NG23A:
Advances in Data Assimilation, Predictability, and Uncertainty Quantification I

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Session ID#: 31490

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:  Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States, Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, Brian C Ancell, Texas Tech Univ-Geosciences, Lubbock, TX, United States and Carmen Blackwood, Jet Propulsion Laboratory, Pasadena, CA, United States
OSPA Liaison:  Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States

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:

Combining Gaussianization with Diffusion-based Correlation Modeling for Assimilating Ice Concentration Data (221732)
Max Yaremchuk1, Tamara L Townsend1, Gleb Panteleev1, David A Hebert2 and Richard Allard1, (1)Naval Research Lab, Stennis Space Center, MS, United States, (2)Naval Research Laboratory, Ocean Sciences Division, Stennis Space Center, MS, United States
Preservation of physical properties with Ensemble-type Kalman Filter Algorithms (Invited) (233106)
Tijana Janjic, Deutscher Wetterdienst (DWD), Offenbach am Main, Germany
Bayesian Lagrangian Data Assimilation and Drifter Deployment Strategies (299146)
Arkopal Dutt, Massachusetts Institute of Technology, Cambridge, MA, United States and Pierre F J Lermusiaux, Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States
Structural and parameteric uncertainty quantification in cloud microphysics parameterization schemes (288518)
Marcus van Lier Walqui, Columbia University, Center for Climate Systems Research, New York, United States, Hugh Morrison, NSF NCAR, MMM Laboratory, Boulder, United States, Matthew R Kumjian, Pennsylvania State University Main Campus, University Park, PA, United States, Olivier P Prat, CICS-NC/NCSU, Asheville, NC, United States and Charlotte Martinkus, Pennsylvania State University Main Campus, Meteorology and Atmospheric Science, University Park, PA, United States
Are consistent equal-weight particle filters possible? (255891)
Peter Jan van Leeuwen, University of Reading, Meteorology, Reading, United Kingdom
Sensitivity Analysis of Expected Wind Extremes over the Northwestern Sahara and High Atlas Region. (285259)
Elena Garcia-Bustamante1, Fidel J González-Rouco2 and Jorge Navarro1, (1)Ciemat, Madrid, Spain, (2)Universidad Complutense de Madrid, Astrofísica y CC. Atmósfera, Madrid, Spain

See more of: Nonlinear Geophysics