Search Results (209)

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO₂ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO₂ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO₂ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO₂ retrieval data, ODIAC 1 km anthropogenic CO₂ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO₂ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO₂ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO₂ and XCO₂ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO₂. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making. 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 investigates an extreme squall line event that occurred in northern Jiangxi Province, China on 30–31 March 2024. Based on the WRF model, convection-permitting ensemble forecast experiments were conducted using two distinct initial perturbation approaches, namely, the Local Breeding of Growing Modes (LBGM) and the Ensemble Transform Kalman Filter (ETKF), to compare their perturbation structures, spatiotemporal evolution, and precipitation forecasting capabilities. The experiments demonstrated the following: (1) The LBGM method significantly improved the root mean square error (RMSE) of mid-upper tropospheric variables, particularly demonstrating superior performance in low-level temperature field forecasts, but the overall ensemble spread of the system was consistently smaller than that of ETKF. (2) The evolution of dynamical spread within the squall line system confirmed that ETKF generated greater spread growth in low-level wind fields, while LBGM exhibited better spatiotemporal alignment between mid-upper tropospheric wind field spread and the synoptic system evolution. (3) Vertical profiles of total moist energy revealed that ETKF initially exhibited higher total moist energy than LBGM. Both methods showed increasing total moist energy with forecast lead time, displaying a bimodal structure dominated by kinetic energy in upper layers (300–100 hPa) and balanced kinetic energy and moist physics terms in lower layers (1000–700 hPa), with ETKF demonstrating larger growth rates. (4) Kinetic energy spectrum analysis indicated that ETKF exhibited significantly higher perturbation energy than LBGM in the 100–1000 km mesoscale range and superior small- to medium-scale perturbation characterization at the 6–60 km scales initially. Precipitation and radar echo verification showed that ETKF effectively corrected positional biases in precipitation forecasts, while LBGM more accurately reproduced the bow-shaped echo structure near Nanchang due to its precise simulation of leading-edge vertical updrafts and rear-sector low pseudo-equivalent potential temperature 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

This paper describes and implements a time-spatial correction of the regional prediction wave system, compared with altimeter data, by using an ensemble Kalman filter. The technique is successful in areas with substantial wave height around the Azores islands. Using winds from ERA5 and wave spectral boundary conditions from a prior study, the SWAN wave model generates wave conditions in the Azores area for 6 years. The time-spatial correction model is determined by comparing the hindcast data with the data from seven altimetry satellites: ERS-1, ERS-2, ENVISAT, TOPEX/POSEIDON, Jason-1, Jason 2, and GEOSAT Follow ON. The hindcast results are then corrected with the correction model. Furthermore, in situ buoy measurements are then employed to validate the corrected hindcast data. The outcomes demonstrate a significant improvement in the wave predictions. Full article

Precise and real-time estimation of vehicle mass and road slope plays a pivotal role in attaining accurate vehicle control. Currently, road slope estimation predominantly emphasizes longitudinal slopes, with limited research on intricate slopes that include both longitudinal roads and continuous turning up-and-down slopes. To address the limitations in existing road slope estimation research, this paper puts forward a novel joint-estimation approach for vehicle mass and road slope. Vehicle mass is initially estimated via M-estimation and recursive least squares with a forgetting factor (FFRLS). A road slope estimate approach, which utilizes interacting multiple models (IMM) and cubature Kalman filtering (CKF), is proposed for complex road slope scenarios. This algorithm integrates kinematic and dynamic vehicle models within the multi-model (MM) ensemble of the IMM filter. The kinematic vehicle model is appropriate for longitudinal road gradients, whereas the dynamic vehicle model is better suited for continuous turning up-and-down slope conditions. The IMM filter employs a stochastic process to weight the appropriate vehicle model according to the driving conditions. Consequently, the weights assigned by the IMM filter enable the algorithm to adaptively select the most suitable vehicle model, leading to more accurate slope estimates under complex conditions compared to single-model-based algorithms. Simulations were carried out using Matlab/Simulink2020-Trucksim2020 to verify the effectiveness of the proposed estimation approach. The results demonstrate that, compared with existing methods, the proposed estimation approach has achieved an improvement in the precision of evaluating vehicle mass and road gradient, thus confirming its superiority. Full article

Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean. 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

