Primary Convener:
Steven Fletcher, Cooperative Institute for Research in the Atmosphere

Convener:
Brian Ancell, Texas Tech Univ-Geosciences
Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego
Derek Posselt, University of California
Shahrzad Roshankhah, University of Utah
Hiroki Sone, University of Wisconsin
Wenfeng Li, Los Alamos National Laboratory

Chair:
Steven Fletcher, Cooperative Institute for Research in the Atmosphere
Shahrzad Roshankhah, University of Utah
Soyoung Ha, NCAR
Moha Gharamti, NSF National Center for Atmospheric Research

Geophysical processes are typically sparsely observed and the relations between observed quantities and geophysical variables are often nonlinear, so that the statistics are non-Gaussian. Data assimilation (DA), data fusion, machine learning (ML), uncertainty quantification (UQ) and predictability are important areas of active research in geoscience. This interdisciplinary session focuses on new ideas and advanced techniques across geoscience that improve the robustness and the efficiency of computational methods for fusing models and data. Our session is broad in the application areas, and numerical methods of interest include variational and ensemble DA, Markov chain Monte Carlo, ensemble-based and adjoint sensitivity, observation impact and targeting, particle filters, deep/reinforcement learning, inversion techniques, deep/statistical/machine learning from scarce data, control variable transforms/pre-conditioning. We also welcome contributions to new computational infrastructure, e.g., the Joint Effort for Data assimilation Integration (JEDI).

Index Terms
3315 Data assimilation
3336 Numerical approximations and analyses
0555 Neural networks, fuzzy logic, machine learning
4468 Probability distributions, heavy and fat-tailed

Cross-Listed:
NH - Natural Hazards
NS - Near Surface Geophysics
A - Atmospheric Sciences
H - Hydrology

Neighborhoods:
1. Science Nexus