NG002
Advances in Data Assimilation, Predictability, and Uncertainty Quantification
Session ID#: 156921
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 advanced 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, and linear approximations to non-Gaussian/nonlinear applications. We welcome contributions from all of the geosciences and the mathematical and statistical communities. Our main goal is to share ideas that can be applied across Earth science and the larger scientific community.
Index Terms:
1910 Data assimilation, integration and fusion [INFORMATICS]
3238 Prediction [MATHEMATICAL GEOPHYSICS]
3260 Inverse theory [MATHEMATICAL GEOPHYSICS]
3275 Uncertainty quantification [MATHEMATICAL GEOPHYSICS]
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, CA, United States and Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States
Forward Model Emulator for Computationally Expensive UQ in Atmospheric Remote Sensing (1194212)
Otto Lamminpää, NASA Jet Propulsion Laboratory, Pasadena, United States, Jonathan Hobbs, Jet Propulsion Laboratory, Pasadena, CA, United States, Jouni Susiluoto, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States and Amy J Braverman, NASA Jet Propulsion Laboratory, Uncertainty Quantification and Statistical Analysis Group, Pasadena, United States
New approach for estimating the ocean observation impacts on coupled assimilation using EFSO (1202064)
Chu-Chun Chang, University of Maryland College Park, College Park, MD, United States, Tse-Chun Chen, Physical Sciences Laboratory, NOAA, Boulder, United States and Eugenia Kalnay, University of Maryland College Park, Department of Atmospheric and Oceanic Science, College Park, MD, United States
A Hidden Markov Model Approach to Quantify Uncertainty of High-Resolution 3D Lithofacies Models Using Seismic Data (1196689)
Jonas Kloeckner1, David Zhen Yin2, Diego Machado Marques3, joão Felipe coimbra leite Costa4 and Jef Caers2, (1)UFRGS Federal University of Rio Grande do Sul, Porto Alegre, Brazil, (2)Stanford University, Department of Geological Sciences, Stanford, CA, United States, (3)UFRGS Federal University of Rio Grande do Sul, Department of Geological Science, Porto Alegre, Brazil, (4)UFRGS Federal University of Rio Grande do Sul, Department of Mining, Porto Alegre, Brazil
Guidance on Localization for Strongly Coupled Atmosphere-Ocean Data Assimilation (1197292)
Zofia Stanley1, Clara Draper2, Sergey Frolov2, Laura Slivinski1, Wei Huang1, Jeffrey Whitaker2 and Henry Winterbottom1, (1)CIRES/NOAA PSL, Boulder, United States, (2)NOAA PSL, Boulder, United States
An observing system simulation experiment (OSSE) using the Particle Flow Filter (PFF) in a high-dimensional geophysical system in the Data Assimilation Research Testbed (DART) (1196792)
Chih-Chi Hu, Colorado State University, Atmospheric Sciences, Fort Collins, CO, United States, Peter Jan van Leeuwen, Colorado State University, Atmospheric Science, Fort Collins, CO, United States and Jeffrey L Anderson, NCAR, Boulder, United States
Assessment of Bayesian PDF for 2D structure using Hybrid Global Inversion of Magnetotelluric datasets. (1163473)
Kuldeep Sarkar, Indian Institute of Technology Dhanbad, Dhanbad, India, Mukesh Mukesh, Indian School of Mines, APPLIED GEOPHYSICS, Dhanbad, India and Upendra K. Singh, Indian School of Mines, Dhanbad, India
Machine Learning Opportunities to Reduce the Dimensionality of Uncertain Complex Geophysical Media (1174609)
Fanny Lehmann1, Filippo Gatti2, Michaël Bertin1 and Didier Clouteau3, (1)CEA/DAM/DIF, Arpajon, France, (2)Université Paris-Saclay, ENS Paris-Saclay, CentraleSupélec, CNRS, LMPS - Laboratoire de Mécanique Paris-Saclay, GIf-sur-Yvette, France, (3)Université Paris-Saclay, ENS Paris-Saclay, CentraleSupélec, CNRS, LMPS - Laboratoire de Mécanique Paris-Saclay, Gif-Sur-Yvette, France
Regularization of the ensemble Kalman filter for non-local observations: application to elliptic observations (1176409)
Mathieu Le Provost1, Ricardo Baptista2, Youssef Marzouk2 and Jeff D. Eldredge3, (1)Long Island University, Department of Technology, Innovation and Computer Science, Brooklyn, United States, (2)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, United States, (3)University of California Los Angeles, Department of Mechanical and Aerospace Engineering, Los Angeles, United States
