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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. [ABSTRACT FROM AUTHOR] more...

Inverse theory and data assimilation methods are commonly used in earth and environmental science studies to predict unknown variables, such as the physical properties of underground rocks, from a set of measured geophysical data, like geophysical seismic or electromagnetic data. A new Bayesian approach based on the ensemble Kalman filter using Gaussian mixture models is presented to overcome the assumption of Gaussian distribution of the unknown variables commonly used in the data assimilation literature and to generalize the algorithm to inverse problems with multimodal probability distributions. In applications of subsurface characterization, the multimodality of the unknown variables is generally due to the presence of different rock types, also known as geological facies. In the proposed method, the weights of the Gaussian mixture model represent the facies proportions, and they follow a Markov chain model. The proposed Bayesian model generates the unknown model parameters conditioned on measured data using a Markov chain Monte Carlo sampler. The validity of the method is demonstrated on a data assimilation problem where the goal is to estimate the posterior distribution of the unknown rock density from a set of repeated measurements of acoustic wave velocity measured at different times. The proposed method provides accurate estimates with efficient computational times. [ABSTRACT FROM AUTHOR] more...

With the progress of information technology, precision irrigation technology has become the core of modern agriculture. In particular, technologies such as Internet of Things (IoT), Big Data and Artificial Intelligence (AI) have provided strong support for the intelligence of agricultural production. This paper focuses on the precise prediction of irrigation water use in the maize industry through Ensemble Kalman Filter (EnKF) and fuzzy optimization methods combined with the DSSAT (Decision Support System for Agrotechnology Transfer) model. We use the remote sensing data of land moisture and leaf area in the Yellow Huaihai Plain provided by Google Earth Engine (GEE), as well as the maize market data released by the Ministry of Agriculture (MOA), to make predictions through the EnKF-DSSAT and fuzzy optimization-DSSAT models. The results showed that these models achieved high accuracy of 98.11% and 97.78% in short-term and long-term forecasts, respectively, which were significantly better than the traditional models. We also introduce a Boltzmann machine-based fusion algorithm to improve the model convergence speed and prediction accuracy. Ultimately, this paper verifies the important influence of policy factors on long-term irrigation prediction and proposes an adaptive prediction model and policy recommendations, which provide innovative methods and technical support for the implementation of precision irrigation technology. more...

Abstract Local ensemble transform Kalman filter (LETKF)‐based Ocean Research Analysis (LORA) version 1.0 datasets for western North Pacific (WNP) and Maritime Continent (MC) regions (LORA‐WNP and ‐MC, respectively) are released through the JAXA‐RIKEN Ocean Analysis website. The LORA datasets are created using an eddy‐resolving LETKF‐based ocean data assimilation system with satellite sea‐surface temperature, salinity, and height data and with in‐situ temperature and salinity data assimilated daily. The LORA datasets include 128‐member ensemble analyses at the sea surface (2D), each term of mixed‐layer temperature and salinity budget equations, and the related variables (2D) such as mixed‐layer depth and heat and freshwater fluxes as well as system grid information and analysis ensemble mean and spread (3D), from August 2015 to January 2024 (as of June 2024). The LORA datasets are useful for geoscience research and practical applications, especially for particle tracking, boundary conditions of atmospheric models, and research on spatiotemporal variations in sea‐surface temperature and salinity. more...

Parameters of turbulence model have rarely been changed to solve flow around ship hulls. Since these values are determined using relatively simple flows such as two-dimensional flat plate, there may be other parameter sets which are more suitable for resolving complex turbulent flow around ship hulls. Present study investigates suitable parameters of the k − ω SST model, one of the most popular turbulence models among ship flow simulations, throughout the data assimilation with ensemble Kalman filter aiming to improve computational accuracy of stern wake. Five parameters of k − ω SST model are first selected and are subjected to data assimilation using two-dimensional NACA4412 airfoil with moderate separation under 13.87 degrees of attack angle. Then, assimilated value " β 1 = 0.16 (originally 0.075)" which controls the production and destruction of ω is applied to solve four different blunt hull forms under towed condition. Bulk flow and available turbulence quantities on the propeller plane are subjected to validation. Consequently, the model parameter " β 1 = 0.16 (originally 0.075)" has been found to improve axial velocity distribution in model scale on the propeller plane in four different blunt hull forms maintaining the change of total resistance coefficient less than 0.8% difference compared to the original parameter. This research is of its first kind to bring the idea of parameter adaptation of turbulence model via data assimilation in the field of ship hydrodynamics. [ABSTRACT FROM AUTHOR] more...

