scholarly journals Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

2006 ◽  
Vol 7 (3) ◽  
pp. 548-565 ◽  
Author(s):  
Jasper A. Vrugt ◽  
Hoshin V. Gupta ◽  
BreanndánÓ Nualláin ◽  
Willem Bouten
Keyword(s):  
Data Assimilation ◽  
Simultaneous Optimization ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimization Approach ◽  
Streamflow Forecasting ◽  
Time Data ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Using Data

Abstract Operational flood forecasting requires that accurate estimates of the uncertainty associated with model-generated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimization and data assimilation method (SODA), uses an ensemble Kalman filter (EnKF) for recursive state estimation allowing for treatment of streamflow data error, model structural error, and parameter uncertainty, while enabling implementation of the Sacramento model without major modification to its current structural form. Model parameters are estimated in batch using the shuffled complex evolution metropolis stochastic-ensemble optimization approach (SCEM-UA). The SODA approach was implemented using parallel computing to handle the increased computational requirements. Studies using data from the Leaf River, Mississippi, indicate that forecast performance improvements on the order of 30% to 50% can be realized even with a suboptimal implementation of the filter. Further, the SODA parameter estimates appear to be less biased, which may increase the prospects for finding useful regionalization relationships.

scholarly journals Evaluating prior predictions of production and seismic data

2019 ◽  
Vol 23 (6) ◽  
pp. 1331-1347 ◽  
Author(s):  
Miguel Alfonzo ◽  
Dean S. Oliver
Keyword(s):  
Data Assimilation ◽  
Seismic Data ◽  
History Matching ◽  
Simulated Data ◽  
Model Parameters ◽  
Model Diagnostic ◽  
Diagonal Approximation ◽  
Data Coverage ◽  
Reservoir Simulation Model ◽  
Using Data

Abstract It is common in ensemble-based methods of history matching to evaluate the adequacy of the initial ensemble of models through visual comparison between actual observations and data predictions prior to data assimilation. If the model is appropriate, then the observed data should look plausible when compared to the distribution of realizations of simulated data. The principle of data coverage alone is, however, not an effective method for model criticism, as coverage can often be obtained by increasing the variability in a single model parameter. In this paper, we propose a methodology for determining the suitability of a model before data assimilation, particularly aimed for real cases with large numbers of model parameters, large amounts of data, and correlated observation errors. This model diagnostic is based on an approximation of the Mahalanobis distance between the observations and the ensemble of predictions in high-dimensional spaces. We applied our methodology to two different examples: a Gaussian example which shows that our shrinkage estimate of the covariance matrix is a better discriminator of outliers than the pseudo-inverse and a diagonal approximation of this matrix; and an example using data from the Norne field. In this second test, we used actual production, repeat formation tester, and inverted seismic data to evaluate the suitability of the initial reservoir simulation model and seismic model. Despite the good data coverage, our model diagnostic suggested that model improvement was necessary. After modifying the model, it was validated against the observations and is now ready for history matching to production and seismic data. This shows that the proposed methodology for the evaluation of the adequacy of the model is suitable for large realistic problems.

scholarly journals Simultaneous state-parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, applying classical prediction method will result in significant error. Data assimilation method offers a new way to merge multi-source data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide system. In this paper, simultaneous state-parameter estimation (SSPE) using particle filter-based data assimilation is applied to predict displacement of the landslide. Landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan province, China as experiment site to test SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of SSPE assimilation strategy in short-term landslide displacement estimation.

Estimation of FEM Model Parameters Using Data Assimilation and Its Application to an Electrical Machine

2016 ◽  
Vol 52 (3) ◽  
pp. 1-4 ◽  
Author(s):  
A. Bacchus ◽  
A. Tounzi ◽  
J.-P. Argaud ◽  
B. Bouriquet ◽  
M. Biet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Model Parameters ◽  
Electrical Machine ◽  
Fem Model ◽  
Using Data

scholarly journals Estimating DSGE model parameters in a small open economy: Do real-time data matter?

