Variational assimilation of radar reflectivities in a cirrus model. II: Optimal initialization and model bias estimation

2003 ◽  
Vol 129 (587) ◽  
pp. 301-319 ◽  
Author(s):  
Angela Benedetti ◽  
Graeme L. Stephens ◽  
Tomislava Vukićević
Keyword(s):  
Bias Estimation ◽  
Model Bias ◽  
Variational Assimilation

scholarly journals Nonlinear Conditional Model Bias Estimation for Data Assimilation

2021 ◽  
Vol 20 (1) ◽  
pp. 299-332
Author(s):  
Jason A. Otkin ◽  
Roland W. E. Potthast ◽  
Amos S. Lawless
Keyword(s):  
Data Assimilation ◽  
Bias Estimation ◽  
Model Bias ◽  
Conditional Model

Separate-bias estimation with reduced-order Kalman filters

1998 ◽  
Vol 43 (7) ◽  
pp. 983-987 ◽  
Author(s):  
D. Haessig ◽  
B. Friedland
Keyword(s):  
Kalman Filters ◽  
Bias Estimation ◽  
Reduced Order

An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Lei Shi ◽  
Ren-Jye Yang ◽  
Ping Zhu
Keyword(s):  
Response Surface ◽  
Bias Correction ◽  
Adaptive Response ◽  
Large Scale ◽  
Correction Method ◽  
Data Uncertainty ◽  
Correction Function ◽  
Surface Model ◽  
Model Bias ◽  
Adaptive Response Surface Method

The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.

Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics

2015 ◽  
Vol 30 (4) ◽  
Author(s):  
Valeriy I. Agoshkov ◽  
Eugene I. Parmuzin ◽  
Vladimir B. Zalesny ◽  
Victor P. Shutyaev ◽  
Natalia B. Zakharova ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Heat Flux ◽  
Baltic Sea ◽  
Satellite Observation ◽  
Sea Surface ◽  
Observation Data ◽  
The Baltic Sea ◽  
Variational Assimilation ◽  
The Baltic ◽  
The Mathematical Model

AbstractA mathematical model of the dynamics of the Baltic Sea is considered. A problem of variational assimilation of sea surface temperature (SST) data is formulated and studied. Based on variational assimilation of satellite observation data, an algorithm solving the inverse problem of heat flux restoration on the interface of two media is proposed. The results of numerical experiments reconstructing the heat flux functions in the problem of variational assimilation of SST observation data are presented. The influence of SST assimilation on other hydrodynamic parameters of the model is considered.

scholarly journals Quantifying the attribution of model bias in simulating summer hot days in China with IAP AGCM 4.1

2016 ◽  
Vol 9 (6) ◽  
pp. 436-442 ◽  
Author(s):  
Zhao-Hui LIN ◽  
Zheng YU ◽  
He ZHANG ◽  
Cheng-Lai WU
Keyword(s):  
Model Bias ◽  
Hot Days

scholarly journals On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference

2018 ◽  
Vol 113 (522) ◽  
pp. 767-779 ◽  
Author(s):  
Sebastian Calonico ◽  
Matias D. Cattaneo ◽  
Max H. Farrell
Keyword(s):  
Nonparametric Inference ◽  
Bias Estimation ◽  
Coverage Accuracy
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