Taiwan faces intense rainfall during typhoon seasons, leading to rapid increases in water level in rivers. Accurate flood forecasting in rivers is essential for protecting lives and property. The objective of this study is to develop a river flood forecasting model combining multiple additive regression trees (MART) and ensemble Kalman filtering (EnKF). MART, a machine learning technique, predicts water levels for internal boundary conditions, correcting a one-dimensional (1D) unsteady flow model. EnKF further refines these predictions, enabling precise real-time forecasts of water levels in the Danshui River system for up to three hours lead time. The model was calibrated and validated using observed data from four historical typhoons to evaluate its accuracy. For the present time at three water level stations in the Danshui River system, the root mean square error (RMSE) ranged from 0.088 to 0.343 m, while the coefficient of determination (R2) ranged from 0.954 to 0.999. The validated model (module 1) was divided into two additional modules: module 2, which combined the ensemble unsteady flow model with inner boundary correction and MART, and module 3, which featured an ensemble 1D unsteady flow model without inner boundary correction. These modules were employed to forecast water levels at three stations from the present time to 3 h lead time during Typhoon Muifa in 2022. The study revealed that the Tu-Ti-Kung-Pi station was less affected by inner boundaries due to significant tidal influences. Consequently, excluding the upstream and downstream boundaries, Tu-Ti-Kung-Pi station showed a superior RMSE trend from present time to 3 h lead time across all three modules. Conversely, the Taipei Bridge and Bailing Bridge stations began using inner boundary forecast values for correction from 1 h to 3 h lead times. This increased the uncertainty of the inner boundary, resulting in higher RMSE values for these locations in modules 1 and 2 compared to module 3. [ABSTRACT FROM AUTHOR] more...

Given the importance of input data, particularly precipitation, in hydrologic modeling for streamflow simulation, there has been growing emphasis on developing frameworks that harness multiple data sources concurrently to achieve more precise results. In the proposed framework of this study, which relies on the integrated capabilities of the multiobjective optimization model [nondominated sorting genetic algorithm-II (NSGA-II)], the ensemble Kalman filter data assimilation method, and data fusion, rainfall data from multiple sources are incorporated. The utilized framework leads to an improvement in the mean absolute relative error (MARE) index of streamflow simulation results. The innovation of the proposed methodology is the calculation of optimal weights corresponding to the simulated runoff time-series in the fusion model. This is accomplished through a competitive process among a multitude of optimized scenarios simulated within the framework provided. MARE as the main index identified in the objective functions and standard deviation, centered root mean square distance, and the correlation coefficient as auxiliary indices have been considered in this process. In this framework, satellite-based and in situ precipitation data sets are used as the forcing data. The main challenge has been to choose the greatest scenario for fusion among the selected scenarios, which the proposed methodology has overcome. The performance of the suggested methodology is demonstrated for the Siakh-Darengon catchment located in the Fars Province of Iran. According to the results, an average of 14.07% improvement in the MARE index has been achieved after applying the proposed methodology. By utilizing the proposed method, satellite-based rainfall data are integrated alongside ground-based rainfall data in the flood modeling process, resulting in enhanced accuracy in simulation outcomes within the utilized watersheds. Practical Applications: Today, influenced by factors such as climate change and anthropogenic alterations to the environment, the issue of flooding and its associated hazards has garnered unprecedented attention from researchers. One of the crucial components in flood modeling is rainfall data, which are collected through various means such as ground stations and satellite sensing instruments. In the past, the primary focus in the process of flood modeling has been on rainfall data recorded at ground stations; nowadays, efforts are being made to further enhance the role of satellite-derived rainfall data in flood modeling, aiming at enhancing their precision. In this study, a fusion model has been developed using the data fusion method and simultaneous utilization of ground-based and satellite rainfall data. Various flood simulation scenarios have been generated using a multiobjective optimization model, and the best scenario is selected through a competitive process. By implementing the proposed methodology in the Siakh-Darengon watershed located in Fars Province, Iran, improvements in simulation results have been achieved, resulting in based on the calculated performance indicators. [ABSTRACT FROM AUTHOR] more...

