scholarly journals Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

2018 ◽  
Vol 54 (2) ◽  
pp. 1252-1280 ◽  
Author(s):  
S. Pathiraja ◽  
H. Moradkhani ◽  
L. Marshall ◽  
A. Sharma ◽  
G. Geenens
Keyword(s):  
Data Assimilation ◽  
Model Uncertainty ◽  
Uncertainty Estimation ◽  
Data Driven ◽  
Hydrologic Data

scholarly journals Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

2019 ◽  
Vol 11 (7) ◽  
pp. 858 ◽  
Author(s):  
Redouane Lguensat ◽  
Phi Huynh Viet ◽  
Miao Sun ◽  
Ge Chen ◽  
Tian Fenglin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Data Driven ◽  
Optimal Interpolation ◽  
Observation System ◽  
Orthogonal Functions ◽  
Sea Level Anomalies ◽  
Recent Developments

From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.

scholarly journals Data assimilation and uncertainty assessment in 3D geological modeling

2017 ◽  
Author(s):  
Daniel Schweizer ◽  
Philipp Blum ◽  
Christoph Butscher
Keyword(s):  
Data Assimilation ◽  
Information Entropy ◽  
Model Uncertainty ◽  
Model Space ◽  
Distance Measures ◽  
Geological Model ◽  
Multiple Model ◽  
Geological Data ◽  
3D Geological Modeling ◽  
Model Geometry

Abstract. The quality of a 3D geological model strongly depends on the type of integrated geological data, their interpretation and associated uncertainties. In order to improve an existing geological model and effectively plan further site investigation, it is of paramount importance to identify existing uncertainties within the model space. Information entropy, a voxel based measure, provides a method for assessing structural uncertainties, comparing multiple model interpretations and tracking changes across consecutively built models. The aim of this study is to evaluate the effect of data assimilation on model uncertainty, model geometry and overall structural understanding. Several geological 3D models of increasing complexity, incorporating different input data categories, were built for the study site Staufen (Germany). We applied the concept of information entropy in order to visualize and quantify changes in uncertainty between these models. Furthermore, we propose two measures, the Jaccard and the City-Block distance, to directly compare dissimilarities between the models. The study shows that different types of geological data have disparate effects on model uncertainty and model geometry. The presented approach using both information entropy and distance measures can be a major help in the optimization of 3D geological models.

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