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[PDF] Top 20 Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

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Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation

... The analysis presented above is based on the hypothesis that the initial state of the system under observa- tion is known, and that it is only model parameters (boundary conditions, forcing terms, distributed ... See full document

11

On optimal solution error covariances in variational data assimilation problems

On optimal solution error covariances in variational data assimilation problems

... the variational DA problem for a nonlinear evo- lution model to estimate the model ...input data errors using the Hessian of the auxiliary DA ...coefficient estimation problem is considered in ... See full document

20

On the Use of Landsat 5 TM Satellite for Assimilating Water Temperature Observations in 3D Hydrodynamic Model of Small Inland Reservoir in Midwestern US

On the Use of Landsat 5 TM Satellite for Assimilating Water Temperature Observations in 3D Hydrodynamic Model of Small Inland Reservoir in Midwestern US

... of observations can help adjust the model better by providing benefits of individual monitoring technology within the model updating ...sensing data provide a spatially dense dataset of model variables at ... See full document

14

Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

... years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model ...condition estimation and a small ... See full document

16

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

Quasi-static ensemble variational data assimilation: a theoretical and numerical study with the iterative ensemble Kalman smoother

... future observations (with respect to the analy- sis time) in the ...the analysis at the end of the ...the parameter S improves the filtering eMSE on the unstable ...of observations, a ... See full document

20

Analysis error covariance versus posterior covariance in variational data assimilation

Analysis error covariance versus posterior covariance in variational data assimilation

... the analysis error covariance matrix (see, for example, Thacker, 1989; Rabier and Courtier, 1992; Fisher and Courtier, 1995; Yang et ...Gaussian data errors (which include observation and background/prior ... See full document

16

Using adjoint sensitivity as a local structure function in variational data assimilation

Using adjoint sensitivity as a local structure function in variational data assimilation

... with respect to the observations is 8% worse than the ones derived from the background (with no assim- ...no analysis (background field) but only for 3 times: IOP16, 18 and ...3D-Var ... See full document

9

On analysis error covariances in variational data assimilation

On analysis error covariances in variational data assimilation

... good estimation of the whole covariance matrix (rather than only its diagonal elements) one apparently needs an ensemble size much larger than n = ...this respect it looks somewhat surprising that the value ... See full document

28

Data Assimilation Methods for Neuronal State and Parameter Estimation

Data Assimilation Methods for Neuronal State and Parameter Estimation

... We also investigated the utility of variational techniques to recover the mechanisms of bursting. For these runs, we took our observations to be coarsely sampled at 0.1 ms, and our forward mapping is taken ... See full document

38

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods

... the analysis of a vectorial response such as the flood hydrograph, the Jacobian of the transformation can be cal- culated using the adjoint ...the parameter and observa- tions spaces brings out relevant ... See full document

15

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

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

... The data assimila- tion method offers a new way to merge multisource data with the ...However, data assimilation is still deficient in the ability to meet the demand of dynamic landslide sys- ... See full document

12

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

... PMCMC data assimilation meth- ods, we reminisce about the key differences of the EnKF and ...state analysis step, but rather assigns a likelihood to each parti- ...state analysis step of the ... See full document

32

Analytical Study for Optimal Time Parameter in Cellular Communication Data Analysis

Analytical Study for Optimal Time Parameter in Cellular Communication Data Analysis

... with respect to network evolving, result in huge heterogeneous data which are time variant, and dynamically ...status data for months before deleting, thus required a huge amount of storage ... See full document

5

Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution

Mixed Variational-Monte Carlo Assimilation of Streamflow Data in Flood Forecasting: the Impact of Observations Spatial Distribution

... streamflow data are assimilated at all the displayed ...whether data are or are not assimilated ...assimilating data locally, than in the 2 nd one (yellow circles), which assimilates streamflow ... See full document

8

divand -1.0: -dimensional variational data analysis for ocean observations

divand -1.0: -dimensional variational data analysis for ocean observations

... one-dimensional analysis with m = 1 ...the analysis has a discontinuous derivative at every observation location (black ...the analysis has a continuous ... See full document

17

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

... the parameter optimization shows promise with the simple coupled model to mitigate the model bias arising from dynamical core misfitting, further research is required to understand the impact of such model biases ... See full document

10

Variational data assimilation with the YAO platform for hydrological forecasting

Variational data assimilation with the YAO platform for hydrological forecasting

... the assimilation window to n = 3 days, we note an important decline of the prediction accuracy (Table ...the variational assimilation results in a lower Nash (= ...the variational ... See full document

6

Model error estimation in ensemble data assimilation

Model error estimation in ensemble data assimilation

... the estimation ac- curacy is investigated in (Mitchell and Houtekamer, 2002) and (Reichle et ...the observations which con- tain information about the model ... See full document

13

Variational Data Assimilation in the Mathematical Model of the Black Sea Dynamics

Variational Data Assimilation in the Mathematical Model of the Black Sea Dynamics

... years, data assimilation methods have been widely used in Earth sciences, providing the relationship between the two main components that permit monitoring of the environment ...are observations and ... See full document

13

Pattern statistics on Markov chains and sensitivity to parameter estimation

Pattern statistics on Markov chains and sensitivity to parameter estimation

... and parameter estimation, m should not be greater than h – 2 for a pattern of length h (as else, the observed frequency of the pattern will be included in the ...for parameter estimations are often ... See full document

13

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