The conventional ocean data assimilation process typically involves assimilating hydrographic data, such as temperature and salinity measurements, obtained from both satellites and in-situ observations. This study introduces a novel approach to enhance ocean circulation modeling by assimilating surface geostrophic currents derived from satellite altimetry data using the ensemble Kalman filter. To match the time scales for the variability in the observed surface geostrophic currents and the model currents, the current velocities from the model were low-pass filtered. The optimal cut-off period for the low-pass filter was determined to be 31 days in the East Sea. Eight sensitivity experiments were then conducted to examine the effects of observation error and low-pass filtering during the assimilation of surface geostrophic current data. Assimilation experiments with surface geostrophic current data improved surface currents but had minor negative impacts on the temperature and salinity when compared with assimilation experiments without surface geostrophic current data. Notably, the experiment with an observation error of 10 cm/s for the geostrophic current outperformed the other experiments. Surface geostrophic current assimilation improved the sea surface temperature during winter and effectively modified surface current patterns during autumn in the East Sea. Assimilating satellite-derived surface geostrophic currents in the ocean circulation model thus enhanced the accuracy of surface circulation simulation. This improvement in ocean analysis data offers significant benefits for understanding ocean climate change and for developing marine management strategies. [ABSTRACT FROM AUTHOR] more...

The ensemble Kalman filter is widely used in applications because, for highdimensional filtering problems, it has a robustness that is not shared, for example, by the particle filter; in particular, it does not suffer from weight collapse. However, there is no theory which quantifies its accuracy as an approximation of the true filtering distribution, except in the Gaussian setting. To address this issue, we provide the first analysis of the accuracy of the ensemble Kalman filter beyond the Gaussian setting. We prove two types of results: The first type comprises a stability estimate controlling the error made by the ensemble Kalman filter in terms of the difference between the true filtering distribution and a nearby Gaussian, and the second type uses this stability result to show that, in a neighborhood of Gaussian problems, the ensemble Kalman filter makes a small error in comparison with the true filtering distribution. Our analysis is developed for the mean-field ensemble Kalman filter. We rewrite the update equations for this filter and for the true filtering distribution in terms of maps on probability measures. We introduce a weighted total variation metric to estimate the distance between the two filters, and we prove various stability estimates for the maps defining the evolution of the two filters in this metric. Using these stability estimates, we prove results of the first and second types in the weighted total variation metric. We also provide a generalization of these results to the Gaussian projected filter, which can be viewed as a mean-field description of the unscented Kalman filter. [ABSTRACT FROM AUTHOR] more...

Accurate estimation of forecast‐error covariance matrices is an essential step in data assimilation, which becomes a challenging task for high‐dimensional data assimilation. The standard ensemble Kalman filter (EnKF) may diverge due to both the limited ensemble size and the model bias. In this article, we propose to replace the sample covariance in the EnKF with a statistically consistent high‐dimensional tapering covariance matrix estimator to counter the estimation problem under high dimensions. A high‐dimensional EnKF scheme combining covariance localization with the inflation method and an iterative update structure is developed. The proposed assimilation scheme is tested on the Lorenz‐96 model with spatially correlated observation systems. The results demonstrate that the proposed method could improve the assimilation performance under multiple settings. [ABSTRACT FROM AUTHOR] more...

Warm-sector rainstorms are highly localized events, with weather systems and triggering mechanisms are not obvious, leading to limited forecasting capabilities in numerical models. Based on the ensemble Kalman filter (PSU-EnKF) assimilation system and the regional mesoscale model WRF, this study conducted a simulation experiment assimilating all-sky infrared (IR) radiance for a warm-sector rainstorm in East China and investigated the positive impact of assimilating the Himawari-8 moisture channel all-sky IR radiance on the forecast of the rainstorm. Results indicate that hourly cycling assimilation of all-sky IR radiance can significantly improve the forecast accuracy of this warm-sector rainstorm. There is a notable increase in the Threat Score (TS), with the simulated location and intensity of the 3-hour precipitation aligning more closely with observations. These improvements result from the assimilation of cloud-affected radiance, which introduces more mesoscale convective information into the model's initial fields. The adjustments include enhancements to the moisture field, such as increased humidity and moisture transport, and modifications to the wind field, including the intrusion of mid-level cold air and the strengthening of low-level convergent shear. These factors are critical in improving the forecast of this warm-sector rainstorm event. [ABSTRACT FROM AUTHOR] more...

