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

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Session ID#: 12523

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 be directly addressed. This session focuses on nonlinear and non-Gaussian techniques for DA, UQ and predictability, including variational methods, Kalman filters, adjoint and ensemble sensitivity, hybrid techniques, Monte Carlo sampling, particle filters as well as linear approximations to non-Gaussian/nonlinear applications. We welcome 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 Arizona, Department of Mathematics, Tucson, AZ, United States and Derek J Posselt, University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States
Chairs:  Brian C Ancell, Texas Tech University, Lubbock, TX, United States, Derek J Posselt, University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, United States, Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States and Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
OSPA Liaison:  Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, 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]
3245 Probabilistic forecasting [MATHEMATICAL GEOPHYSICS]
3260 Inverse theory [MATHEMATICAL GEOPHYSICS]
3315 Data assimilation [ATMOSPHERIC PROCESSES]

Abstracts Submitted to this Session:

Permutation Entropy as a Method for Determining Perceived Randomness and Minimum Magnitude of Completeness of an Earthquake Catalog (182591)
Samantha Sage Gossage and J. Quinn Norris, Brigham Young University – Idaho, Rexburg, ID, United States
Forward modeling of ground penetrating radar (GPR) and electric resistivity tomography (ERT) using finite difference time domain and finite volume methods, first steps for a joint inversion. (152309)
Diego Domenzain, Boise State University, Boise, ID, United States, John Bradford, Boise State University, Department of Geosciences, Boise, ID, United States and Jodi Mead, Boise State University, Mathematics, Boise, United States
Hierarchical scale dependence associated with the extension of the nonlinear feedback loop in high-dimensional Lorenz models (160079)
Bo-Wen Shen, San Diego State University, San Diego, CA, United States
Selection of observation points in kriging based on non-convex programming (191197)
Daichi Ishikawa, University of Tokyo, Information Science and Technology, Bunkyo-ku, Japan, Masayuki Kano, Graduate School of Science, Tohoku University, Sendai, Miyagi, Japan, Shin-ichi Ito, The University of Tokyo, Earthquake Research Institute, Tokyo, Japan and Hiromichi Nagao, Earthquake Research Institute, The University of Tokyo, Research Center for Computational Earth Science, Tokyo, Japan
Chaos modeling applied to environmental dynamics from in situ and satellite observational time series (200074)
Sylvain Mangiarotti, Centre d'Etudes Spatiales de la Biosphere, Toulouse Cedex 9, France
Practical Predictability of Flows with Many Scales of Motion (199849)
Chris Snyder, National Center for Atmospheric Research, Boulder, CO, United States
Efficient Estimation of the Impact of Observing Systems using EFSO (154320)
Tse-Chun Chen, University of Maryland College Park, College Park, MD, United States, Eugenia Kalnay, University of Maryland College Park, Department of Atmospheric and Oceanic Science, College Park, MD, United States, James Jung, Joint Center for Satellite Data Assimilation, Camp Springs, MD, United States and Daisuke Hotta, Japan Meteorological Agency, Tokyo, Japan
Implementing of lognormal humidity and cloud-related control variables for the NCEP GSI hybrid EnVAR Assimilation scheme. (176913)
Steven J Fletcher, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States and Daryl T Kleist, NOAA NCEP/EMC, College Park, United States
DART: A Community Facility Providing State-of-the-Art, Efficient Ensemble Data Assimilation for Large (Coupled) Geophysical Models (182348)
Timothy J Hoar1, Jeffrey L Anderson1, Nancy Collins1, Helen Kershaw2, Jonathan Hendricks1, Kevin Raeder1, Arthur P Mizzi1, Jerome Barré3, Benjamin Gaubert4, Luke Edward Madaus5, Ali Aydogdu6, Joachim Raeder7, Hernan Arango8, Andrew M Moore9, Christopher A Edwards10, Enrique N Curchitser11, Romain Escudier8,11, Raphael Dussin12, Cecilia M Bitz13, Yong-Fei Zhang14, Prabhakar Shrestha15, Rafael Rosolem16 and Mostaquimur Rahman16, (1)National Center for Atmospheric Research, Boulder, CO, United States, (2)Brown University, Providence, RI, United States, (3)University Corporation for Atmospheric Research, Joint Center for Satellite Data Assimilation, Boulder, United States, (4)National Center for Atmospheric Research (NCAR), Atmospheric Chemistry Observations & Modeling Laboratory (ACOM), Boulder,CO, United States, (5)University of Washington Seattle Campus, Seattle, WA, United States, (6)Euro-Mediterranean Center on Climate Change, Italy, (7)University of New Hampshire Main Campus, Durham, NH, United States, (8)Rutgers University, New Brunswick, NJ, United States, (9)University of California Santa Cruz, Santa Cruz, CA, United States, (10)University of California Santa Cruz, Ocean Sciences, Santa Cruz, United States, (11)Rutgers University New Brunswick, Department of Environmental Sciences, New Brunswick, NJ, United States, (12)Rutgers University New Brunswick, Department of Environmental Sciences, New Brunswick, United States, (13)University of Washington, Atmospheric and Climate Science, Seattle, United States, (14)Univ of Washington, Seattle, WA, United States, (15)Meteorological Institute, Bonn University, Bonn, Germany, (16)University of Bristol, Civil Engineering, Bristol, United Kingdom
