OceanVar is a data assimilation (DA) software which is being used in Italy within the Mediterranean Forecasting System (MFS) to combine observational data (SLA, SST, Argo-floats profiles) with backgrounds produced by computational models of ocean currents of the Mediterranean Sea (namely, the NEMO framework). OceanVAR implements a three-dimensional variational scheme. We discuss the design of a fully parallel version of OceanVar, based on domain decomposition approach, which is able to face to the ever greater multi-level parallelism and scalability of the current and the next generation of leadership computing facility systems (multi processors, many core and GPUs), while fulfilling the specific requirements of OceanVar within the MFS
This report describes the Inria contribution to WP2 regarding the use of 4D-Var in the ocean compone...
. The Swedish Meterological and Hydrological Institute (SMHI) makes daily forecasts of temperature, ...
International audienceThe convergence of variational data assimilation algorithms for high dimension...
AbstractOceanVar is a Data Assimilation (DA) software which is being used in Italy within the Medite...
OceanVar is a Data Assimilation (DA) software which is being used in Italy within the Mediterranean ...
We present a framework based on Domain Decomposition approach (we call it DD-framework), for solving...
Typical methodological approaches for designing parallel algorithms rely on the introduction of conc...
This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterra...
Earth System Models are critical tools for the study of our climate and its future trends. These mod...
This Project is placed in the scientific context of Uncertainty Quantification (UQ) in Ocean Circul...
National audienceThe key importance of data assimilation to oceanography and in particular to the de...
The present work is about the algorithms and parallel constructs of a spectral element equatorial oc...
A parallel algorithm is described for variational assimilation of observations into oceanic and atmo...
International audienceThe convergence of variational data assimilation algorithm for high dimensiona...
In this document is described the OceanVAR software (a three- dimensional variational data assimilat...
This report describes the Inria contribution to WP2 regarding the use of 4D-Var in the ocean compone...
. The Swedish Meterological and Hydrological Institute (SMHI) makes daily forecasts of temperature, ...
International audienceThe convergence of variational data assimilation algorithms for high dimension...
AbstractOceanVar is a Data Assimilation (DA) software which is being used in Italy within the Medite...
OceanVar is a Data Assimilation (DA) software which is being used in Italy within the Mediterranean ...
We present a framework based on Domain Decomposition approach (we call it DD-framework), for solving...
Typical methodological approaches for designing parallel algorithms rely on the introduction of conc...
This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterra...
Earth System Models are critical tools for the study of our climate and its future trends. These mod...
This Project is placed in the scientific context of Uncertainty Quantification (UQ) in Ocean Circul...
National audienceThe key importance of data assimilation to oceanography and in particular to the de...
The present work is about the algorithms and parallel constructs of a spectral element equatorial oc...
A parallel algorithm is described for variational assimilation of observations into oceanic and atmo...
International audienceThe convergence of variational data assimilation algorithm for high dimensiona...
In this document is described the OceanVAR software (a three- dimensional variational data assimilat...
This report describes the Inria contribution to WP2 regarding the use of 4D-Var in the ocean compone...
. The Swedish Meterological and Hydrological Institute (SMHI) makes daily forecasts of temperature, ...
International audienceThe convergence of variational data assimilation algorithms for high dimension...