The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real‐time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill‐posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch‐wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B‐spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study's region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively. Plain Language Summary: Wet refractivity indices measure the amount of water vapor in the atmosphere, which are important for weather forecasting and the accuracy of satellite‐based positioning. Water vapor can affect how signals travel through the atmosphere, influencing weather predictions and positioning systems. Traditional methods to estimate these indices are often slow and complex. In this study, we introduced a data assimilation method to improve the efficiency and accuracy of these estimates. By integrating real‐time satellite observations with a background atmospheric model, we could quickly update the estimates of wet refractivity indices. We tested this method in western Europe using data from 893 Global Navigation Satellite System stations over 1 year. Our new approach significantly reduced errors, showing a major improvement in estimating wet refractivity indices compared to the considered numerical atmospheric model. Key Points: 3D distribution of wet refractivity indices has been estimated using an Ensemble‐based methodThe GPT3w model serves as the background model, and the assimilation of Global Navigation Satellite System zenith wet delay measurements has been conductedThe results show the superior performance of the proposed method compared to the considered empirical and numerical atmospheric models [ABSTRACT FROM AUTHOR] more...

Accurate prediction of soil organic carbon stock (SOCS) dynamics in areas with intensive human activities is crucial for developing sustainable soil management practices and climate change mitigation strategies. This study investigated the spatiotemporal dynamics of SOCS by collecting a total of 1219 topsoil samples in southern Jiangsu Province of China in 1980, 2000 and 2015, and compared the performance of three predictive models: random forest (RF), RothC, and a hybrid model of RF‐RothCEnKF. The hybrid model integrated outputs from the process‐based RothC model and the data‐driven RF model using the Ensemble Kalman Filter (EnKF) for sequential model state updates. Results showed that the three models presented similar spatial patterns of SOCS from 1980 to 2015, with relatively higher SOCS mainly distributed in the areas surrounding Taihu Lake. The mean SOCS change rates estimated by the RF‐RothCEnKF model represented an overall net increase of 0.04 t C ha−1 yr.−1 during that period. The RF‐RothCEnKF model exhibited high prediction accuracy, with an R2 of.52, a mean absolute error (MAE) of 7.38 t C ha−1, and a root mean square error (RMSE) of 9.13 t C ha−1 in 2015. This highlighted the RF‐RothCEnKF's ability to enhance performance when the individual RF model (R2 =.47, MAE = 7.66 t C ha−1, and RMSE = 9.42 t C ha−1) and the RothC (R2 =.13, MAE = 8.77 t C ha−1, and RMSE = 10.87 t C ha−1) fell short. Our findings may not only provide a framework for integrating process‐based and machine learning models to enhance the accuracy and adaptability of SOCS modelling in areas affected by intensive human activities, but also offer some guidance for developing sustainable agricultural practices and carbon management strategies in complex environmental settings. [ABSTRACT FROM AUTHOR] more...

Local ensemble transform Kalman filter (LETKF)‐based Ocean Research Analysis (LORA) version 1.0 datasets for western North Pacific (WNP) and Maritime Continent (MC) regions (LORA‐WNP and ‐MC, respectively) are released through the JAXA‐RIKEN Ocean Analysis website. The LORA datasets are created using an eddy‐resolving LETKF‐based ocean data assimilation system with satellite sea‐surface temperature, salinity, and height data and with in‐situ temperature and salinity data assimilated daily. The LORA datasets include 128‐member ensemble analyses at the sea surface (2D), each term of mixed‐layer temperature and salinity budget equations, and the related variables (2D) such as mixed‐layer depth and heat and freshwater fluxes as well as system grid information and analysis ensemble mean and spread (3D), from August 2015 to January 2024 (as of June 2024). The LORA datasets are useful for geoscience research and practical applications, especially for particle tracking, boundary conditions of atmospheric models, and research on spatiotemporal variations in sea‐surface temperature and salinity. [ABSTRACT FROM AUTHOR] more...

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R¯0 and R̲0 that bound the effective reproduction number R0, from below and above, that is R̲0≤R0≤R¯0. We prove that cholera tends to disappear when R¯0≤1, while when R̲0>1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera. more...

