Assimilating Observation Data into Hydrological Model with Ensemble Kalman Filter

2011 ◽  
Vol 255-260 ◽  
pp. 3632-3636 ◽  
Author(s):  
Jun Xiong ◽  
Xiao Lan Huang ◽  
Zeng Yan Cao
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Observation Data ◽  
Infiltration Process ◽  
Whole Process ◽  
New Strategy ◽  
Dynamic Stochastic ◽  
Posteriori Distribution ◽  
Priori Information ◽  
Conventional Optimization

The ensemble Kalman filter (EnKF) is employed to simulate of streamflow of a slope sub-catchment during the rainfall infiltration process. With this method the whole process is treated as a dynamic stochastic system, and its streamflow is taken as the variable to describe the state of system. Furthermore, it is coupled with a hydrology model to cope with system uncertainty. Thus, the dynamical estimation of hydrological parameters is performed; the model variables and their uncertainty are obtained simultaneously. Numerical examples show that this strategy can effectively deal with observation noises and can provide the inversion results and the posteriori distribution of the priori information together. Compared with the conventional optimization algorithm, the new strategy combined with EnKF shows better character of real time response and model reliability.

scholarly journals Multiple Target Tracking Based on Multiple Hypotheses Tracking and Modified Ensemble Kalman Filter in Multi-Sensor Fusion

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3118 ◽  
Author(s):  
Zequn Zhang ◽  
Kun Fu ◽  
Xian Sun ◽  
Wenjuan Ren
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Sensor Fusion ◽  
Ensemble Kalman Filter ◽  
Observation Data ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Numerical Studies ◽  
Multiple Hypotheses ◽  
Corrected Data

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.

scholarly journals Performance Characteristics of a Pseudo-Operational Ensemble Kalman Filter

2008 ◽  
Vol 136 (10) ◽  
pp. 3947-3963 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Prediction Models ◽  
North Pacific Ocean ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Data ◽  
Error Statistics ◽  
Ensemble Forecasts ◽  
Ensemble Mean

The 2-yr performance of a pseudo-operational (real time) limited-area ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemble-mean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Time-averaged correlations indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemble-mean error and the spread increases monotonically with forecast hour.

scholarly journals Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model

2014 ◽  
Vol 7 (4) ◽  
pp. 1451-1465 ◽  
Author(s):  
S. Skachko ◽  
Q. Errera ◽  
R. Ménard ◽  
Y. Christophe ◽  
S. Chabrillat
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Control Variable ◽  
Observation Data ◽  
Tracer Transport ◽  
Error Statistics ◽  
Data Set ◽  
Two Parameters ◽  
Assimilation System

Abstract. An ensemble Kalman filter (EnKF) assimilation method is applied to the tracer transport using the same stratospheric transport model as in the four-dimensional variational (4D-Var) assimilation system BASCOE (Belgian Assimilation System for Chemical ObsErvations). This EnKF version of BASCOE was built primarily to avoid the large costs associated with the maintenance of an adjoint model. The EnKF developed in BASCOE accounts for two adjustable parameters: a parameter α controlling the model error term and a parameter r controlling the observational error. The EnKF system is shown to be markedly sensitive to these two parameters, which are adjusted based on the monitoring of a χ2 test measuring the misfit between the control variable and the observations. The performance of the EnKF and 4D-Var versions was estimated through the assimilation of Aura-MLS (microwave limb sounder) ozone observations during an 8-month period which includes the formation of the 2008 Antarctic ozone hole. To ensure a proper comparison, despite the fundamental differences between the two assimilation methods, both systems use identical and carefully calibrated input error statistics. We provide the detailed procedure for these calibrations, and compare the two sets of analyses with a focus on the lower and middle stratosphere where the ozone lifetime is much larger than the observational update frequency. Based on the observation-minus-forecast statistics, we show that the analyses provided by the two systems are markedly similar, with biases less than 5% and standard deviation errors less than 10% in most of the stratosphere. Since the biases are markedly similar, they most probably have the same causes: these can be deficiencies in the model and in the observation data set, but not in the assimilation algorithm nor in the error calibration. The remarkably similar performance also shows that in the context of stratospheric transport, the choice of the assimilation method can be based on application-dependent factors, such as CPU cost or the ability to generate an ensemble of forecasts.

Using ensemble Kalman filter to determine parameters for computational crowd dynamics simulations

2018 ◽  
Vol 35 (7) ◽  
pp. 2612-2628 ◽  
Author(s):  
Fumiya Togashi ◽  
Takashi Misaka ◽  
Rainald Löhner ◽  
Shigeru Obayashi
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Ensemble Kalman Filter ◽  
Input Parameter ◽  
Observation Data ◽  
Pedestrian Flow ◽  
Content Type ◽  
Data Assimilation Technique

Purpose It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow. Design/methodology/approach The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted. Findings The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged. Practical implications This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers. Originality/value To the authors’ knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.

scholarly journals DATA ASSIMILATION METHOD FOR THE TASK OF PASSIVE IMPURITY PROPAGATION IN THE ATMOSPHERE BASED ON A DYNAMIC-STOCHASTIC APPROACH

2019 ◽  
Vol 6 (1) ◽  
pp. 94-100
Author(s):  
Marina Platonova ◽  
Ekaterina Klimova
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Stochastic Approach ◽  
Passive Impurity ◽  
Dynamic Stochastic ◽  
Inflation Factor ◽  
Impurity Propagation

In this paper, we consider the method of data assimilation for the problem the propagation of the concentration a passive impurity in the atmosphere. Classical approaches to solving such problems are described, features of the application of algorithms, their minuses and pros. Two algorithms are considered: the ensemble Kalman filter and the ensemble Kalmans moother. Various ways to improve the convergence of these algorithms, such as localization and inflation factor, are considered.

