H11N:
Understanding the Interface Between Models and Data I

Monday, 15 December 2014: 8:00 AM-10:00 AM
Chairs:  Benjamin L Ruddell, Arizona State University, Tempe, AZ, United States and Kenneth W Harrison, UMD ESSIC/NASA GSFC, Greenbelt, MD, United States
Primary Conveners:  Grey S Nearing, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Co-conveners:  Benjamin L Ruddell, Arizona State University, Tempe, AZ, United States, Jasper A Vrugt, UC-Irvine, Irvine, CA, United States and Kenneth W Harrison, UMD ESSIC/NASA GSFC, Greenbelt, MD, United States
OSPA Liaisons:  Grey S Nearing, NASA Goddard Space Flight Center, Greenbelt, MD, United States

Abstracts Submitted to this Session:

8:00 AM
H11N-01
 
How Does Higher Frequency Monitoring Data Affect the Calibration of a Process-Based Water Quality Model?
Leah Jackson-Blake, James Hutton Institute, Aberdeen, AB15, United Kingdom
8:15 AM
H11N-02
 
Analysing the Information Content of Point Measurements of the Vadose Zone State Variables for the Inverse Estimation of Soil Hydraulic Parameters
Stefan Werisch, Dresden University of Technology, Dresden, Germany and Franz Lennartz, United Arab Emirates University, Al Ain, United Arab Emirates
8:30 AM
H11N-03
 
Parametric uncertainty quantification of large complex dynamical system models
Qingyun Duan, Wei Gong, Zhenhua Di, Chen Wang, Jiping Quan, Yanjun Gan and Jianduo Li, Beijing Normal University, Beijing, China
8:45 AM
H11N-04
 
Model-free data analysis for source separation based on Non-Negative Matrix Factorization and k-means clustering (NMFk)
Velimir V Vesselinov and Boian Alexandrov, LANL, Santa Fe, NM, United States
9:00 AM
H11N-05
 
Density Estimation Framework for Model Error Assessment
Khachik Sargsyan1, Zhen Liu2, Habib N Najm1, Cosmin Safta2, Bart VanBloemenWaanders2, Hope A Michelsen1 and Ray Bambha1, (1)Sandia National Laboratories, Livermore, CA, United States, (2)Sandia National Laboratories, Albuquerque, NM, United States
9:15 AM
H11N-06
 
The Interface Between Data and Predictions through Machine Learning and Bayesian Networks
Michael N Fienen, USGS Wisconsin Water Science Center, Middleton, WI, United States and Bernard T Nolan, USGS Headquarters, Reston, VA, United States
9:30 AM
H11N-07
 
Comparing Linear and Nonlinear Methods for More Reliable Predictive Uncertainty Quantification and Optimal Design of Experiments
Thomas Wöhling1, Andreas Geiges1, Moritz Gosses1 and Wolfgang Nowak2, (1)University of Tübingen, Tübingen, Germany, (2)University of Stuttgart, Stuttgart, Germany
9:45 AM
H11N-08
 
Application of the Discrimination Inference to Reduce Expected Cost Technique (DIRECT) to a Contaminant Transport Problem.
Timothy West Bayley, Organization Not Listed, Washington, DC, United States and Ty P.A. Ferré, University of Arizona, Tucson, AZ, United States
 
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