Search Results (111)

Soil moisture (SM) plays a pivotal role in regulating terrestrial energy-water exchanges and exerts substantial influence on agricultural productivity. In this study, a high-resolution soil moisture (HRSM) dataset (16 m) was generated by integrating multi-source remote sensing data from SMAP, HJ-2, Sentinel-2, and Gaofen-6, together with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model. The data assimilation (DA) method was implemented for assimilating HRSM within the Ensemble Kalman Filter (EnKF) framework using the Noah-MP model at a spatial resolution of 1 km. To enhance the spatial detail of SM, HRSM and its relative uncertainties derived from the three-cornered hat (TCH) method were used to update the observation error and Kalman gain in the EnKF framework, thereby improving SM profile estimates at a 16 m resolution. The performance of the DA method was evaluated against in situ measurements during the spring drought period in central Yunnan Province, China. The results show that assimilating HRSM (DA_HRSM) significantly improves surface and root-zone SM estimates in the Noah-MP model. The simulated SM from the DA_HRSM method demonstrates lower relative uncertainty. Compared to the assimilation of SMAP SM, the DA_HRSM method provides higher-resolution spatial features of SM and enhances spatial heterogeneity across 20 irrigation districts. The DA_HRSM method effectively captured the spring drought in central Yunnan, demonstrating good agreement with the Palmer Drought Severity Index (PDSI). The result highlights the advantages of incorporating high-resolution SM data into agricultural and drought monitoring systems. Full article

In managed pressure drilling (MPD), wellbore flow states cannot be obtained in real time, so kick intervention decisions rely on the empirical judgment of engineers, which introduces a significant lag. The central hypothesis of this study is that fusing a physics-constrained transient two-phase flow model with real-time surface measurements through data assimilation can reconstruct the unobservable downhole flow state and, on this basis, enable quantitative and earlier prediction of the safe intervention timing than empirical judgment alone. To this end, this paper proposes a method for real-time inversion of wellbore flow states and safety intervention timing prediction based on the Ensemble Kalman Filter (EnKF). Using a transient wellbore gas–liquid two-phase flow model as the EnKF model operator, the method continuously assimilates real-time casing pressure, standpipe pressure (SPP), and pit gain data. This process dynamically corrects model prediction bias while maintaining multiphase flow physical constraints. Thus, the method achieves high-precision dynamic inversion of wellbore pressure profiles and gas holdup distributions. On this basis, the authors use the inverted states as initial conditions to calculate safety casing pressure with the multiphase flow model. The method then predicts intervention timing by combining three trigger conditions: safety casing pressure, pit gain, and the density difference between the inlet and outlet. The authors validated the method using kick scenarios from Well L and Well Z in the Shunbei block. The results showed that the mean absolute errors (MAEs) for casing pressure inversion were 0.113 MPa and 0.135 MPa, respectively. The MAEs for SPP were 1.324 MPa and 0.954 MPa. The MAEs for pit gain were 0.174 m³ and 0.114 m³. The inverted spatiotemporal distribution of gas holdup reflected the entire process of gas migration and expansion in the wellbore. Prediction results for intervention timing showed that the method issued early warning signals approximately 53 min and 29 min earlier than actual field operations. This method provides a quantitative decision-making basis with safety redundancy for MPD field operations. Full article

Accurate cotton yield estimation in arid oasis regions faces challenges from landscape fragmentation and the conflict between monitoring precision and computational costs. To address this, we developed a robust integrated framework combining multi-source remote sensing, spatiotemporal fusion, and data assimilation. To resolve spatiotemporal data gaps, the existing Agricultural Fusion (Agri-Fuse) algorithm was validated and employed to generate high-resolution time-series data, which achieved superior spectral fidelity (Root Mean Square Error, RMSE = 0.041) compared to traditional methods like Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Subsequently, high-precision Leaf Area Index (LAI) time series retrieved via the eXtreme Gradient Boosting (XGBoost) algorithm (c = 0.97) were integrated into the Ensemble Kalman Filter (EnKF)-assimilated World Food Studies (WOFOST) model. This approach significantly corrected simulation biases, improving the yield estimation accuracy (R² = 0.86, RMSE = 171 kg/ha) compared to the open-loop model. Crucially, we systematically evaluated the trade-off between assimilation frequency and efficiency. Findings identified the 3-day fusion interval as the optimal operational strategy, maintaining high accuracy (R² = 0.83, RMSE = 181 kg/ha) while reducing computational costs by 66.5% compared to daily assimilation. This study establishes a scalable, cost-effective benchmark for precision agriculture in complex arid environments. Full article