Ensemble Kalman Filter With Principal Component Analysis Assisted Sampling for Channelized Reservoir Characterization

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Byeongcheol Kang ◽  
Hyungjun Yang ◽  
Kyungbook Lee ◽  
Jonggeun Choe
Keyword(s):  
Principal Component Analysis ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Petroleum Engineering ◽  
Real Field ◽  
Model Parameters ◽  
Observation Data ◽  
Simulation Time

Ensemble Kalman filter (EnKF) is one of the widely used optimization methods in petroleum engineering. It uses multiple reservoir models, known as ensemble, for quantifying uncertainty ranges, and model parameters are updated using observation data repetitively. However, it requires a large number of ensemble members to get stable results, causing huge simulation time. In this study, we propose a sampling method using principal component analysis (PCA) and K-means clustering. It excludes poor ensemble with different geological trends to the reference so we can improve both speed and reliability of future predictions. A representative model, which is selected from candidate models of each cluster, has a role to choose proper ensemble for EnKF. For applying EnKF to channelized reservoirs, we compare cases with using 400, randomly picked 100, sampled 100 using Hausdorff distance, and sampled 100 by the proposed method. The proposed method shows improvements over the other cases compared. It gives stable uncertainty ranges and well-updated reservoir parameters after the assimilations. Randomly selected 100 ensemble members predict wrong reservoir performances, and 400 ensemble members exhibit too large uncertainty ranges with long simulation times. Even though more ensemble members are utilized, they provide worse results due to disturbance by improperly designed models. We confirm our sampling strategy in a real field case, PUNQ-S3, and it reduces simulation time as well as improves the future predictions for efficient and reliable history matching.

scholarly journals Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Haiyan Zhou ◽  
Liangping Li ◽  
J. Jaime Gómez-Hernández
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Transient State ◽  
Observation Data ◽  
Model Structure ◽  
Prior Model ◽  
State Observation ◽  
Normal Score ◽  
Observation Network

The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observation data, and it deteriorates as the number of observation piezometers decreases. It is also remarkable that, even when the prior model structure is wrong, the localized normal-score ensemble Kalman filter can produce acceptable results for a sufficiently dense observation network.

The Role of Geomechanical Observation in Continuous Updating of Thermal-Recovery Simulations With the Ensemble Kalman Filter

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1043-1056 ◽  
Author(s):  
A.. Azad ◽  
R.J.. J. Chalaturnyk
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Numerical Models ◽  
Thermal Recovery ◽  
Rock Properties ◽  
Observation Data ◽  
Oil Sand ◽  
Thermal Methods ◽  
Cyclic Steam Stimulation

Summary In-situ thermal methods such as steam-assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS) are widely used in oil-sand reservoirs. The physics of such thermal processes is generally well-understood, and it has been shown that rock properties are highly influenced by the geomechanical behavior of the reservoir during these recovery processes. Geomechanics improves the process dynamically, and its response can depict the progress of production within a reservoir. However, the potential of geomechanical monitoring is not usually practiced. With increased implementation of highly instrumented wells and communication technologies providing real-time monitoring data from different sources, combining available data into reservoir geomechanical simulations can improve updating numerical models and the prediction process. This research explores effective uses of geomechanical observation data for history matching and types of geomechanical observation sources adequate for thermal recovery. The ensemble Kalman filter (EnKF), combined with an iterative geomechanical coupled simulator, has been chosen as the data-assimilation algorithm to update the model continuously on the basis of geomechanical observations and production data. The results show that considering geomechanical modeling and observation improves history matching when geomechanical behavior plays a role in the process.

Application of the Ensemble Kalman Filter to a One-Dimensional Linear Advection Model

2015 ◽  
Vol 804 ◽  
pp. 287-290
Author(s):  
Somsiri Payakkarak ◽  
Dusadee Sukawat
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Ensemble Member ◽  
Observation Data ◽  
State Estimate ◽  
One Dimensional ◽  
Weather Forecasts

Data assimilation is used in numerical weather prediction to improve weather forecasts by incorporating observation data into the model forecast. The Ensemble Kalman Filter (EnKF) is a method of data assimilation which updates an ensemble of states to provide a state estimate and associated error at each step. The atmospheric model that is used in this research is a one-dimensional linear advection model. This model describes the motion of a scalar field as it is advected by a known speed field. The result shows that by selecting appropriate initial ensemble, model noise and measurement perturbations, it is possible to achieve a significant improvement in the EnKF results. The accuracy of the EnKF increases when the number of ensemble member grows. That is, the larger ensemble sizes perform better than those of smaller sizes.

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