OM33A:
Advances in Ocean Data Assimilation, Forecasting, and Reanalysis IV

Session ID#: 93587

Session Description:
Quantifying and reducing uncertainties in ocean models through data assimilation are essential steps towards accurate oceanic simulations and forecasts. Data assimilation and forecasting products based are now widely used in a variety of applications ranging from guiding maritime transportation, planning recreational activities, and supporting hazard and emergency responses. The challenges in this area are numerous due to the nonlinear dynamics and interactions at multiple spatio-temporal scales, computational burden, and diverse sources of uncertainties in the numerical models and observations. The goal of this session is to provide a forum for presenting and discussing recent developments in ocean data assimilation and forecasting methodologies, applications and assessments. Contributions concerning the following issues are of particular interest:

  • Developments of new data assimilation methodologies;
  • New developments, assessments and original applications of ocean data assimilation, operational and reanalysis systems;
  • Pushing the limits of prediction skill, through stochastic parameterizations and accounting for model errors;
  • Coupled data assimilation, including ocean-atmosphere and ocean-biogeochemical systems;
  • Estimation and uncertainty quantification of ocean model parameters, inputs, and outputs;
  • Assimilation of new datasets and design of observation systems.
Co-Sponsor(s):
  • IS - Ocean Observatories, Instrumentation and Sensing Technologies
  • PL - Physical Oceanography: Mesoscale and Larger
  • PS - Physical Oceanography: Mesoscale and Smaller
Primary Chair:  Ibrahim Hoteit, King Abdullah University of Science and Technology (KAUST), Department of Earth Sciences and Engineering, Thuwal, Saudi Arabia
Co-chairs:  Mohamed Iskandarani, University of Miami - RSMAS, Miami, FL, United States, Zhijin Li, JPL, Pasadena, CA, United States and Aneesh Subramanian, University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States
Primary Liaison:  Ibrahim Hoteit, King Abdullah University of Science and Technology (KAUST), Department of Earth Sciences and Engineering, Thuwal, Saudi Arabia
Moderators:  Ibrahim Hoteit, King Abdullah University of Science and Technology (KAUST), Department of Earth Sciences and Engineering, Thuwal, Saudi Arabia, Zhijin Li, JPL, Pasadena, CA, United States and Aneesh Subramanian, University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States
Student Paper Review Liaison:  Ibrahim Hoteit, King Abdullah University of Science and Technology (KAUST), Department of Earth Sciences and Engineering, Thuwal, Saudi Arabia

Abstracts Submitted to this Session:

GFDL's SPEAR prediction system: MOM6 initialization and bias correction with data assimilation (639375)
Feiyu Lu1, Anthony John Rosati2, Matt Harrison2, Thomas L Delworth3, William Cooke2 and Liwei Jia4, (1)Princeton University, Princeton, NJ, United States, (2)Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States, (3)NOAA/GFDL, Princeton, NJ, United States, (4)NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, United States
Addressing Uncertainties in Global Ocean Ensembles (640665)
Prasad G Thoppil, US Naval Research Laboratory, Ocean Sciences Division, Stennis Space Center, MS, United States, Patrick J Hogan, US Naval Research Laboratory, Oceanography Division, Stennis Space Center, MS, United States, Ole Martin Smedstad, Pareton, Inc., Herndon, United States and Clark David Rowley, Naval Research Laboratory, Oceanography, Stennis Space Center, MS, United States
Gaussian approximations in data assimilation (646014)
Matthias Morzfeld, University of Arizona, Department of Mathematics, Tucson, AZ, United States and Daniel Hodyss, US Naval Research Laboratory, Washington D.C., United States
Generalized Four-Dimensional Variational Data Assimilation for Ocean Modeling (651654)
Matthew Carrier, U.S. Naval Research Laboratory, Ocean Dynamics and Prediction, Stennis Space Center, MS, United States, Hans Ngodock, Naval Research Lab Stennis Space Center, Stennis Space Center, MS, United States, Ole Martin Smedstad, Pareton, Inc., Herndon, United States and Innocent Souopgui, The University of New Orleans, New Orleans, LA, United States
Using dual numbers for automatic differentiation of complex functions -- a simple way to create data assimilation code for coupled models. (647151)
Jann Paul Mattern1, Christopher A Edwards1 and Christopher N Hill2, (1)University of California Santa Cruz, Santa Cruz, CA, United States, (2)MIT, Cambridge, United States
MPAS-Ocean and the AOT Algorithm: A Novel Data Assimilation Technique Applied to Ocean Models (652174)
Elizabeth Carlson1, Luke Van Roekel2, Humberto C Godinez3 and Mark R Petersen3, (1)United States, (2)Los Alamos National Laboratory, Fluid Dynamics and Solid Mechanics Group, Los Alamos, United States, (3)Los Alamos National Laboratory, Los Alamos, NM, United States
A polynomial chaos framework for probabilistic predictions of storm surge events (652320)
Clint Dawson, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States, Pierre Sochala, BRGM, DRP, Orléans, France, Mohamed Iskandarani, University of Miami - RSMAS, Miami, FL, United States and Chen Chen, University of Texas at Austin, United States
Non-localized particle flow filters for ocean data assimilation (651980)
Peter Jan van Leeuwen, Colorado State University, Atmospheric Science, Fort Collins, CO, United States, Manuel A Pulido, University of Reading, Meteorology, Reading, United Kingdom and Chih-Chi Hu, Colorado State University, Atmospheric Sciences, Fort Collins, CO, United States
See more of: Ocean Modeling