scholarly journals Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

2020 ◽  
Vol 125 (1) ◽  
Author(s):  
Shunji Kotsuki ◽  
Yousuke Sato ◽  
Takemasa Miyoshi
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Large Scale ◽  
Research Model ◽  
Model Parameter ◽  
Climate Research ◽  
Model Parameter Estimation

scholarly journals Soil model parameter estimation with ensemble data assimilation

2009 ◽  
Vol 10 (2) ◽  
pp. 127-131 ◽  
Author(s):  
Biljana Orescanin ◽  
Borivoj Rajkovic ◽  
Milija Zupanski ◽  
Dusanka Zupanski
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Soil Model

scholarly journals Model parameter estimation using coherent structure colouring

2018 ◽  
Vol 861 ◽  
pp. 886-900 ◽  
Author(s):  
Kristy L. Schlueter-Kuck ◽  
John O. Dabiri
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Coherent Structure ◽  
Large Scale ◽  
Numerical Models ◽  
Spatial Uncertainty ◽  
Chaotic Advection ◽  
Model Parameters ◽  
Model Parameter ◽  
Lagrangian Data

Lagrangian data assimilation is a complex problem in oceanic and atmospheric modelling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremely challenging. Temporal and spatial discretisation, factors necessary in modelling large scale flows, also contribute to separation between real and simulated drifter trajectories. The chaotic advection inherent in these turbulent flows tends to separate even closely spaced tracer particles, making error metrics based solely on drifter displacements unsuitable for estimating model parameters. We propose to instead use error in the coherent structure colouring (CSC) field to assess model skill. The CSC field provides a spatial representation of the underlying coherent patterns in the flow, and we show that it is a more robust metric for assessing model accuracy. Through the use of two test cases, one considering spatial uncertainty in particle initialisation, and one examining the influence of stochastic error along a trajectory and temporal discretisation, we show that error in the coherent structure colouring field can be used to accurately determine single or multiple simultaneously unknown model parameters, whereas a conventional error metric based on error in drifter displacement fails. Because the CSC field enhances the difference in error between correct and incorrect model parameters, error minima in model parameter sweeps become more distinct. The effectiveness and robustness of this method for single and multi-parameter estimation in analytical flows suggest that Lagrangian data assimilation for real oceanic and atmospheric models would benefit from a similar approach.

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

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