them against the VS estimations in previous studies.

Variational Data Assimilation in Problems of Modeling Hydrophysical Fields in Open Water Areas

The formulation of boundary conditions at liquid (open) boundaries is a topical problem in mathematically modeling the hydrothermodynamics of open water areas. Variational data assimilation is one method allowing one to take into account liquid boundaries in models. According to the approach considered in this paper, observational data at a certain time are given and the problem is treated as an inverse one with open boundary flows as additional unknowns. This paper presents a formulation of the general problem of the variational assimilation of observational data for a model of the hydrothermodynamics of open water areas based on the splitting method. Algorithms for the variational assimilation of temperature and sea-level data at the liquid boundary are formulated and the results of numerical experiments on the use of the algorithms in the Baltic Sea circulation model are presented.

A fundamental theorem for eco-environmental surface modelling and its applications

We propose a fundamental theorem for eco-environmental surface modelling (FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling (FTESM). The Beijing-Tianjin-Hebei (BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling (HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-mean-square error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

Вариационные алгоритмы идентификации мощности импульсного источника загрязнения в модели переноса примеси

To solve the problems of monitoring the environmental situation it is necessary to create computer systems that allow assessing the state of the environment. Such systems should allow for rapid assessment of the effects of pollution in the areas under investigation, especially in the areas of traditional shipping lanes and intensive construction of various communications. To solve such problems, it is necessary to have mathematical models and methods for solving inverse problems, when the input parameters of the model are determined using the measurement data due to their rapid assimilation. Variational algorithms for assimilation of incoming information are based on iterative minimization of the quadratic prediction quality function that characterizes deviations of the solution obtained from the used model from the measurement data. In this case, the mathematical model is a constraint on the variation of input parameters while minimizing the forecast quality functional. In this paper we propose the use of such procedures for the assimilation of data distributed in space and time. Based on the passive impurity transfer model, various approaches are considered to solve the problem of identifying the power of a point instantaneous source of pollution from time and space distributed measurement data. In this paper, we consider a variational method for identifying the power of a point instantaneous source of pollution based on solving adjoint  problems and the problem in variations. The proposed algorithm is obtained for the case when the measurement data is received at different times. In addition, the method of estimation and the variational method of filtering linear systems of equations are considered for searching for the required values. The initial values of the power of the pollution source are determined in the result of solving the redefined system of equations. The result obtained is consistent with all available information about the impurity concentration. The considered algorithms for identifying the power of the pollution source in relation to the passive impurity transfer model can be used to solve various environmental problems when studying the impact of anthropogenic pollution sources.

Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series.

Extreme events appear in many complex nonlinear dynamical systems. Predicting extreme events has important scientific significance and large societal impacts. In this paper, a new mathematical framework of building suitable nonlinear approximate models is developed, which aims at predicting both the observed and hidden extreme events in complex nonlinear dynamical systems for short-, medium-, and long-range forecasting using only short and partially observed training time series. Different from many ad hoc data-driven regression models, these new nonlinear models take into account physically motivated processes and physics constraints. They also allow efficient and accurate algorithms for parameter estimation, data assimilation, and prediction. Cheap stochastic parameterizations, judicious linear feedback control, and suitable noise inflation strategies are incorporated into the new nonlinear modeling framework, which provide accurate predictions of both the observed and hidden extreme events as well as the strongly non-Gaussian statistics in various highly intermittent nonlinear dyad and triad models, including the Lorenz 63 model. Then, a stochastic mode reduction strategy is applied to a 21-dimensional nonlinear paradigm model for topographic mean flow interaction. The resulting five-dimensional physics-constrained nonlinear approximate model is able to accurately predict extreme events and the regime switching between zonally blocked and unblocked flow patterns. Finally, incorporating judicious linear stochastic processes into a simple nonlinear approximate model succeeds in learning certain complicated nonlinear effects of a six-dimensional low-order Charney-DeVore model with strong chaotic and regime switching behavior. The simple nonlinear approximate model then allows accurate online state estimation and the short- and medium-range forecasting of extreme events.

Tomographic Imaging of Traveling Ionospheric Disturbances Using GNSS and Geostationary Satellite Observations

AbstractTraveling ionospheric disturbances (TIDs) are the manifestations of atmospheric gravity waves in the ionosphere. These disturbances have practical importance because they affect satellite navigation technologies such as Global Navigational Satellite System (GNSS), causing degradation in precise positioning applications. They also have scientific significance as their generation mechanisms and propagation are not fully understood. While there are specific instruments that can measure TIDs in certain locations, there is a need for wide‐area observations across extended geographical regions to continuously monitor their onset and spatial and temporal characteristics. This paper evaluates the use of observations from ground‐based geodetic GNSS receivers to image TIDs using ionospheric tomography and data assimilation. Certain GNSS receivers also monitor signals from geostationary (GEO) satellites, which provide a unique perspective on the TID. The advantage of using the GEO data is investigated. A computerized simulation of GNSS observations is used for evaluation of the Multi‐Instrument Data Analysis System (MIDAS) with GEO and regular GNSS geometry. The simulated observations are generated by integrating the electron density through a modeled TID‐perturbed dynamic ionosphere between actual receiver and satellite positions. The output 3‐D electron density image series generated from the synthetic data by the MIDAS ionospheric tomography and data assimilation algorithm are compared with the input model ionosphere. Results show that GEO geometry improves the reconstruction of medium‐scale TIDs (MSTIDs) and smaller LSTIDs in cases where the movement of regular GNSS satellites in Medium Earth orbit (MEO) may otherwise introduce distortions to the observations.

Evaluation on the Quasi‐Realistic Ionospheric Prediction Using an Ensemble Kalman Filter Data Assimilation Algorithm

AbstractIn this work, we evaluated the quasi‐realistic ionosphere forecasting capability by an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as the background model in the system. The slant total electron contents (TECs) from global International Global Navigation Satellite Systems Service ground‐based receivers and from the Constellation Observing System for Meteorology, Ionosphere and Climate are assimilated into the system, and the ionosphere is then predicted in advance during the quiet interval of 23 to 27 March 2010. The predicted ionosphere vertical TEC (VTEC) and the critical frequency foF2 are validated by the Massachusetts Institute of Technology VTEC and global ionosondes network, respectively. We found that the ionosphere forecast quality could be enhanced by optimizing the thermospheric neutral components via the EnKF method. The ionosphere electron density forecast accuracy can be improved by at least 10% for 24 hr. Furthermore, the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) [O/N2] observations are used to validate the predicted thermosphere [O/N2]. The validation shows that the [O/N2] optimized by EnKF has better agreement with the TIMED/GUVI observation. This study further demonstrates the validity of EnKF in enhancing the ionospheric forecast capability in addition to our previous observing system simulation experiments by He et al. (2019, https://doi.org/10.1029/2019JA026554).