NG33A:
Advances in Data Assimilation, Predictabilit, and Uncertainty Quantification II Posters


Session ID#: 7275

Session Description:
Geophysical processes are typically sparsely observed and their dynamics are nonlinear, so that the state-space statistics may become non-Gaussian.  Successful and efficient data assimilation (DA) algorithms, uncertainty quantification (UQ) techniques and prediction require that these characteristics are directly addressed.  This session focuses on advanced techniques for DA, UQ and predictability, for example variational methods, Kalman and ensemble filters, adjoint and ensemble sensitivity, hybrid techniques, Monte-Carlo sampling, particle filters or linear approximation to non-Guassian/nonlinear applications.  This session 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 University, Lubbock, TX, United States, Matthias Morzfeld, University of California Berkeley, Berkeley, CA, United States and Derek J Posselt, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
Chairs:  Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States, Matthias Morzfeld, University of California Berkeley, Berkeley, CA, United States and Derek J Posselt, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States
OSPA Liaison:  Derek J Posselt, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States

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

1910 Data assimilation, integration and fusion [INFORMATICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]
4260 Ocean data assimilation and reanalysis [OCEANOGRAPHY: GENERAL]
4468 Probability distributions, heavy and fat-tailed [NONLINEAR GEOPHYSICS]

Abstracts Submitted to this Session:

