PO52C:
Developments and Ocean Applications of Data Assimilation, Uncertainty, and Sensitivity Analyses II

Submit an Abstract to this Session

Session ID#: 11485

Session Description:
Data assimilation and uncertainty and sensitivity analyses are vital components in the production of ocean science reanalyses for the study of various ocean processes. They are also used in model calibration (including parameter estimation), design of observation systems, and for operational forecasts and analyses. The challenges in this area are numerous due to the non-linear interaction of multiple spatio-temporal scales as well as uncertainties due to the resolution of physical processes, parameterizations, and uncertain inputs. The goal of this session is to bring together researchers working in the areas of ocean data assimilation, model sensitivity analysis, and uncertainty quantification, with the goal of discussing new technical developments and recent applications. Contributions concerning the following issues are particularly of interest:

-       New developments and original applications of data assimilation, uncertainty and sensitivity analyses methods

-       Coupled data assimilation, including ocean-atmosphere and ocean-biogeochemical systems

-       Estimation and uncertainty quantification of ocean models parameters, inputs, and outputs

-       Developments of advanced ocean operational and reanalysis systems

-       Assimilation of new data sets and design of observation systems

Primary Chair:  Ibrahim Hoteit, King Abdullah University of Science and Technology, Physical Sciences and Engineering Division, Thuwal, Saudi Arabia
Chairs:  Bruce D Cornuelle, University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States, Mohamed Iskandarani, University of Miami, Rosenstiel School of Marine, Atmospheric and Earth Science, Miami, United States and Hans E Ngodock, Naval Research Lab Stennis Space Center, Stennis Space Center, MS, United States
Moderators:  Ibrahim Hoteit, King Abdullah University of Science and Technology, Physical Sciences and Engineering, Thuwal, Saudi Arabia and Mohamed Iskandarani, University of Miami, Rosenstiel School of Marine, Atmospheric and Earth Science, Miami, United States
Student Paper Review Liaison:  Ibrahim Hoteit, King Abdullah University of Science and Technology, Physical Sciences and Engineering, Thuwal, Saudi Arabia
Index Terms:

1910 Data assimilation, integration and fusion [INFORMATICS]
1990 Uncertainty [INFORMATICS]
4260 Ocean data assimilation and reanalysis [OCEANOGRAPHY: GENERAL]
4263 Ocean predictability and prediction [OCEANOGRAPHY: GENERAL]
Co-Sponsor(s):
  • OD - Ocean Observing and Data Management
  • PC - Past, Present and Future Climate
  • O - Other

Abstracts Submitted to this Session:

PO52C-01
Towards an Operational Hybrid Global Ocean Data Assimilation System (Hybrid-GODAS) at NCEP (86830)
Stephen Gregory Penny, NOAA Boulder, Boulder, MD, United States, David Behringer, Environmental Modeling Center, College Park, MD, United States, James Carton, 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 and Yan Xue, Climate Prediction Center College Park, NOAA NWS OSTI Modeling Program Division, Silver Spring, United States
PO52C-02
Challenges and algorithmic solutions for data assimilation in coupled systems (88100)
Sergey Frolov1, Craig H Bishop2, Douglas R Allen3, David Kuhl4 and Teddy Holt2, (1)University Corporation for Atmospheric Research, Boulder, CO, United States, (2)Naval Research Lab, Monterey, United States, (3)US Naval Research Laboratory, Remote Sensing Division, Washington, DC, United States, (4)Naval Research Lab, Washington, DC, United States
PO52C-03
Coarse-grained sensitivity for multiscale data assimilation (87941)
Nozomi Sugiura, Japan Agency for Marine-Earth Science and Technology, Research and Development Center for Global Change, Yokosuka, Japan
PO52C-04
On the predictability of North Atlantic ocean state (88611)
Florian Sevellec, University of Southampton, Southampton, SO14, United Kingdom and Alexey V Fedorov, Yale University, New Haven, CT, United States
PO52C-05
Adaptive Sampling for the Coastal Ocean (93664)
Sri Venkata Tapovan Lolla and Pierre F J Lermusiaux, Massachusetts Institute of Technology, Cambridge, MA, United States
PO52C-06
Model error varieties and their relative importance in ocean state estimation. (91374)
Gael Forget, Massachusetts Institute of Technology, EAPS, Cambridge, United States, Jean-Michel Campin, Massachusetts Institute of Technology, Cambridge, United States, Patrick Heimbach, University of Texas at Austin, Oden Institute for Computational Engineering and Sciences, Austin, TX, United States, Christopher N Hill, Massachusetts Institute of Technology, Cambridge, MA, United States, Rui M Ponte, Atmospheric and Environmental Research, Lexington, MA, United States and Carl Wunsch, Harvard University, Cambridge, MA, United States
See more of: Physical Oceanography/Ocean Circulation