scholarly journals Parameter estimation and prediction for groundwater contamination based on measure theory

2015 ◽  
Vol 51 (9) ◽  
pp. 7608-7629 ◽  
Author(s):  
S. A. Mattis ◽  
T. D. Butler ◽  
C. N. Dawson ◽  
D. Estep ◽  
V. V. Vesselinov
Keyword(s):  
Parameter Estimation ◽  
Groundwater Contamination ◽  
Measure Theory ◽  
Estimation And Prediction

Use of MiniColumns for linear isotherm parameter estimation and prediction of benchtop column performance

2015 ◽  
Vol 1418 ◽  
pp. 94-102 ◽  
Author(s):  
William R. Keller ◽  
Steven T. Evans ◽  
Gisela Ferreira ◽  
David Robbins ◽  
Steven M. Cramer
Keyword(s):  
Parameter Estimation ◽  
Estimation And Prediction ◽  
Linear Isotherm ◽  
Column Performance

scholarly journals Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model

2012 ◽  
Vol 140 (12) ◽  
pp. 3956-3971 ◽  
Author(s):  
Xinrong Wu ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Anthony Rosati ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Surface Temperature ◽  
Parameter Optimization ◽  
General Circulation ◽  
Coupled Model ◽  
Model Parameters ◽  
Estimation And Prediction ◽  
Dependent Parameter ◽  
The Impact ◽  
Mean Square Errors

Abstract Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

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