[PDF] Top 20 Generalized background error covariance matrix model (GEN_BE v2.0)
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Generalized background error covariance matrix model (GEN_BE v2.0)
... state background error statis- tics is a key component of data assimilation since it affects the impact observations will have on the ...the background error covariance matrix ... See full document
28
Background error covariance with balance constraints for aerosol species and applications in variational data assimilation
... online model with a regional meteorological model that is coupled to aerosol and chemistry models (Grell et ...The model domain with three spatial domains is shown in ... See full document
16
A posteriori error covariances in variational data assimilation
... The error in the optimal solution can be derived through the errors in the input data using the Hessian of the cost functional of an auxiliary DA ...the model) the covariance matrix of the ... See full document
10
On errors in variables estimation with unknown noise variance ratio
... Identifiability of the model is recovered by construct- ing two estimating equations corresponding to the two clusters. The idea generalizes to problems involv- ing more than two unknown parameters in the mea- ... See full document
6
Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance
... assimilation model, the observational errors need to be taken into ...main error sources of the absolute TEC value obtained from RO include multipath ef- fects, DCBs from GPS and low Earth orbit (LEO) ... See full document
12
A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data
... (WRF) model showed that RadarVar was effective at producing an analysis of re- flectivity pattern and intensity similar to the observed ...the background fields and the use of the static background ... See full document
21
Analysis error covariance versus posterior covariance in variational data assimilation
... The error in the optimal solution (or ‘analysis error’) is naturally defined as a difference between the solution u and the true state u t ; this error is quantified by the analysis error ... See full document
16
Comparison of extended and ensemble based Kalman filters with low and high resolution primitive equation ocean models
... the model state and a low rank approximation of the corresponding er- ror covariance matrix (generally obtained by applying an em- pirical functions analysis (EOF) on a set of model outputs), ... See full document
11
Distribution of sum of squares and products matrices for the generalized multilinear matrix-T model
... the covariance of U is ν−2 1 A ⊗ Ω, an mn × mn matrix, where ⊗ is the Kronecker product between two ...the covariance matrix to be finite, we need a restriction on ν, namely, ν > ...the ... See full document
19
On optimal solution error covariances in variational data assimilation problems
... lution model to estimate the model ...solution error is derived through the errors of the input ...solution error covariance operator through the covariance operators of the ... See full document
20
Representing model error in ensemble data assimilation
... representing model error in the operational ECMWF EDA is presented and compared to the standard SPPT ...The model uncertainties are rep- resented by adding perturbations to the model ... See full document
15
Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)
... each model level, correspond- ing to application of horizontal localization ...transform matrix C 0 is derived from the localization cor- relation matrix C in a similar way as for the static back- ... See full document
18
Construction of non-diagonal background error covariance matrices for global chemical data assimilation
... (background) error covariances that account for spatial correlations in both horizontal and vertical directions, and assess its impact on the assimilation of tropospheric ozone profiles from ...of ... See full document
18
Kalman Filteroptimal Estimator- A Tracker Development Approach
... model:This model is also called the process model or time propagation model which describes how Kalman filter states and error covariance matrix vary with ... See full document
5
Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
... pling error: inflation ...the background- error covariance ...analysis error is of minor impor- tance in offline approaches, because the ensemble members are not propagated forward in ... See full document
15
Bayesian and Non-Bayesian Estimation for Weibull Parameters Based on Generalized Type-II Progressive Hybrid Censoring Scheme
... the generalized Type-II PHCS and special cases are ...variance covariance matrix and the approximate confidence interval based on the asymptotic normality of the maximum likelihood estimators (MLEs) ... See full document
14
Model errors in tree biomass estimates computed with an approximation to a missing covariance matrix
... were, not statistically significant from the actual esti- mates of error (Table 5). This is spite of overestimating, by a factor of approximately two, the variances in the regression parameters for G. ulmifolia. ... See full document
14
Estimating model error covariance matrix parameters in extended Kalman filtering
... similar model error parameters appear in many other data assimilation methods as well, like, for instance, in the weak-constraint 4D-Var (Fisher et ...the model error is neglected, but ... See full document
9
Adolescent girls and young women living with HIV: preconception counseling strategies
... control. Generalized linear mixed models (GLMMs) in combination with an unstructured covariance matrix – to model the dependency between repeated measures – were used to compare intervention ... See full document
7
An approach for constructing parsimonious generalized Gaussian kernel regression models
... a generalized Gaussian kernel model, in which each kernel regressor has an individually tuned diagonal covariance ...a generalized kernel regression model has the potential of improving ... See full document
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