In this article, a novel prediction index is constructed, a hybrid filtering is proposed, and a remaining useful life (RUL) prediction framework is developed. In the proposed framework, different models are built for different operation states of rolling bearings. In the normal state, a linear model is built, and a Kalman filter (KF) is implemented to determine the failure start time (FST). In the degradation state, a dimensionless prediction index CRRMS is constructed, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold. Then, a double exponential model is established, and the hybrid filtering is proposed to estimate the future trend of CRRMS, which is combined by a particle filter (PF) and an unscented Kalman filter (UKF). At the same time, dynamic failure threshold technology is adaptively used to determine the failure thresholds of different bearings. Furthermore, the RUL is extrapolated at the moment the prediction index exceeds the failure threshold. Finally, the effectiveness and practicability of the proposed method is verified on the bearing dataset given by the PRONOSTIA platform. Full article

To evaluate the potential of an upcoming large-swath satellite for estimating surface methane (CH₄) fluxes at a weekly scale, we report the results from a series of observing system simulation experiments (OSSEs) that use an established modeling framework that includes the GEOS-Chem 3D atmospheric transport model and an ensemble Kalman filter. These experiments focus on the sensitivity of CH₄ flux estimates to systematic errors ($\mu$) and random errors ($\sigma$) in the column average methane (XCH₄) measurements. Our control test (INV_CTL) demonstrates that with median errors ($\mu$ = 1.0 ± 0.9 ppb and $\sigma$ = 6.9 ± 1.6 ppb) in XCH₄ measurements over a 1000 km swath, global CH₄ fluxes can be estimated with an accuracy of 5.1 ± 1.7%, with regional accuracies ranging from 3.8% to 21.6% across TransCom sub-continental regions. The northern hemisphere mid-latitudes show greater reliability and consistency across varying $\mu$ and $\sigma$ levels, while tropical and boreal regions exhibit higher sensitivity due to limited high-quality observations. In $\sigma$-sensitive regions, such as the North American boreal zone, expanding the swath width from 1000 km to 3000 km significantly reduces discrepancies, while such adjustments provide limited improvements for $\mu$-sensitive regions like North Africa. For TanSat-2 mission, with its elliptical medium Earth orbit and 1500 km swath width, the global total estimates achieved an accuracy of 3.1 ± 2.2%. Enhancing the swath width or implementing a dual-satellite configuration is proposed to further improve TanSat-2 inversion performance. Full article

Based on the “21.7” Henan extreme rainstorm case, this study investigates the influence of updating model state variables in the GSI-EnKF (Gridpoint Statistical Interpolation–ensemble Kalman filter) system with the Thompson microphysics scheme. Six sensitivity experiments are conducted to assess the impact of updating different model state variables on the EnKF analysis and subsequent forecast. The experiments include the Z_ALL experiment (updating all variables), the Z_NoEnv experiment (excluding dynamical and thermodynamical variables), the Z_NoNr experiment (excluding rainwater number concentration), and three additional experiments that examine the removal of updating horizontal wind (U, V), vertical wind (W), and perturbation potential temperature (T), which are marked as Z_NoUV, Z_NoW, and Z_NoT. The results indicate that updating different model state variables leads to various effects on dynamical, thermodynamical, and hydrometeor fields. Specifically, excluding the update of vertical wind or perturbation potential temperature has little effect on the rainwater mixing ratio, whereas excluding the update of the rainwater number concentration causes a significant increase in the rainwater mixing ratio, particularly in the northern region of Zhengzhou. Not updating horizontal wind or environmental variables shifts the rainwater mixing ratio northward, deviating from the observed rainfall center. The analysis of near-surface divergence and vertical wind also reveals that not updating certain variables could result in weaker or less detailed wind structures. Although radar reflectivity, which is mainly influenced by the mixing ratios of hydrometeors, shows consistent spatial distribution across experiments, their intensity varies, with the Z_ALL experiment showing the most accurate prediction. The 4 h deterministic forecasts based on the ensemble mean analysis demonstrate that updating all variables provides the best improvement in predicting the “21.7” Henan extreme rainstorm. These results emphasize the importance of updating all relevant model variables for improving predictions of extreme rainstorms. Full article

This paper examines the performance of Bayesian filtering system identification in the context of nonlinear structural and mechanical systems. The objective is to assess the accuracy and limitations of the four most well-established filtering-based parameter estimation methods: the extended Kalman filter, the unscented Kalman filter, the ensemble Kalman filter, and the particle filter. The four methods are applied to estimate the parameters and the response of benchmark dynamical systems used in structural mechanics, including a Duffing oscillator, a hysteretic Bouc–Wen oscillator, and a hysteretic Bouc–Wen chain system. Based on the performance, accuracy, and computational efficiency of the methods under different operating conditions, it is concluded that the unscented Kalman filter is the most effective filtering system identification method for the systems considered, with the other filters showing large estimation errors or divergence, high computational cost, and/or curse of dimensionality as the dimension of the system and the number of uncertain parameters increased. Full article