Learning from JEFE: Using an Adjoint Model to Compare Error Sensitivities of Different Bulk Representations of Cloud Microphysics (1179065)
Sean Patrick Santos, Columbia University, Center for Climate Systems Research, New York, United States; NASA Goddard Institute for Space Studies, New York, United States, Hugh Morrison, NCAR, MMM Laboratory, Boulder, United States and Marcus van Lier-Walqui, Columbia University, Center for Climate Systems Research, New York, NY, United States
Cross-Attractor Transformations: A Novel Machine Learning Framework to Minimize Forecast Error in the Presence of Model Bias (1160637)
Niraj Agarwal, University of Colorado Boulder, Boulder, CO, UNITED STATES, Ian Grooms, University of Colorado at Boulder, Applied Mathematics, Boulder, United States and Daniel E Amrhein, National Center for Atmospheric Research, Boulder, United States
PROM: A Method for Estimating Error Covariance Functions for Variational Data Assimilation Schemes in Ocean Models (1151148)
jose Maria gonzalez Ondina, University of Florida, Gainesvile, United States, Georgy I. Shapiro, Plymouth University, Faculty of Science and Engineering, Plymouth, United Kingdom and Lewis Sampson, MetOffice, Exeter, United Kingdom
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence (1138931)
Ashesh Kumar Chattopadhyay1, Jaideep Pathak2, Ebrahim Nabizadeh3, Pedram Hassanzadeh3 and Wahid Bhimji2, (1)Rice University, Houston, TX, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, United States, (3)Rice University, Houston, United States
The IMFogram: a New Time-Frequency Representation Method for Nonstationary Signals Analysis (1140821)
Antonio Cicone, Giulia D'Angelo2, Emanuele Papini3, Dario Recchiuti4 and Pietro Ubertini2, (1)INAF-IAPS, Rome, Italy, (2)Università degli Studi di Firenze, Florence, Italy, (3)University of Trento, Trento, Italy
Towards a strongly coupled land-atmosphere data assimilation with NOAA Unified Forecast System model (1141016)
Zhaoxia Pu, University of Utah, Salt Lake City, UT, United States, Qien Huang, University of Utah, Salt Lake City, United States and Daryl Kleist, NOAA/NWS/NCEP/EMC, College Park, MD, United States
Nonlinear Ensemble Transport Smoothing (1142599)
Maximilian Ramgraber1, Ricardo Baptista1, Dennis McLaughlin2 and Youssef Marzouk1, (1)Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, United States
Why is "Bad" Localization so "Good"? (1059200)
Matthias Morzfeld, Scripps Institution of Oceanography, La Jolla, CA, United States and Daniel Hodyss, US Naval Research Laboratory, Washington D.C., United States
Supermodeling: Synchronising Models to Improve Predictions (1066258)
Francine Schevenhoven, University of Colorado, Boulder, United States, Mao-lin Shen, University of Bergen, Bergen, Norway, Jeffrey Weiss, University of Colorado at Boulder, Atmospheres and Oceans, Boulder, CO, United States and Gregory S. Duane, Univ of Colorado, Atmospheric and Oceanic Sciences, Boulder, United States
Estimation of Spatially and Temporally Varying Biogeochemical Parameters in a Global Ocean Model (1072514)
Nabir Mamnun, Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research Bremerhaven, Bremerhaven, Germany, Christoph D Voelker, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany, Mihalis Vrekoussis, University of Bremen, Institute of Environmental Physics + MARUM, Bremen, Germany and Lars Nerger, Alfred Wegener Institute, Bremerhaven, Germany, Germany
Analog Ensemble Data Assimilation in a Quasigeostrophic Coupled Model (1075578)
Ian Grooms1, Camille Renaud1, Zofia Stanley1,2 and Lucia Minah Yang1,3, (1)University of Colorado at Boulder, Applied Mathematics, Boulder, United States, (2)CIRES/NOAA PSL, Boulder, United States, (3)Courant Institute of Mathematical Sciences, Center for Atmosphere Ocean Science, New York, United States
A Hybrid Nonlinear Data Assimilation Method for Complex Fluid Systems with Partial Observations, with Applications to Precipitating Quasi-geostrophic Equations (1080895)
Changhong Mou, University of Wisconsin Madison, Department of Mathematics, Madison, WI, United States, Leslie M Smith, University of Wisconsin Madison, Madison, United States and Nan Chen, University of Wisconsin-Madison, Department of Mathematics, Madison, United States
Non‐Gaussian Hybrid Variational Data Assimilation (1089792)
