OM31A:
Advances in Ocean Data Assimilation, Forecasting, and Reanalysis III
OM31A:
Advances in Ocean Data Assimilation, Forecasting, and Reanalysis III
Advances in Ocean Data Assimilation, Forecasting, and Reanalysis III
Session ID#: 93217
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
Index Terms:
1910 Data assimilation, integration and fusion [INFORMATICS]
1922 Forecasting [INFORMATICS]
4260 Ocean data assimilation and reanalysis [OCEANOGRAPHY: GENERAL]
4263 Ocean predictability and prediction [OCEANOGRAPHY: GENERAL]
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, Rosenstiel School of Marine, Atmospheric and Earth Science, Miami, 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:
See more of: Ocean Modeling