Intermittency Route to Hyperchaos in 3D Rayleigh-Benard Convection (63359)
Erico Luiz Rempel, Roman Chertovskih and Emanuel V Chimanski, ITA Technological Institute of Aeronautics, Sao Jose dos Campos, Brazil
A Numerical Study on the Characteristics of High-frequency Oscillations in the Eyewall of Tropical Cyclones (80047)
Shumin Chen1, Youyu Lu2, Weibiao Li3, Zhiping Wen4, Mingsen Zhou3 and Yu-Kun Qian5, (1)SCSIO South China Sea Institute of Oceanology, Chinese Acaademy of Sciences, Guangzhou, China, (2)Bedford Institute of Oceanography, Dartmouth, NS, Canada, (3)Sun Yat-Sen University, Guangzhou, China, (4)Fudan University, Shanghai, China, (5)South China Sea Institute of Oceanology,Chinese Academy of Sciences, State Key Laboratory of Tropical Oceanography, Guangzhou, China
Analysis of the efficiency of the Ensemble Kalman Filter for Marginal and Joint Posteriors  (71039)
Matthias Morzfeld, University of California Berkeley, Berkeley, CA, United States, Daniel Hodyss, US Naval Research Laboratory, Monterey, CA, United States and Chris Snyder, National Center for Atmospheric Research, Boulder, CO, United States
Analysis of structure optimal control under earthquake excitation using Pseudospectral Legendre method (80777)
Hao-CHENG Tsai, NTU National Taiwan University, Taipei, Taiwan
Large-Scale Flow Generation due to Inhomogeneous Turbulent Helicity (84258)
Nobumitsu Yokoi, University of Tokyo, Institute of Industrial Science, Bunkyo-ku, Japan and Axel Brandenburg, Department of Astronomy, AlbaNova University Center, Stockholm University, Stockholm, Sweden; Nordita, KTH Royal Institute of Technology and Stockholm University, Stockholm, Sweden
Classifying Multi-year Land Use and Land Cover using Deep Convolutional Neural Networks (85891)
Bumsuk Seo, Seoul National University, Asian Institute for Energy, Environment & Sustainability, Seoul, Korea, Republic of (South); Kangwon National University, Department of Environmental Science, Chuncheon, Korea, Republic of (South)
A study on the background error covariance for reduced-rank retrospective optimal interpolation with WRF (86673)
Shinwoo Kim, Seoul National University, Seoul, Korea, Republic of (South)
The Object-analogue approach for probabilistic forecasting (86419)
Thomas M Hopson, National Center for Atmospheric Research, Boulder, CO, United States, Maria Eugenia Frediani, University of British Columbia, Earth, Ocean and Atmospheric Sciences, Vancouver, BC, Canada, Emmanouil N Anagnostou, University of Connecticut, Department of Civil and Environmental Engineering, Storrs Mansfield, United States and Joshua Hacker, Jupiter, Boulder, United States
Estimating Ocean Surface Level using the Intrinsic Non-stationary Covariance Function (82084)
Chintan Alakh Dalal1, Vladimir Pavlovic1 and Robert E Kopp2, (1)Rutgers University New Brunswick, New Brunswick, NJ, United States, (2)Rutgers University, Department of Earth and Planetary Sciences, New Brunswick, United States
Low Order Uncertainty Dynamics in Ocean State Estimation: Reduced Hessian Method for Constraining Barotropic Drake Passage Transport (82278)
Alexander Kalmikov, Massachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, MA, United States, Patrick Heimbach, University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, TX, United States and Carl I Wunsch, Massachusetts Institute of Technology, Cambridge, MA, United States
High-precision square-root filter/smoother for near-singular system in estimation of terrestrial reference frame (81211)
Toshio Michael Chin1, Richard S Gross2, Michael B Heflin2, Claudio Abbondanza3, Jay W Parker4 and Xiaoping Wu2, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (3)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (4)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Multi-model Cross Pollination in Time via Data Assimilation (75156)
Hailiang Du, University of Chicago, Chicago, IL, United States and Leonard A Smith, London School of Economics, London, United Kingdom
Parameter Estimation for Observation Bias with an Ensemble Kalman Filter (72652)
Raquel Lorente-Plazas, University of Notre Dame, Notre Dame, IN, United States and Joshua Hacker, Jupiter, Boulder, United States
Thermal conductivity modeling in variably saturated porous media (74934)
Dr. Behzad Ghanbarian, Kansas State University, Geology, Manhattan, United States and Hugh Daigle, University of Texas at Austin, Austin, TX, United States
Gaussian and Mixed Lognormal-Gaussian 1DVAR Satellite Retrievals for Water Vapor and Temperature in the West Pacific (62009)
Steven J Fletcher1, Anton Kliewer2, Andrew S Jones1 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 @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States, (3)Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, United States
Lognormal Assimilation of Water Vapor in a WRF-GSI Cycled System (62019)
Anton Kliewer1, Steven J Fletcher2, Andrew S Jones2 and John Michael Forsythe3, (1)Colorado State University, Cooperative Institute for Research in the Atmosphere @NOAA/OAR/ESRL Global Systems Lab, Boulder, CO, United States, (2)Colorado State University, 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
Towards Assimilating GOES-R Infrared Brightness Temperatures for the Analysis and Forecast of Tropical Cyclones and Severe Weather (67809)
Fuqing Zhang1, Masashi Minamide1 and Eugene Edmund Clothiaux2, (1)Pennsylvania State University Main Campus, University Park, PA, United States, (2)Penn State, University Park, PA, United States
Improving the Fit of a Land-Surface Model to Data Using its Adjoint (64764)
Nina Raoult, Timothy Edmund Jupp, Peter Michael Cox and Catherine Luke, University of Exeter, Exeter, United Kingdom
Combination of UV Irradiance from TOMS-OMI and UVMRP Ground Observations: A New Ensemble Method to Estimate Covariance and Correlation Region in Data Fusion (63889)
Zhibin Sun1, John Davis2 and Wei Gao2, (1)Colorado State University, Fort Collins, CO, United States, (2)Colorado State University, Natural Resource Ecology Laboratory, Fort Collins, CO, United States
Particle Filter with Nudging in Soil Hydrology (63209)
Daniel Berg1,2, Hannes H Bauser1,3 and Kurt Roth2, (1)HGS MathComp, Heidelberg, Germany, (2)Heidelberg University, Institute of Environmental Physics, Heidelberg, Germany, (3)Heidelberg University, Heidelberg, Germany
On the Fate of Debris Associated with the Disappearance of Flight MH370: a Dynamical System Perspective (62512)
Ana Maria Mancho1, Victor Jose Garcia-Garrido1, Stephen Wiggins2 and Carolina Mendoza3, (1)ICMAT-CSIC, Madrid, Spain, (2)University of Bristol, Bristol, United Kingdom, (3)Universidad Politecnica de Madrid, ETSI Navales, Madrid, Spain
A discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics  (70784)
Fei Lu, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; University of California Berkeley, Mathematics, Berkeley, CA, United States and Alexandre J Chorin, University of California Berkeley, Berkeley, CA, United States
Adaptive Covariance Inflation in a Multi-Resolution Assimilation Scheme (70772)
Kyle S. Hickmann and Humberto C Godinez, Los Alamos National Laboratory, Los Alamos, NM, United States
A new ocean data assimilation method without background and observational error covariance matrices (59372)
Peter C Chu, Naval Postgraduate School, Monterey, CA, United States

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