NG24A:
Advances in Data Assimilation, Predictability, and Uncertainty Quantification II

Submit an Abstract to this Session



Session ID#: 22354

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 ideas and advanced techniques for DA, UQ and predictability, for example: 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. Contributions are welcome 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 Carmen Blackwood, Jet Propulsion Laboratory, Pasadena, CA, United States
Chairs:  Brian C Ancell, Texas Tech Univ-Geosciences, Lubbock, TX, United States, Steven J Fletcher, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, Maggie Johnson, Norman, OK, United States and Daniel Limonadi, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
OSPA Liaison:  Brian C Ancell, Texas Tech Univ-Geosciences, Lubbock, TX, United States
Co-Organized with:
Nonlinear Geophysics, and Atmospheric Sciences

Cross-Listed:
  • A - Atmospheric Sciences
  • C - Cryosphere
  • H - Hydrology
  • OS - Ocean Sciences
Index Terms:

1910 Data assimilation, integration and fusion [INFORMATICS]
3260 Inverse theory [MATHEMATICAL GEOPHYSICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]
4468 Probability distributions, heavy and fat-tailed [NONLINEAR GEOPHYSICS]

Abstracts Submitted to this Session:

Bayesian Decision Support (Invited) (224690)
Mark Berliner, Retired, Washington, DC, United States
Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions (207908)
Nan Chen, New York University, New York, NY, United States and Andrew Majda, Courant Institute-NYU, New York, NY, United States
Handling Uncertainty in Palaeo-Climate Models and Data (Invited) (226662)
Jochen Voss1, Alan M Haywood1, Aisling M Dolan2 and Dario Domingo3, (1)University of Leeds, Leeds, United Kingdom, (2)University of Leeds, Leeds, LS2, United Kingdom, (3)University of Leeds, Department of Statistics, Leeds, United Kingdom
Using EFSO/PQC to Improve the Quality of Observations and Analyses (248003)
Tse-Chun Chen, University of Maryland College Park, College Park, MD, United States and Eugenia Kalnay, University of Maryland College Park, Department of Atmospheric and Oceanic Science, College Park, MD, United States
Predicting Statistical Response and Extreme Events in Uncertainty Quantification through Reduced-Order Models (207651)
Di Qi, Purdue University, Mathematics, West Lafayette, IN, United States and Andrew Majda, Courant Institute-NYU, New York, NY, United States

See more of: Nonlinear Geophysics