NG23B:
Advances in Data Assimilation, Predictability, and Uncertainty Quantification: Phytobiome I

Submit an Abstract to this Session Please know the abstract submission deadline has passed. There are no exceptions to this deadline and no new abstracts can be submitted.
You can still search the program to see your colleague�s abstract submissions as well as the sessions submitted for the 2016 Fall Meeting. The final program will be released in early October.


Session ID#: 15920

Session Description:
Geophysical processes are typically sparsely observed, and their dynamics are nonlinear, so that the statistics are non-Gaussian. Successful and efficient data assimilation (DA), uncertainty quantification (UQ) and prediction require that these characteristics be directly addressed. This session focuses on nonlinear and non-Gaussian techniques for DA, UQ and predictability, including variational methods, Kalman filters, adjoint and ensemble sensitivity, hybrid techniques, Monte Carlo sampling, particle filters as well as linear approximations to non-Gaussian/nonlinear applications. We welcome contributions from all of the geosciences, as well as the mathematical and statistical communities to share ideas that can be applied across the disciplines of the AGU and the larger scientific community.
Primary Convener:  Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
Conveners:  Brian C Ancell, Texas Tech University, Lubbock, TX, United States, Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States and Derek J Posselt, University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States
Chairs:  Brian C Ancell1, Derek J Posselt2, Steven J Fletcher3 and Andrew S Jones3, (1)Texas Tech University, Lubbock, TX, United States(2)University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States(3)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
OSPA Liaison:  Brian C Ancell, Texas Tech University, Lubbock, TX, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • GC - Global Environmental Change
  • H - Hydrology
  • OS - Ocean Sciences
Index Terms:

1910 Data assimilation, integration and fusion [INFORMATICS]
3245 Probabilistic forecasting [MATHEMATICAL GEOPHYSICS]
3260 Inverse theory [MATHEMATICAL GEOPHYSICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]

Abstracts Submitted to this Session:

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem (Invited) (141099)
Noemi Petra1, Tiangang Cui2, Omar Ghattas3, Youssef Marzouk4, Peherstorfer Benjamin5 and Karen Willcox5, (1)University of California Merced, School of Natural Sciences, Merced, CA, United States, (2)ExxonMobil Upstream Research Company, Houston, TX, United States, (3)University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, United States, (4)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, United States, (5)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, United States
Combining a Complex Network Approach and a SEIR Compartmental Model to link Fast Spreading of Infectious Diseases with Climate Change (151566)
Frank Brenner, Peter Hoffmann and Norbert Marwan, Potsdam Institute for Climate Impact Research, Potsdam, Germany
Probabilistic weather analysis and prediction using the local particle filter (Invited) (117788)
Jonathan Poterjoy1,2, Ryan Sobash3, Jeffrey L Anderson4 and Louis Wicker2, (1)University of Maryland, Atmospheric and Oceanic Science, College Park, United States, (2)National Severe Storms Lab Norman, Norman, OK, United States, (3)NCAR/MMM, Boulder, CO, United States, (4)University Corporation for Atmospheric Research, Boulder, United States
Uncertainty in Operational Atmospheric Analyses and Re-Analyses (123162)
Rolf Langland, Naval Research Lab Monterey, Marine Meteorology, Monterey, CA, United States and Ryan N. Maue, WeatherBell Analytics, Atlanta, GA, United States
Embracing Complexity of Crop Phytobiomes with a Multidisciplinary Roadmap for Phytobiomes Research and an Industry-Academic Research Alliance (Invited) (195861)
Kellye Eversole, International Alliance for Phytobiomes Research, Bethesda, MD, United States
State Estimation and Prediction using Clustered Particle Filters (125181)
Yoonsang Lee, Courant Institute of Mathematical Sciences, NEW YORK, NY, United States and Andrew Majda, New York University, New York, NY, United States
Improving Forecasts Through Realistic Uncertainty Estimates: A Novel Data Driven Method for Model Uncertainty Quantification in Data Assimilation (155121)
Sahani Darshika Pathiraja1, Hamid Moradkhani2, Lucy Amanda Marshall1, Ashish Sharma3 and Gery Geenens4, (1)University of New South Wales, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)Portland State University, Portland, OR, United States, (3)University of New South Wales, Sydney, NSW, Australia, (4)University of New South Wales, School of Mathematics and Statistics, Sydney, Australia
What the Collapse of the Ensemble Kalman Filter Tells Us About Particle Filters (149647)
Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States, Daniel Hodyss, Naval Research Lab Monterey, Marine Meteorology, Monterey, CA, United States and Chris Snyder, National Center for Atmospheric Research, Boulder, CO, United States

See more of: Nonlinear Geophysics