Abstract's details
Impact of present and future altimetric missions on ocean forecasts
CoAuthors
Event: 2014 Ocean Surface Topography Science Team Meeting
Session: Quantifying Errors and Uncertainties in Altimetry data
Presentation type: Type Poster
Contribution: not provided
Abstract:
Mercator Ocean, as a major operational oceanography center, must adapt its modeling and data assimilation systems regarding new measurements technologies. As satellite altimetry is one of the most important observing systems to constrain high resolution ocean models, it is a main concern to assess the impacts and evolutions that will bring the new altimeter based on the SAR technology compared to LRM. The SAR technology allows a lower measurement noise close to 1 cm and much better than the LRM's 3cm noise. It is important to assess and quantify its impact on operational modeling and data assimilation systems.
The study is based on the OSSE (Observing System Simulation Experiments) methods. OSSEs are carried out with a global 1/4° modeling and data assimilation system similar to the operational one but using simulated dataset of observations (altimetry here) in order to assess their contribution and to test the sensitivity of results to different parameters (errors, observation density, type of observations). Simulated data sets are extracted from a global free 1/12° run and assimilated in the global 1/4° modeling and data assimilation system. Using the 1/12° simulation is justified by the fact that mesoscale variability is better represented than in a 1/4° one.
The main goal is to assess how the reduction of measurement noise (SAR/LRM) and number of satellites impact the analysis and forecast errors at global and regional (i.e. Gulf Stream, Agullas Current) scales.
The study is based on the OSSE (Observing System Simulation Experiments) methods. OSSEs are carried out with a global 1/4° modeling and data assimilation system similar to the operational one but using simulated dataset of observations (altimetry here) in order to assess their contribution and to test the sensitivity of results to different parameters (errors, observation density, type of observations). Simulated data sets are extracted from a global free 1/12° run and assimilated in the global 1/4° modeling and data assimilation system. Using the 1/12° simulation is justified by the fact that mesoscale variability is better represented than in a 1/4° one.
The main goal is to assess how the reduction of measurement noise (SAR/LRM) and number of satellites impact the analysis and forecast errors at global and regional (i.e. Gulf Stream, Agullas Current) scales.