Search Results (165)

Traditional 4-dimensional variational data assimilation methods have limitations due to the Gaussian distribution assumption of observation errors, and the gradient of the objective functional is vulnerable to observation noise and outliers. To address these issues, this paper proposes a non-Gaussian nonlinear data assimilation method called α-4DVar, based on Rényi entropy and the α-generalized Gaussian distribution. By incorporating the heavy-tailed property of Rényi entropy, the objective function and its gradient suitable for non-Gaussian errors are derived, and numerical experiments are conducted using the Lorenz-63 model. Experiments are conducted with Gaussian and non-Gaussian errors as well as different initial guesses to compare the assimilation effects of traditional 4DVar and α-4DVar. The results show that α-4DVar performs as well as traditional method without observational errors. Its analysis field is closer to the truth, with RMSE rapidly dropping to a low level and remaining stable, particularly under non-Gaussian errors. Under different initial guesses, the RMSE of both the background and analysis fields decreases quickly and stabilizes. In conclusion, the α-4DVar method demonstrates significant advantages in handling non-Gaussian observational errors, robustness against noise, and adaptability to various observational conditions, thus offering a more reliable and effective solution for data assimilation. Full article

Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two-phase loop analysis. Unlike surrogate-based or model-reduction strategies, our approach leverages full-model propagation without relying on computationally intensive sampling. The results demonstrate that KF and 3D-VAR exhibit robustness against varied noise types, intensities, and distributions, achieving significant uncertainty reduction in state variables and parameter estimation. The framework’s adaptability is further validated under oceanic conditions, suggesting its potential to augment baseline models beyond conventional extrapolation boundaries. These findings highlight DA’s capacity to improve model calibration, safety margin quantification, and reactor field reconstruction. By integrating high-fidelity simulations with real-world data corrections, the study establishes a scalable pathway to enhance the reliability of nuclear system predictions, emphasizing DA’s role in bridging theoretical models and operational demands without compromising computational efficiency. Full article

This research investigates how incorporating both radar radial velocity ($V_r$) and radar reflectivity influences the accuracy of tropical cyclone (TC) prediction. Different control variables are introduced to analyze their roles in $V_r$ data assimilation, while background-dependent radar reflectivity assimilation methods are also applied. Using Typhoon “Doksuri” (2023) as a primary case study and Typhoon “Kompasu” (2021) as a supplementary case, the Weather Research and Forecasting (WRF) model’s three-dimensional variational assimilation (3DVAR) is utilized to assimilate $V_r$ and reflectivity observations to improve TC track, intensity, and precipitation forecasts. Three experiments were conducted for each typhoon: one with no assimilation, one with $V_r$ assimilation using $\psi -\chi$ control variables and background-dependent radar reflectivity assimilation, and one with $V_r$ assimilation using $U-V$ control variables and background-dependent radar reflectivity assimilation. The results show that assimilating $V_r$ enhances small-scale dynamics in the TC core, leading to a more organized and stronger wind field. The experiment involving $U-V$ control variables consistently showed advantages over the $\psi -\chi$ scheme in aspects such as overall track prediction, initial intensity representation, and producing more stable or physically plausible intensity trends, particularly evident when comparing both typhoon events. These findings highlight the importance of optimizing control variables and assimilation methods to enhance the prediction of TCs. 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

This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a Probability of Detection (POD) of 0.92 and a Threat Score (TS) of 0.72 for rainfall estimates in the Chi Basin. Rainfall data from the Weather Research and Forecasting (WRF) numerical models were used as inputs for the HEC-HMS model to simulate water inflows into the dam. To refine rainfall estimates, various microphysics schemes were tested, including WSM3, WSM5, WSM6, Thompson, and Thompson Aerosol-Aware. Among these, the Thomson Aerosol-Aware scheme demonstrated the highest accuracy, achieving an average POD of 0.96, indicating highly reliable rainfall predictions for the Lam Pao Dam catchment. The findings underscore the potential benefits of using satellite-derived meteorological data for rainfall estimation, particularly where installing and maintaining ground-based measurement stations is difficult, e.g., forests/mountainous areas. This research contributes to a better understanding of satellite-derived rainfall patterns and their influence on catchment hydrology for enhanced water resource analysis. Full article

To improve the prediction of Typhoon Doksuri (2023), this paper explores how variations in horizontal scale factors used in assimilating radar-derived wind velocities influence the performance of numerical simulations and forecasts. Using the WRF-ARW model in conjunction with the WRF-3DVAR data assimilation system, two assimilation configurations were tested with horizontal length scale factors of 1.0 and 0.25. Results show that a reduced length scale facilitates a more detailed reconstruction of mesoscale features, including the typhoon’s eye and inner-core circulation, leading to improved accuracy in short-term intensity and structure forecasts. The experiment utilizing the 0.25 length scale exhibited a tighter warm core, stronger cyclonic wind bands, and a better representation of the vortex’s three-dimensional structure. However, this configuration also led to growing forecast deviations in the latter stages, likely due to imbalances introduced by excessive localization. In contrast, the 1.0-scale experiment produced smoother but less accurate structures and demonstrated larger track deviations. These findings highlight a key trade-off between localized observational influence and long-term forecast stability. The study underscores the importance of optimizing horizontal scale parameterization in variational assimilation to enhance the forecasting accuracy of high-impact tropical cyclones and offers practical insights for operational forecasting systems in regions frequently affected by typhoon activity. Full article

