NG22A:
Advances in Data Assimilation, Predictability, and Uncertainty Quantification I


Submit an Abstract to this Session


Session ID#: 57840

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 are directly addressed.   This session focuses on ideas and advances techniques for DA, UQ and predictability, for example variational methods, (ensemble) Kalman filtering, adjoint and ensemble sensitivities, hybrid techniques, NDEnVAR, representer based approaches, Monte Carlo sampling, particle Filters as well as linear approximations to non-Gaussian/nonlinear applications and welcomes 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 Univ-Geosciences, Lubbock, TX, United States, Derek J Posselt, NASA Jet Propulsion Laboratory, Pasadena, United States and Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States
Primary Liaison:  Steven J Fletcher, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
Chairs:  Steven J Fletcher, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, Brian C Ancell, Texas Tech Univ-Geosciences, Lubbock, TX, United States, Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States and Derek J Posselt, University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States
OSPA Liaison:  Steven J Fletcher, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
Co-Organized with:
Nonlinear Geophysics, and Atmospheric Sciences

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

Proposed Co-Organized Session with:
  • A - Atmospheric Sciences
Index Terms:

1910 Data assimilation, integration and fusion [INFORMATICS]
3275 Uncertainty quantification [MATHEMATICAL GEOPHYSICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]
4468 Probability distributions, heavy and fat-tailed [NONLINEAR GEOPHYSICS]

Abstracts Submitted to this Session:

Lower-Stratosphere Wind Predictions with an Analog Ensemble (375477)
Salvatore Candido1, Luca Delle Monache2 and Aakanksha Singh1, (1)Loon, Mountain View, CA, United States, (2)Scripps Institution of Oceanography, San Diego, CA, United States
Observation Informed Generalized Hybrid Error Covariance Models (Invited) (352773)
Elizabeth Satterfield, US Naval Research Laboratory, Marine Meteorology Division, Monterey, CA, United States, Daniel Hodyss, Naval Research Lab Monterey, Marine Meterology, Monterey, CA, United States, David Kuhl, Naval Research Laboratory, Remote Sensing Division, Washington, D.C., United States and Craig H Bishop, Naval Research Lab, Monterey, United States
The Dynamic Likelihood Filter (347018)
Juan M. Restrepo, Oregon State University, Mathematics, Corvallis, OR, United States
A Hybrid Kalman-Nonlinear Ensemble Transform Filter (377528)
Lars Nerger, Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research, Bremerhaven, Germany
Toward a Search Space Extension for the Analog Ensemble technique to Improve Probabilistic Predictions (459584)
Laura Clemente-Harding1, George s Young1, Guido Cervone2, Luca Delle Monache3 and Sue Ellen Haupt4, (1)Pennsylvania State University Main Campus, University Park, PA, United States, (2)Pennsylvania State University Main Campus, Department of Geography and Institute for Computational and Data Sciences, University Park, PA, United States, (3)Scripps Institution of Oceanography, San Diego, CA, United States, (4)National Center for Atmospheric Research, Boulder, CO, United States
Reconstructing past mantle flow with the adjoint method in the presence of a mismatched model (394476)
Lorenzo Colli, University of Houston, Department of Earth and Atmospheric Sciences, Houston, TX, United States, Hans-Peter Bunge, Ludwig-Maximilians-Universität München, Earth and Environmental Sciences, Munich, Germany and Jens Oeser, Ludwig Maximilians University of Munich, Munich, Germany

See more of: Nonlinear Geophysics