The paper presents a novel approach to enhancing the accuracy of turbulence modeling in nuclear reactor simulations, specifically addressing secondary flow in the fuel assembly of pressurized water reactors. The accurate prediction of secondary flow in the rod bundle channels of pressurized water reactors is crucial for the design and performance evaluation of nuclear fuel assemblies. Traditional numerical simulation methods have difficulty in striking a balance between computational cost and prediction accuracy. This paper addressed this challenge by calibrating the constants of the nonlinear eddy viscosity model (NLEVM) based on the high-fidelity flow field measurement data from a 5×5 rod bundle with a split mixing vaned spacer grid using a novel data assimilation strategy that incorporated the ensemble Kalman filter (EnKF) algorithm. This study enhanced the overall data assimilation strategy and the EnKF algorithm by introducing sensitivity-based deterministic sampling and correlation-length-based local adjustment, respectively. These modifications aimed to accelerate convergence and reduce residuals in the model constant calibration process. Besides, this study adopted the correlation coefficient between the predicted and measured flow fields as a criterion for judging whether the calibrated model constants improve the prediction accuracy of the secondary flow in the rod bundle channel, which is more in line with the actual phenomenon than the traditional relative error criterion. The calibrated NLEVM significantly improves the prediction accuracy of secondary flow in the rod bundle channels compared to the standard NLEVM. The similarity between the predicted flow field of the calibrated model and the experimentally observed flow field is improved, with the correlation coefficients of the full cross-section flow field improving to a greater extent the further away from the localized grids. The secondary flow structure of the subchannels predicted by the calibrated model agrees well with the experimental observations and can successfully predict the cross-sectional vortex structure that is not accurately predicted by the original model. These results demonstrate effectiveness in refining turbulence model predictions through the novel data assimilation strategy and the modified EnKF algorithm. This research represents a substantial contribution to computational fluid dynamics, particularly in the context of nuclear reactor applications. The innovative approach to calibrating turbulence models using data assimilation strategies paves the way for more accurate and reliable predictions of turbulent flows in complex geometries, with broad-reaching implications for various scientific and engineering disciplines. The study’s findings could be instrumental in enhancing the safety and efficiency of nuclear reactor operations and potentially applicable in other fields requiring precise turbulence modeling, such as aerospace engineering, climate modeling, and industrial process optimization. The integration of data assimilation strategies with traditional turbulence models opens new avenues for improving the fidelity of simulations in complex flow scenarios. more...

State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension. The performance of the extended LEnKF algorithm is illustrated by dynamic network embedding and dynamic Poisson spatial models. [ABSTRACT FROM AUTHOR] more...

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R0 and R0 that bound the effective reproduction number R0, from below and above, that is R0 ≤ R0 ≤ R0. We prove that cholera tends to disappear when R0 ≤ 1, while when R0 >1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera. [ABSTRACT FROM AUTHOR] more...

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. Plain Language Summary: The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Key Points: Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experimentsMoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed dataMoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission [ABSTRACT FROM AUTHOR] more...

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Surveillance of underwater for maritime warfare is traditionally being carried out by bearings-only tracking from many decades. The measurements used for state estimation here are nonlinear. Also the noise in the measurements and the process cannot be always Gaussian. The traditional nonlinear filtering algorithms like extended Kalman filter and modified gain extended Kalman filter use the linearisation of the system. The unscented Kalman filter (UKF) uses the sigma point approach based on Gaussian distribution to deal with nonlinearity. The particle filter (PF) uses the randomly generated particles based on the pdf of the state. PF is highly complex to implement and it also suffers from sample impoverishment. Hence, ensemble Kalman filter (EnKF) which is a simplified form of PF will be tried out for bearings-only tracking in this research work. The performance of EnKF is compared with PF and UKF and the results obtained using these filters in Matlab are presented. [ABSTRACT FROM AUTHOR] more...

Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability. Furthermore, we propose a method to significantly reduce the computational complexity required for CKF/CKS implementation in ECG processing. The denoising performance of the proposed filter was compared to those of various nonlinear Kalman-based frameworks involving the Extended Kalman filter/smoother (EKF/EKS), the unscented Kalman filter/smoother (UKF/UKS), and the ensemble Kalman filter (EnKF) that was recently proposed for ECG enhancement. In this study, we conducted a comprehensive evaluation and comparison of the performance of various nonlinear Kalman-based frameworks for ECG signal processing, which have been proposed in recent years. Our assessment was carried out on multiple normal ECG segments extracted from different entries in the MIT-BIH Normal Sinus Rhythm Database (NSRDB). This database provides a diverse set of ECG recordings, allowing us to examine the filters' denoising capabilities across various scenarios. By comparing the performance of these filters on the same dataset, we aimed to provide a thorough analysis and identification of the most effective approach for ECG denoising. Two kinds of noises were introduced to such segments: 1-stationary white Gaussian noise and 2-non-stationary real muscle artifact noise. For evaluation, four comparable measures namely the SNR improvement, PRD, correlation coefficient and MSEWPRD were employed. The findings demonstrated that the suggested algorithm outperforms the EKF/EKS, EnKF/EnKS, UKF/UKS methods in both stationary and nonstationary environments regarding SNR improvement, PRD, correlation coefficient and MSEWPRD metrics. [ABSTRACT FROM AUTHOR] more...