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns. Full article

Temperature and water vapor density profiles in the troposphere (from the surface to 10 km) can be retrieved from a ground-based microwave radiometer (MWR) at high temporal and moderate vertical resolution. The back-propagation neural network (BPNN) algorithm is commonly deployed in ground-based microwave radiometers. Some studies have shown that the accuracy of the BPNN retrieval algorithm is affected by training data with large deviations. In this paper, an improved 1D-VAR method is proposed, which can effectively correct the bias; the results show that the improved 1D-VAR method can provide more accurate inversion results. Compared to the BPNN and 1D-VAR methods, the root mean square errors of temperature for the improved 1D-VAR method are reduced by $60.8\%$ and $29.4\%$ during daytime and by $54.2\%$ and $49.7\%$ during nighttime, respectively. Full article

Understanding the thermodynamic horizontal structure of the mesopause is essential for studying atmospheric wave dynamics and energy transport. However, conventional models like MSISE-00 exhibit some discrepancies from lidar observations in the mesopause. To obtain a more reliable horizontal temperature structure, this study integrates coordinated lidar observations from Urumqi, Yuzhong, and Yangbajing with models using a three-dimensional variational (3DVAR) data assimilation method to construct a high-resolution temperature field over northwestern China. The assimilated temperature profiles closely match lidar observations, with the RMSE (root mean square error) of residual reductions of 67.35% at Urumqi, 60.69% at Yuzhong, and 34.80% at Yangbajing. Independent validation at Korla showed a RMSE of residual reductions of 40.14%, confirming the model’s effectiveness. The thermodynamical horizontal structures of the mesopause obtained from this model were also analyzed. The lidar-based model for the mesopause extends the observation results from disparate lidar stations to the area between lidar stations and will contribute to a deeper understanding of upper atmospheric dynamics. 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

This study investigated advancements in atmospheric forecasting by integrating real-time observational data into the Weather Research and Forecasting (WRF) model through the WRF-Data Assimilation (WRF-DA) framework. By refining atmospheric models, we aimed to improve regional high-resolution wave and hydrodynamic forecasts essential for environmental management. Focused on southern Greece, including Crete, the study applied a 3D-Var assimilation technique within WRF, downscaling forecasting data from the Global Forecast System (GFS) to resolutions of 9 km and 3 km. The results showed a 4.7% improvement in wind speed predictions, with significant gains during forecast hours 26–72, enhancing model accuracy across METAR validation locations. These results underscore the positive impact of the integration of additional observational data on model accuracy. This study also highlights the utility of refined atmospheric models for real-world applications through their use in forcing ocean circulation and wave models and subsequent Digital Twin of the Ocean applications. Two such applications—optimal ship routing to minimize CO₂ emissions and oil spill trajectory forecasting to mitigate marine pollution—demonstrate the practical utility of improved models through what-if scenarios in easily deployable, containerized formats. Full article

Understanding boundary-layer atmospheric temperature and moisture is essential for advancing our knowledge of the Earth system. This study adopts a one-dimensional variational (1DVAR)-based technique to integrate spaceborne measurement and ground-based observations for improving the retrieval of low-level atmospheric profiles. The performance of the algorithm under different atmospheric and observational scenarios, such as surface-air and skin temperature differences (∆T), surface pressure (Ps), and satellite zenith angle, respectively, has been systematically evaluated using the Geosynchronous Interferometric Infrared Sounder (GIIRS) on board the Fengyun-4A satellite as an example. Through theoretical information analysis, using both simulated and actual data experiments, this study demonstrates that incorporating ground-based temperature and moisture observations significantly enhances retrieval accuracy with 1DVAR, particularly over elevated terrain. The new algorithm is more effective in low-level temperature retrievals when air temperatures are colder relative to surface-skin temperatures, and it also shows greater benefit for water-vapor retrievals when the temperature difference between the air and the skin is minimal. However, as the zenith angle increases to 55°, the accuracy of temperature retrievals deteriorates, although this is mitigated by the combination of surface-air temperature observations. Notably, the positive impact of surface observations extends to approximately 100–200 hPa above the surface, underscoring the importance of accurate ground-based measurements in conjunction with spaceborne data for atmospheric profiling. Full article

Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations. Full article

This paper introduces an efficient approach for implementing the Four-Dimensional Variational Ensemble Kalman Filter (4D-EnKF) for non-linear data assimilation, leveraging a modified Cholesky decomposition (4D-EnKF-MC). In this method, control spaces at observation times are represented by full-rank square root approximations of background error covariance matrices, derived using the modified Cholesky decomposition. To ensure global convergence, we integrate line-search optimization into the filter formulation. The performance of the 4D-EnKF-MC is evaluated through experimental tests using the Lorenz 96 model, and its accuracy is compared to that of a 4D-Var extension of the Maximum-Likelihood Ensemble Filter (4D-MLEF). Through Root Mean Square Error (RMSE) analysis, we demonstrate that the proposed method outperforms the 4D-MLEF across a range of ensemble sizes and observational network configurations, providing a robust and scalable solution for non-linear data assimilation in complex systems. Full article

Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the ‘localization’ of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R² = 0.7119, p < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R² = 0.5142, p < 0.01) and particle filter (PF) ET (R² = 0.5596, p < 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development. Full article