Steven J Fletcher1, Senne Van Loon2, Jakir Hossen1, Michael Goodliff3, Anton Kliewer4, Milija Zupanski1, Ting-Chi Wu5, Andrew S Jones3 and John Michael Forsythe3, (1)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, United States, (3)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (4)Colorado State University, Cooperative Institute for Research in the Atmosphere @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States, (5)Cooperative Institute for Research in the Atmosphere, Fort Collins, United States
Machine Learning for Distribution Selection in Non-Gaussian Variational Data Assimilation (1091765)
Senne Van Loon1, Steven J Fletcher1, Jakir Hossen2 and Michael Goodliff1, (1)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
Uncertainty Quantification and Global Sensitivity Analysis for CME simulations in the SWMF (1093645)
Aniket Jivani1, Hongfan Chen1, Xun Huan1, Yang Chen2, Bart van der Holst3, Shasha Zou3, Zhenguang Huang4, Nishtha Sachdeva3, Daniel Iong2, Ward Manchester3 and Gabor Toth5, (1)University of Michigan, Department of Mechanical Engineering, Ann Arbor, United States, (2)University of Michigan, Department of Statistics, Ann Arbor, United States, (3)University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States, (4)University of Michigan-AOSS, Ann Arbor, United States, (5)University of Michigan, Department of Climate and Space Sciences and Engineering, Ann Arbor, United States
The Latest from the Data Assimilation Research Testbed: New Algorithms for Non-Gaussian Distributions and Sampling Errors; New Memory Management for Larger Models and Faster Execution; New Model and Observation Interfaces for Land, Upper Atmosphere, and Ocean Biogeochemistry. (1094367)
Benjamin K Johnson1, Kevin Raeder1, Jeffrey L Anderson2, Moha El Gharamti3, Helen Kershaw4, Brett M Raczka3, Marlena Smith3, Jiachen Liu5, Fairuz Ishraque6, Jon Labriola7 and Daniel Hagan8, (1)National Center for Atmospheric Research, Boulder, CO, United States, (2)NCAR, Boulder, United States, (3)National Center for Atmospheric Research, Boulder, United States, (4)NCAR, Boulder, CO, United States, (5)Drexel University, Civil, Architectural, and Environmental Engineering, Philadelphia, United States, (6)Princeton University, Department of Geosciences, Princeton, United States, (7)National Severe Storms Laboratory, Norman, United States, (8)Nanjing University of Information Science & Technology, Nanjing, China
Parameter inversion with sequential neural density estimators: an enhanced machine learning-based inversion (1098232)
Lijing Wang1, Celine Scheidt2, David Zhen Yin1, Alex Miltenberger3, Kate Maher4 and Jef Caers1, (1)Stanford University, Department of Geological Sciences, Stanford, CA, United States, (2)Stanford University, Department of Energy Resources Engineering, Stanford, CA, United States, (3)Stanford University, Stanford, CA, United States, (4)Stanford University, Earth Systems Science, Stanford, United States
Non-Gaussian stochastic dynamical model for the El Niño southern oscillation (1112663)
Ludovico Theo Giorgini1, Woosok Moon1, Nan Chen2 and John S. Wettlaufer3, (1)Stockholm University, Stockholm, Sweden, (2)University of Wisconsin-Madison, Department of Mathematics, Madison, United States, (3)Yale University, New Haven, CT, United States
The GMAO Observing System Simulation Experiment (OSSE) Framework (1115443)
Isaac Moradi, Earth System Science Interdisciplinary Center - CICS-MD, University of Maryland, College Park, United States, Nikki Prive, NASA Goddard Space Flight Center, Greenbelt, MD, United States, Erica L McGrath-Spangler, Morgan State University, Baltimore, United States and Ron Gelaro, NASA/GMAO, Greenbelt, United States
Ensemble Kalman Inversion with Limited Observation for Parameter Estimation and Upstream Streamflow Prediction (1119176)
Andrew Pensoneault1, Xueyu Zhu2, Witold F Krajewski1, Nicolas Velasquez3 and Ricardo Mantilla4, (1)University of Iowa, Iowa City, United States, (2)University of Iowa, Department of Mathematics, Iowa City, United States, (3)University of Iowa, Iowa City, IA, United States, (4)University of Manitoba, Winnipeg, Canada
A Bayesian framework for uncertainty analysis of anomalous body shapes using gravity data (1121066)
Xiaolong Wei, University of Houston, Department of Earth and Atmospheric Sciences, Houston, TX, United States, Jiajia Sun, University of Houston, Department of Earth and Atmospheric Sciences, Houston, United States and Mrinal K Sen, Univ Texas Inst Geophysics, Austin, TX, United States
