This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF) and ensemble sm...
The covariance matrix estimated from the ensemble data assimilation always suffers from filter collapse because of the spurious correlations induced by the finite ensemble size. The localization technique is applied to ameliorate this issue, which ha...
The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results sugge...
Assimilation of cloud properties in the convective scale ensemble data assimilation system is one of the prime topics of research in recent years. Satellites can retrieve cloud properties that are important sources of information of the cloud and atm...
Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accu...
Predictive uncertainty analysis focuses on defensible variability in model projected values after estimation of the posterior parameter distribution. Inverse-style parameter estimation selects posterior parameters through history matching where param...
Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localizatio...
This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The conventional EAKF adopts linear and Gaussian assumptions, making it difficult to handle cross-component updates in strongly couple...
The selection of soil parameters relies heavily on the engineering experience of practitioners and frequently results in settlement predictions that deviate from field monitoring, especially for soft soil foundations. This study proposes a settlement...
By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or ph...
Soil moisture plays an important role in climate prediction and drought monitoring. Data assimilation, as a method of integrating multi-geographic spatial data, plays an increasingly important role in estimating soil moisture. Model prediction error,...
Model error is an important source of uncertainty that significantly reduces the accuracy of El Niño–Southern Oscillation (ENSO) prediction. In this study, ensemble coupled data assimilation was employed to estimate the tendency error of...
In this study, we conducted experiments to assess the forecasting capabilities for tropical cyclone (TC) genesis over the east sea of Vietnam using the ensemble-based data assimilation system (EPS-DA) by WRF-LETKF. These experiments covered forecast...
Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminish...
This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). T...
The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both underestimation and miscalculation of uncertainty in model parameters and prediction. This study foc...
This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-r...
Due to the model and sampling errors of the finite ensemble, the background ensemble spread becomes small and the error covariance is underestimated during filtering for data assimilation. Because of the constraint of computational resources, it is d...
The joint state and parameter estimation problem is an important issue in data assimilation. An adjoint free data assimilation method, namely analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation method, was developed to prov...
The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are r...
It is widely recognized that the initial ensemble describes the uncertainty of the variables and, thus, affects the performance of ensemble-based assimilation techniques, which is investigated in this paper with experiments using the Community Earth...
Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was...
Seawater intrusion in coastal aquifers is a worldwide problem exacerbated by aquifer overexploitation and climate changes. To limit the deterioration of water quality caused by saline intrusion, research studies are needed to identify and assess the...
An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as...
The evaluation of several climatological background-error covariance matrix (defined as the B matrix) estimation methods was performed using the ALADIN limited-area modeling data-assimilation system at a 4 km horizontal grid spacing. The B matrices c...
Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of S...
Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable considerati...
Assimilation of spatio-temporal data poses a challenge when allowing non-Gaussian features in the prior distribution. It becomes even more complex with nonlinear forward and likelihood models. The ensemble Kalman model and its many variants have prov...
An important step when using some data assimilation methods, such as the ensemble Kalman filter and its variants, is to calibrate its parameters. Also called hyper-parameters, these include the model and observation errors, which have previously been...
The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecastin...
We develop an assimilation method of high horizontal resolution sea surface temperature data, provided from the Moderate Resolution Imaging Spectroradiometer (MODIS-SST) sensors boarded on the Aqua and Terra satellites operated by National Aeronautic...
A four-dimensional ensemble variational assimilation system for FY-3A satellite data is constructed using the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar Sa...
Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of...
This study proposes a hybrid data assimilation method called the weighted modulated ensemble-based particle Kalman filter (WMPKF), which combines the analysis procedures of the particle filter (PF) and the gain ensemble transform Kalman filter (GETKF...
Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this appr...
In this study, the local unscented transform Kalman filter (LUTKF) proposed in the previous study estimates the state of the Weather Research and Forecasting (WRF) model through local analysis. Real observations are assimilated to investigate the ana...
Accurate tracking and forecasting of ash dispersal in the atmosphere and quantification of its uncertainty are of fundamental importance for volcanic risk mitigation. Numerical models and satellite sensors offer two complementary ways to monitor ash...
In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM) in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with th...
The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (E...
Accurate projection of floods is of great significance to safeguard economic and social development as well as people’s life and property. The development of hydrological models can improve the level of flood projection, however, the numerous u...
In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown...
In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NO
This study assesses the impact of assimilating remote sensing observations of significant wave height (SWH) and 10 m wind speed (WSPD) into the analysis using a weakly coupled data assimilation (WCDA) framework to improve ocean surface monitoring off...
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 framewor...
Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve th...
We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a...
The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling...
With the development of in-situ monitoring techniques, the ensemble Kalman filter (EnKF) has become a popular data assimilation method due to its capability to jointly update model parameters and state variables in a sequential way, and to assess the...
Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Fie...
Remote sensing fractional snow cover area (fSCA) has been increasingly used to get an improved estimate of the spatiotemporal distribution of snow water equivalent (SWE) through reanalysis using different data assimilation (DA) schemes. Although the...
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