Water distribution network (WDN) models are a common decision support tool for understanding the behavior and performance of WDNs, aiding in the planning and management of WDN systems. The increasing availability of real-time data has recently promoted the exploration of Data Assimilation (DA) techniques to improve these models. However, flow, pressure and demand data are uncertain, particularly due to sensor characteristics such as precision and noise. An open question is to what extent DA can still improve hydraulic models when the data used to this end is uncertain. This paper proposes a three-step Ensemble Kalman Filter based DA approach for WDNs (3-EnKF-WDN), building on previous approaches, and advancing in two main fronts: the use of extended period simulation, and the use of pressure-dependent demand (PDD) analysis. Different scenarios considering uncertain sensor data, with varied precision and noise, are applied to two networks of different sizes, representative of real-world WDNs. The computational demand of the 3-EnKF-WDN method is also assessed. Results show that increasing sensor's precision and decreasing the noise in state measurements reduce model error, as expected. However, we also found that model errors: 1) are reduced more effectively by using 3-EnKF-WDN than by increasing sensors' precision; 2) are not reduced if certain noise thresholds are surpassed; 3) can be reduced without assimilating demand data if the WDNs are fully monitored with head sensors in all the nodes and flow sensors in all the links. [ABSTRACT FROM AUTHOR] more...

West Nile virus (WNV) is a mosquito-borne arbovirus that remains a persistent public health challenge in the USA, with seasonal outbreaks that can lead to severe cases. In this study, we detail a real-time prediction system for WNV that incorporates an adapted compartment model to account for the transmission dynamics among birds, mosquitoes and humans, including asymptomatic cases and the influence of weather factors. Using data assimilation techniques, we generate weekly WNV case forecasts for Colorado in 2023, providing valuable insights for public health planning. Comparative analyses underscore the enhanced forecast accuracy achieved by integrating weather variables into our models. more...

Abstract The calibration of reservoir models using production data can enhance the reliability of predictions. However, history matching often leads to only a few matched models, and the original geological interpretation is not always preserved. Therefore, there is a need for stochastic methodologies for history matching. The Ensemble Kalman Filter (EnKF) is a well-known Monte Carlo method that updates reservoir models in real time. When new production data becomes available, the ensemble of models is updated accordingly. The initial ensemble is created using the prior model, and the posterior probability function is sampled through a series of updates. In this study, EnKF was employed to evaluate the uncertainty of production forecasts for a specific development plan and to match historical data to a real field reservoir model. This study represents the first attempt to combine EnKF with an integrated model that includes a genuine oil reservoir, actual production wells, a surface choke, a surface pipeline, a separator, and a PID pressure controller. The research optimized a real integrated production system, considering the constraint that there should be no slug flow at the inlet of the separator. The objective function was to maximize the net present value (NPV). Geological data was used to model uncertainty using Sequential Gaussian Simulation. Porosity scenarios were generated, and conditioning the porosity to well data yielded improved results. Ensembles were employed to balance accuracy and efficiency, demonstrating a reduction in porosity uncertainty due to production data. This study revealed that utilizing a PID pressure controller for the production separator can enhance oil production by 59% over 20 years, resulting in the generation of 2.97 million barrels of surplus oil in the field and significant economic gains. more...

To improve the prediction performance of the RANS model for flow separation, the model constants of the SST turbulence model were recalibrated using the unsteady ensemble Kalman filter (EnKF) data assimilation (DA) combined with the particle image velocimetry (PIV) data of the flow around a NACA0012 aerofoil. The differences in prediction between steady DA and unsteady DA with different model constant perturbations and ensemble sizes were compared and analyzed. The results show that the unsteady simulation can enhance the robustness of the numerical simulation and improve the initial prediction distribution of the RANS model compared to the steady simulation. The steady DA has obvious defects for large model constant perturbation or small ensemble size. The unsteady DA is more robust and can obtain the optimal turbulence model constants with larger perturbation and smaller ensemble size, resulting in more accurate prediction of the flow fields. more...

Geothermal energy systems, such as heat pumps relying on aquifers, use renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, a heat‐tracing experiment is a typical field test that can help with the characterization of the subsurface. During a heat‐tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help characterize the hydrogeological properties. Previous monitoring tools have shortcomings, such as low‐resolution data and over‐smoothing; thus, they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the groundwater is extracted and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modelling (ResIPy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to an experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduced as the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move towards K values inferred with slug tests. [ABSTRACT FROM AUTHOR] more...