Calibrating HIMMELI, a New Wetland CH4 Model, with Adaptive MCMC Techniques (185773)
Jouni Susiluoto, Finnish Meteorological Institute, Helsinki, Finland
Learning-based Wind Estimation using Distant Soundings for Unguided Aerial Delivery (159344)
Mitchell Plyler1,2, Kerri Cahoy3, Kai Angermueller2, Daniel Chen2 and Natasha Markuzon2, (1)Massachusetts Institute of Technology, Aeronautics and Astronautics, Cambridge, MA, United States, (2)Draper Laboratory, Cambridge, MA, United States, (3)Massachusetts Institute of Technology, Cambridge, MA, United States
Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction (160984)
Khachik Sargsyan1, Daniel M Ricciuto2, Cosmin Safta1, Bert Debusschere3, Habib N Najm3 and Peter E Thornton4, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (3)Sandia National Laboratories, Livermore, CA, United States, (4)Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, United States
Testing Four Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction (128609)
Chuan Gao and Rong-Hua Zhang, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
Symmetrized Importance Samplers for Uncertainty Quantification and Data Assimilation (128950)
Andrew Leach, University of Arizona, Tucson, AZ, United States, Kevin K Lin, University of Arizona, Mathematics, Tucson, AZ, United States and Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States
Improving real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using Particle Filter (131275)
Xingya Xu, Tsinghua University, Beijing, China
The Effects of Numerical Noise on Perturbation Experiments (134849)
Brian C Ancell, Texas Tech University, Lubbock, TX, United States
Forecast Sensitivity and Observation Impact (FSOI) Inter-comparison Experiment (135921)
Rahul Mahajan1,2, Thomas Auligne3, Ron Gelaro4, Rolf Langland5, David Neil Groff1,2 and Daisuke Hotta6, (1)Environmental Modeling Center, College Park, MD, United States, (2)IMSG, College Park, MD, United States, (3)Joint Center for Satellite Data Assimilation (JCSDA), Boulder, United States, (4)NASA/GMAO, Greenbelt, United States, (5)Naval Research Lab Monterey, Marine Meteorology, Monterey, CA, United States, (6)Japan Meteorological Agency, Tokyo, Japan
Accounting for Coarse Resolution Model Simulations in Data Assimilation for the Geosciences (139588)
Daniel Hodyss and Sarah King, Naval Research Lab Monterey, Marine Meteorology, Monterey, CA, United States
Studying the Geomagnetic Polarity Time Scale and Superchrons by Scalar Models and Feature Based Data Assimilation (141904)
Jesse Adams, University of Arizona, Tucson, AZ, United States, Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States and Bruce A Buffett, University of California Berkeley, Earth and Planetary Science, Berkeley, CA, United States
Combination of Alternative Models by Mutual Data Assimilation: Supermodeling With A Suite of Primitive Equation Models (145428)
Gregory S. Duane, University of Bergen, Geophysical Institute, Bergen, Norway and Frank Selten, Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
Assimilating every-30-second 100-m-mesh radar observations for convective weather: implications to non-Gaussian PDF (147092)
Takemasa Miyoshi1, Toshiki Teramura2, Juan Ruiz2,3, Keiichi Kondo2 and Guo-Yuan Lien2, (1)RIKEN Center for Computational Science, Kobe, Japan, (2)RIKEN Advanced Institute for Computational Science, Kobe, Japan, (3)University of Buenos Aires, Buenos Aires, Argentina
Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo (150641)
Hamid Moradkhani, Portland State University, Portland, OR, United States, Peyman Abbaszadeh, Portland State University, Civil and Environmental Engineering, Portland, United States and Hongxiang Yan, Portland State University, Civil and Environmental Engineering, Portland, OR, United States
Multi-model data assimilation for subsurface infiltration (118738)
Peng Wang1, Lun Yang1, Akil Narayan2 and Dongbin Xiu3, (1)Beihang University, Beijing, China, (2)University of Utah, Salt Lake City, UT, United States, (3)Ohio State University Main Campus, Columbus, OH, United States
Monte Carlo Bayesian Inference on a Statistical Model of Sub-Gridcolumn Moisture Variability using High-Resolution Cloud Observations (118813)
Peter M Norris, Goddard Earth Sciences Technology and Research, Greenbelt, MD, United States and Arlindo da SIlva, NASA Goddard Space Flight Center, Greenbelt, MD, United States

See more of: Nonlinear Geophysics