Permeability, thermal conductivity, and porosity distribution are key factors to control groundwater flow and heat transport in porous media. The parameter estimation procedure is widely used to understand flow and transport behavior in geothermal systems. As recognized in most studies, this parameter estimation relies on the quality and quantity of spatiotemporal measurements. With the typically limited resources for conducting field investigations, understanding suitable sampling strategies is crucial before applying a model to site-specific conditions. This study aims to quantify uncertainties in hydro-thermal properties using Monte Carlo Simulation (MCS) and Ensemble Kalman Filter (EnKF). A synthetic two-dimensional aquifer profile is used to evaluate the accuracy of the estimated hydrothermal properties in accounting for variations in groundwater temperature resulting from cross-hole pumping and injection events. Based on the calculations of the mean absolute and squared errors for estimated hydrothermal properties, EnKF generally leads to more accurate estimates of hydrothermal properties than MCS. Furthermore, EnKF strikes a balance between accuracy and efficiency, making it the most effective method. This study highlights the strengths and limitations of each method, providing valuable insights for selecting appropriate inversion techniques to quantify uncertainties in geothermal systems. Additionally, well spacing and open screen locations are recommended to obtain optimal thermal energy in the geothermal system Full article

This paper presents a comprehensive and hybrid control framework for the real-time regulation of drillstring systems that are subject to complex nonlinear dynamics, including torsional stick–slip oscillations, coupled axial vibrations, and intricate bit–rock interactions. The model also accounts for parametric uncertainties and external disturbances typically encountered during rotary drilling operations. A robust sliding mode controller (SMC) is designed for inner-loop regulation to ensure accurate state tracking and strong disturbance rejection. This is complemented by an outer-loop model predictive control (MPC) scheme, which optimizes control trajectories over a finite horizon while balancing performance objectives such as rate of penetration (ROP) and torque smoothness, and respecting actuator and operational constraints. To address the challenges of partial observability and noise-corrupted measurements, an Ensemble Kalman Filter (EnKF) is incorporated to provide real-time estimation of both internal states and external disturbances. Simulation studies conducted under realistic operating scenarios show that the hybrid MPC–SMC framework substantially enhances drilling performance. The controller effectively suppresses stick–slip oscillations, provides smoother and more stable bit-speed behavior, and improves the consistency of ROP compared with both open-loop operation and SMC alone. The integrated architecture maintains robust performance despite uncertainties in model parameters and downhole disturbances, demonstrating strong potential for deployment in intelligent and automated drilling systems operating under dynamic and uncertain conditions. Full article

Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios. Full article

In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article

It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel application for short-term multivariate prediction applied to hydrological variables based on a multivariate NARX model coupled to a nonlinear recursive Ensemble Kalman Filter (EnKF). The proposed approach is designed for two hydrological stations of the Atrato river in Colombia, where the variables, water level, water flow, and water precipitation, are correlated using a NARX model based on neural networks. The NARX model is designed to consider the complex dynamics of the hydrological variables and their corresponding cross-correlations. The short-term two-day water-level forecast is designed with a fourth-order NARX model. It is observed that the NARX model coupled with EnKF improves the robustness of the proposed approach in terms of external disturbances. Furthermore, the proposed approach is validated by subjecting the NARX–EnKF coupled model to five levels of additive white noise. The proposed approach employs metric regressions to evaluate the proposed model by means of the Root Mean Squared Error (RMSE) and the Nash–Sutcliffe model efficiency (NSE) coefficient. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

High-resolution radar observations are essential to improving the numerical predictions of high-impact weather systems with data assimilation techniques. The numerical simulations of the landfall of Typhoon Lekima (2019) are conducted in the framework of the WRF model, investigating the impact of assimilating radar radial velocity observations via the Ensemble Kalman Filter (EnKF) on the typhoon’s analysis and forecast performance. The results demonstrate that the EnKF method significantly improves forecast accuracy for Typhoon Lekima, including track, intensity and the 24 h cumulative precipitation. To be specific, the control experiment significantly underestimated typhoon intensity, while EnKF-based radar radial velocity assimilation markedly improved near-surface winds (>48 m/s) in the typhoon core, refined vortex structure and reduced track forecast errors by 50–60%. Compared with the control and 3DVAR experiments, EnKF assimilation better captured typhoon precipitation patterns, with the highest ETS scores, especially for moderate-to-high precipitation intensities. Moreover, the detailed analysis and diagnostics of Lekima show that the warm core structure is better captured in the assimilation experiment. The typhoon system is also improved, as reflected by enhanced potential temperature and a more robust wind field analysis. Full article

Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation district. Vegetation effects were first removed using the water cloud model; after correction, a cubic regression using the vertical transmit/vertical receive (VV) signal retrieved surface moisture with an R² value of 0.7964 and a root mean square error (RMSE) of 0.021 cm³·cm⁻³. HYDRUS-1D, calibrated against multi-depth field data (0–80 cm), reproduced soil moisture profiles at 17 sites with RMSEs of 0.017–0.056 cm³·cm⁻³. The EnKF assimilation of satellite and ground observations further reduced the errors to 0.008–0.017 cm³·cm⁻³, with the greatest improvement in the 0–20 cm layer; the accuracy declined slightly with depth but remained superior to either data source alone. Our study improves soil moisture simulation accuracy and closes the knowledge gaps in multi-source data integration. This framework supports sustainable land management and irrigation policy in vulnerable farming regions. Full article

This study optimized the constants of the RNG k-ε model using the Ensemble Kalman Filter (ENKF) data assimilation method to improve the accuracy of air-lift plunger gap flow predictions. For high Reynolds number turbulent flow, we conducted numerical simulations integrating experimental data with a library of predicted data generated via optimal Latin hypercube sampling. ENKF was employed to assimilate these data and adjust the model constants, significantly reducing prediction errors and enhancing the accuracy of plunger models. Specifically, mean square errors for rectangular and circular plungers decreased from 60.67 and 61.48 to 7.12 and 7.20, respectively. The study also revealed significant changes in vortex dynamics and flow distribution following data assimilation, providing insights for optimizing plunger design and improving system energy efficiency. These findings underscore the potential of data assimilation in advancing oil and gas production. Full article

Rapid resource model updating with real-time data is important for making timely decisions in resource management and mining operations. This requires optimal merging of models and observations, which can be achieved through data assimilation, and the ensemble Kalman filter (EnKF) has become a popular method for this task. However, the modeled resources in mining usually consist of multiple variables of interest with multivariate relationships of varying complexity. EnKF is not a multivariate approach, and even for univariate cases, there may be slight deviations between its outcomes and observations. This study presents a methodology for rapidly updating multivariate resource models using the EnKF with multiple data assimilations (EnKF-MDA) combined with rotation-based iterative Gaussianization (RBIG). EnKF-MDA improves the updating by assimilating the same data multiple times with an inflated measurement error, while RBIG quickly transforms the data into multi-Gaussian factors. The application of the proposed algorithm is validated by a real case study with nine cross-correlated variables. The combination of EnKF-MDA and RBIG successfully improves the accuracy of resource model updates, minimizes uncertainty, and preserves the multivariate relationships. Full article

In modern agriculture, timely and accurate crop yield information is crucial for optimising agricultural production management and resource allocation. This study focused on improving the prediction accuracy of pear yields. Taking Alar City, Xinjiang, China as the research area, a variety of data including leaf area index (LAI), soil moisture (SM) and remote sensing data were collected, covering four key periods of pear growth. Three advanced algorithms, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF), were used to construct the regression models of LAI and vegetation index in four key periods using Sentinel-2 satellite remote sensing data. The results showed that the RF algorithm provided the best results when inverting the LAI. The coefficients of determination (R²) were 0.73, 0.72, 0.76, and 0.77 for the four periods, respectively, and the root-mean-square errors (RMSE) were 0.21 m²/m², 0.24 m²/m², 0.18 m²/m², and 0.16 m²/m², respectively. Therefore, the RF algorithm was selected as the preferred method for LAI inversion in this study. Subsequently, the study further explored the potential of data assimilation techniques in enhancing the accuracy of pear yield simulation. LAI and SM were incorporated into the World Food Studies (WOFOST) crop growth model by four assimilation algorithms, namely, the Four-Dimensional Variational Approach (4D-Var), Particle Swarm Optimisation (PSO) algorithm, Ensemble Kalman Filter (EnKF), and Particle Filter (PF) in separate and joint assimilation, respectively. The experimental results showed that the assimilated model significantly improved the accuracy of yield prediction compared to the unassimilated model. In particular, the EnKF algorithm provided the highest accuracy in yield estimation with R² of 0.82, 0.79 and RMSE of 1056 kg/ha and 1385 kg/ha when LAI alone and SM alone were assimilated, whereas 4D-Var performed the best when LAI and SM were jointly assimilated, with R² as high as 0.88, and the RMSE reduced to 923 kg/ha. In addition, it was found that assimilating LAI outperformed assimilating SM when assimilating one variable, whereas joint assimilation of LAI and SM further enhanced the predictive performance beyond that of assimilating one variable alone. In summary, the present study demonstrated great potential to provide strong support for accurate prediction of pear yield by effectively integrating LAI and SM into crop growth models through data assimilation. Full article