NG33B:
Advances in Data Assimilation, Predictability, and Uncertainty Quantification Posters


Submit an Abstract to this Session


Session ID#: 44330

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, Derek J Posselt, University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States and Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, 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:

A Proactive Quality Control (PQC) based on Ensemble Forecast Sensitivity to Observations (412122)
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
Electron Contribution In Mirror Mode Instability In Quasi-Linear Regime (355561)
Naila Noreen, Forman Christian College, Lahore, Physics, FC College, Lahore, Lahore, Pakistan
Channel Selection Method for Hyperspectral Infrared Data Based on Relative Entropy (345191)
Shen Yan, Huadong Du, Jie Xiang and Sixun Huang, College of Meteorology and Oceanography, National University of Defense Technology, Nanjing, China
Detection of Lognormal Signals in Different Atmospheric Flows (351555)
Michael Goodliff1, Steven J Fletcher1, Anton Kliewer2, John Forsythe1 and Andrew S Jones1, (1)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)Colorado State University, Cooperative Institute for Research in the Atmosphere @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States
An Overview of the CIRA Data Assimilation Testbed (CDAT) Educational Website for Applications with Atmospheric Rivers and Predictive Irrigation Tools (363923)
Andrew S Jones1, Steven J Fletcher2, John Michael Forsythe3, Michael Goodliff2, Anton Kliewer4, Allan Andales5, Jose L Chavez6, Cullen McGovern5 and Garvey E. B. Smith5, (1)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (3)Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, United States, (4)Colorado State University, Cooperative Institute for Research in the Atmosphere @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States, (5)Colorado State University, Soil and Crop Sciences, Fort Collins, CO, United States, (6)Colorado State University, Fort Collins, United States
Comparisons of non-Gaussian with Gaussian-based observational quality control measures and their impacts on data assimilation systems (354588)
Steven J Fletcher1, Michael Goodliff2, Anton Kliewer3, Andrew S Jones2 and John Michael Forsythe4, (1)Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (2)Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, (3)Colorado State University, Cooperative Institute for Research in the Atmosphere @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States, (4)Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, United States
Uncertainty quantification for 4DVar based on the second-order adjoint method (390314)
Hiromichi Nagao, Earthquake Research Institute, The University of Tokyo, Research Center for Computational Earth Science, Tokyo, Japan and Shin-ichi Ito, The University of Tokyo, Earthquake Research Institute, Tokyo, Japan
Localization and bias correction in geomagnetic data assimilation: systematic numerical experiments with reduced-scale models (393180)
Kyle Gwirtz, University of Arizona, Tucson, AZ, United States, Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States and Andrew Tangborn, Univ Maryland-Baltimore County, Greenbelt, MD, United States
Estimation and Adaptive Online Correction of Systematic Errors in the Global Forecast System (GFS) Using Analysis Increments (393669)
Kriti Bhargava, Joint Center for Satellite Data Assimilation, Boulder, United States, Eugenia Kalnay, University of Maryland, Atmospheric and Oceanic Sciences, College Park, MD, United States, James Carton, University of Maryland College Park, College Park, MD, United States and Mark Iredell, NOAA/NWS/NCEP EMC, College Park, MD, United States
Bayesian estimation of the parameters of the nonlinear cloud and rain equation: finding physically relevant model parameters from large-eddy simulations. (394471)
Spence Lunderman, University of Arizona, Tucson, AZ, United States, Matthias Morzfeld, Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States, Franziska Glassmeier, ETH Swiss Federal Institute of Technology Zurich, Zurich, Switzerland and Graham Feingold, NOAA CSL, Boulder, United States
Ensemble Sensitivity-Based Subsetting Overview and Evaluation Activities at the 2018 NOAA HWT (356493)
Brian C Ancell, Texas Tech University, Lubbock, TX, United States, Austin Coleman, Texas Tech University, Geosciences, Lubbock, TX, United States and Aaron Hill, Texas Tech University, Lubbock, United States
Data assimilation with stochastic reduced models quantifying model error (414447)
Fei Lu, Johns Hopkins University, Baltimore, MD, United States, Xuemin Tu, University of Kansas, Lawrence, KS, United States, Alexandre J Chorin, University of California Berkeley, Berkeley, CA, United States and Kevin K Lin, University of Arizona, Mathematics, Tucson, AZ, United States
Blending radar-based nowcast and high-resolution Numerical weather prediction (NWP) model: A demonstration at the CASA DFW testbed (423597)
Chandrasekar Radhakrishnan, Colorado State University, Fort Collins, CO, United States and V. Chandrasekar, Colorado State University, Department of Electrical and Computer Engineering, Fort Collins, United States
Predicting 3D geochemical models from multiple geophysical inversion models by Gaussian process regression with custom kernels (425959)
Tom Horrocks, Eun-Jung Holden, Daniel Wedge and Chris Wijns, The University of Western Australia, Centre for Exploration Targeting, Crawley, Australia
Fault State Estimation in Subduction Zones Using a Particle Filter with Time-Lagged Particle Generation (437483)
Shiran Levy1,2, Femke C Vossepoel3, Marie Bocher2 and Ylona van Dinther4,5, (1)Delft University of Technology, Delft, Netherlands, (2)ETH Zurich, Zurich, Switzerland, (3)Department of Geoscience and Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands, (4)Utrecht University, Department of Earth Sciences, Utrecht, Netherlands, (5)Utrecht University, Earth Sciences, Utrecht, Netherlands
A mapping particle filter for high-dimensional applications (443060)
Peter Jan van Leeuwen1,2 and Manuel A Pulido1, (1)University of Reading, Meteorology, Reading, United Kingdom, (2)Colorado State University, Atmospheric Science, Fort Collins, United States
Synoptic-scale weather prediction using the local particle filter (450887)
Jonathan Poterjoy, University of Maryland, Atmospheric and Oceanic Science, College Park, United States
Operational High Resolution Analysis of Significant Wave Height for CONUS (458905)
Stylianos Flampouris, IMSG/NOAA-NCEP-EMC, College Park, MD, United States and Jacob R Carley, NOAA/NCEP/EMC, College Park, MD, United States
Why Gaussian approximations to posteriors are more appropriate than to priors (417111)
Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States and Daniel Hodyss, Naval Research Lab Monterey, Marine Meterology, Monterey, CA, United States
Adaptive Ensemble Kalman Filter to Mitigate Error Misspecifications in GRACE Data Assimilation (461119)
Ashkan Shokri, Monash University, Melbourne, VIC, Australia, Jeffrey P Walker, Monash University, Department of Civil Engineering, Clayton, VIC, Australia, Albert van Dijk, Australian National University, Canberra, Australia and Valentijn R N Pauwels, Monash University, Melbourne, Australia
How best to add structural complexity to cloud microphysics parameterization schemes? (462369)
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, North Carolina Institute for Climate Studies, North Carolina State University, Asheville, United States and Karly Jackson Reimel, Colorado State University, Department of Atmospheric Science, Fort Collins, CO, United States
Downscaling and projection of summer rainfall in Eastern China using a nonhomogeneous hidden Markov model (384793)
Lianyi Guo, NUIST Nanjing University of Information Science and Technology, Nanjing, China and Zhihong Jiang, NUIST, Nanjing, China

See more of: Nonlinear Geophysics