The Second Generation of Mercator Data Assimilation System: Recent Developments and Last Results

• Main Authors: Tranchant, Benoît, Testut, Charles-Emmanuel, Ferry, Nicolas, Brasseur, Pierre
• Other Authors: Laboratoire des Écoulements Géophysiques et Industriels Grenoble (LEGI), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2005
• Subjects: [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph]; [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph]; North Atlantic
• Online Access: https://hal.science/hal-00232663

Within the last decade, operational oceanography has considerably developed in particular thanks to the favourable research and development context offered by the GODAE project. Mercator, the French operational oceanography centre, contributes to this project by developing a series of ocean analysis / forecasting systems. The Mercator ocean analysis system is based on various assimilation techniques in order to optimally combine observations (sea level anomaly, sea surface temperature, in situ T&S profiles) with ocean model simulations. At present time, a series of ocean models ranging from global (2°, 1/4°) to basin scale eddy resolving (1/15° North Atlantic) resolution use data assimilation to produce operationally ocean analyses and 2 weeks forecasts. Several assimilation techniques are employed: multi data multivariate optimal interpolation or more advanced methods such as Kalman filter or variational methods. Optimal interpolation scheme called SAM1 was the first technique used to take advantage from both in situ and remotely sensed (sea level anomaly, sea surface temperature) data through fully multi variate assimilation. Mercator also developed a second assimilation scheme called SAM2 which is a reduced order Kalman filters using 3D multivariate modal decomposition of the forecast error covariance. The use of 3D modal representation for the error statistics is intended to improve analyses in highly inhomogeneous, anisotropic and nonseparable regions of the world ocean such as shallow areas, as well as in the surface layers. Advanced parametrisation and results of this second generation of Mercator assimilation system will be presented and discussed within the framework of different hindcast experiments using two different ocean forecasting systems (Global 2° and North Atlantic °).