Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system
|Author(s)||Lellouche Jean-Michel1, Greiner Eric2, Le Galloudec Olivier1, Garric Gilles1, Regnier Charly1, Drevillon Marie1, Benkiran Mounir1, Testut Charles-Emmanuel1, Bourdalle-Badie Romain1, Gasparin Florent1, Hernandez Olga1, Levier Bruno1, Drilled Yann1, Remy Elisabeth1, Le Traon Pierre-Yves1, 3|
|Affiliation(s)||1 : Mercator Ocean, Ramonville St Agne, France.
2 : Collecte Localisat Satellites, Ramonville St Agne, France.
3 : IFREMER, F-29280 Plouzane, France.
|Source||Ocean Science (1812-0784) (Copernicus Gesellschaft Mbh), 2018-09 , Vol. 14 , N. 5 , P. 1093-1126|
|WOS© Times Cited||160|
|Note||Special issue The Copernicus Marine Environment Monitoring Service (CMEMS): scientific advances Editor(s): J. M. Huthnance, P.-Y. Le Traon, A. Melet, M. Tonani, E. Stanev, M. Grégoire, and A. Pascual|
Since 19 October 2016, and in the framework of Copernicus Marine Environment Monitoring Service (CMEMS), Mercator Ocean has delivered real-time daily services (weekly analyses and daily 10-day forecasts) with a new global 1∕12° high-resolution (eddy-resolving) monitoring and forecasting system. The model component is the NEMO platform driven at the surface by the IFS ECMWF atmospheric analyses and forecasts. Observations are assimilated by means of a reduced-order Kalman filter with a three-dimensional multivariate modal decomposition of the background error. Along-track altimeter data, satellite sea surface temperature, sea ice concentration, and in situ temperature and salinity vertical profiles are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-VAR scheme provides a correction for the slowly evolving large-scale biases in temperature and salinity.
This paper describes the recent updates applied to the system and discusses the importance of fine tuning an ocean monitoring and forecasting system. It details more particularly the impact of the initialization, the correction of precipitation, the assimilation of climatological temperature and salinity in the deep ocean, the construction of the background error covariance and the adaptive tuning of observation error on increasing the realism of the analysis and forecasts.
The scientific assessment of the ocean estimations are illustrated with diagnostics over some particular years, assorted with time series over the time period 2007–2016. The overall impact of the integration of all updates on the product quality is also discussed, highlighting a gain in performance and reliability of the current global monitoring and forecasting system compared to its previous version.