2015 ◽  
Vol 15 (1) ◽  
pp. 89-114 ◽  
Author(s):  
Jan Capek
Keyword(s):  
Real Time ◽  
Euro Area ◽  
Open Economy ◽  
Response Analysis ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Gdp Growth ◽  
Time Data ◽  
Data Revisions ◽  
Real Time Data

Abstract This paper investigates the differences between parameters estimated using real-time and those estimated with revised data. The models used are New Keynesian DSGE models of the Czech, Polish, Hungarian, Swiss, and Swedish small open economies in interaction with the euro area. The paper also offers an analysis of data revisions of GDP growth and inflation and trend revisions of interest rates. Data revisions are found to be unbiased and not autocorrelated in all countries. Inflation is usually measured more accurately in real-time than GDP growth, but this is not the case in the euro area. The results of the core analysis suggest that there are significant differences between parameter estimates using real-time data and those estimated using revised data. The model parameters that are most prone to significant differences between real-time and revised estimations are habit in consumption and persistence of domestic supply, of demand, and of world-wide technology shocks. The impulse response analysis suggests that the model behavior based on real-time and revised data is different.

scholarly journals Estimating model parameters of liquid rocket engine simulator using data assimilation

2020 ◽  
Vol 177 ◽  
pp. 373-385
Author(s):  
Daiwa Satoh ◽  
Seiji Tsutsumi ◽  
Miki Hirabayashi ◽  
Kaname Kawatsu ◽  
Toshiya Kimura
Keyword(s):  
Data Assimilation ◽  
Rocket Engine ◽  
Model Parameters ◽  
Liquid Rocket Engine ◽  
Using Data ◽  
Liquid Rocket

Learning missing part of physics-based models within a variational data assimilation scheme

2020 ◽  
Author(s):  
Arthur Filoche ◽  
Julien Brajard ◽  
Anastase Charantonis ◽  
Dominique Béréziat
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Automatic Differentiation ◽  
Variational Data Assimilation ◽  
Model Parameters ◽  
Time Data ◽  
Physical Knowledge ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

<p>The analogy between data assimilation and machine learning has already been shown and is still being investigated to address the problem of improving physics-based models. Even though both techniques learn from data, machine learning focuses on inferring model parameters while data assimilation concentrates on hidden system state estimation with the help of a dynamical model. <br> <br>Also, neural networks and more precisely ResNet-like architectures can be seen as dynamical systems and numerical schemes, respectively. They are now considered state of the art in a vast amount of tasks involving spatio-temporal forecasting. But to train such networks, one needs dense and representative data which is rarely the case in earth sciences. At the same time, data assimilation offers a proper Bayesian framework allowing to learn from partial, noisy and indirect observations. Thus, each of this field can profit from the other by providing either a learnable class of dynamical models or dense data sets.</p><p>In this work, we benefit from powerful and flexible tools provided by the deep learning community based on automatic differentiation that are clearly suitable for variational data assimilation, avoiding explicit adjoint modelling. We use a hybrid model divided into 2 terms. The first term is a numerical scheme that comes from the discretisation of physics-based equations, the second is a convolutional neural network that represents the unresolved part of the dynamics. From the Data Assimilation point of view, our network can be seen as a particular parametrisation of the model error. We then jointly learn this parameterisation and estimate hidden system states within a variational data assimilation scheme. Indirectly, the issue of incorporating physical knowledge into machine learning models is also addressed. </p><p>We show that the hybrid model improves forecast skill compared to traditional data assimilation techniques. The generalisation of the method on different models and data will also be discussed.</p>

Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation

2014 ◽  
Vol 15 (1) ◽  
pp. 359-375 ◽  
Author(s):  
Gift Dumedah ◽  
Jeffrey P. Walker
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Open Loop ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Model Parameter ◽  
High Soil Moisture ◽  
Using Data ◽  
Parameter Values ◽  
Moisture Estimation

Abstract Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation (EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.

scholarly journals Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

2019 ◽  
Vol 19 (7) ◽  
pp. 1387-1398 ◽  
Author(s):  
Jing Wang ◽  
Guigen Nie ◽  
Shengjun Gao ◽  
Changhu Xue
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Prediction Method ◽  
State Parameter ◽  
Joint Estimation ◽  
Model Parameters ◽  
Displacement Estimation ◽  
Landslide Stability ◽  
Landslide Displacement Prediction ◽  
Using Data

Abstract. Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, and applying a classical prediction method will result in significant error. The data assimilation method offers a new way to merge multisource data with the model. However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide systems. In this paper, simultaneous state and parameter estimation (SSPE) using particle-filter-based data assimilation is applied to predict displacement of the landslide. A landslide SSPE assimilation strategy can make use of time-series displacements and hydrological information for the joint estimation of landslide displacement and model parameters, which can improve the performance considerably. We select Xishan Village, Sichuan Province, China, as the experiment site to test the SSPE assimilation strategy. Based on the comparison of actual monitoring data with prediction values, results strongly suggest the effectiveness and feasibility of the SSPE assimilation strategy in short-term landslide displacement estimation.

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