One of the main objectives of SEAMLESS is to develop innovative open-source based generation and assimilation methods to accelerate the transition of the physical and biological components of CMEMS MFCs to probabilistic systems, with a better ability to provide products with faithful associated uncertainties. The purpose of this document is to introduce Deliverable D3.1, which consists of a toolbox (code) developed to assess the quality and relevance of model-generated ensembles and to provide probabilistic evaluation scores of ensemble assimilation methods. The toolbox is delivered as a code freely available at https://github.com/brankart/ensdam, supplemented by a user’s guide shown in annex of this document. The package has been conceiv...
The NCEP ensemble verification system was developed to evaluate ensemble based probabilistic forecas...
The Parallel Ocean Program (POP), the ocean model component of the Community Earth System Model (CES...
This is the final version. Available on open access from Taylor & Francis via the DOI in this record...
One of the main objectives of SEAMLESS is to develop innovative open-source based generation and ass...
SEAMLESS has the strong ambition to develop novel ensemble data assimilation systems exploitable ope...
The scope of this document is provide the codes of stochastic processes developed as part of Task 3....
This archive contains the technical implementation of a new probabilistic version of NEMO based on v...
During the last three decades, ensemble modelling has switched the focus from deterministic to proba...
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. ...
This document reports on (i) the developments of strongly coupled ensemble assimilation methods and ...
International audienceA cross-validation algorithm is developed to perform probabilistic observing s...
The most radical change to numerical weather prediction (NWP) during the last decade has been the op...
In the framework of the CMEMS Service Evolution SCRUM2 project (Task 2.3) presentation of the underl...
This study is anchored in the H2020 SEAMLESS project (www.seamlessproject.org), which aims to develo...
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. ...
The NCEP ensemble verification system was developed to evaluate ensemble based probabilistic forecas...
The Parallel Ocean Program (POP), the ocean model component of the Community Earth System Model (CES...
This is the final version. Available on open access from Taylor & Francis via the DOI in this record...
One of the main objectives of SEAMLESS is to develop innovative open-source based generation and ass...
SEAMLESS has the strong ambition to develop novel ensemble data assimilation systems exploitable ope...
The scope of this document is provide the codes of stochastic processes developed as part of Task 3....
This archive contains the technical implementation of a new probabilistic version of NEMO based on v...
During the last three decades, ensemble modelling has switched the focus from deterministic to proba...
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. ...
This document reports on (i) the developments of strongly coupled ensemble assimilation methods and ...
International audienceA cross-validation algorithm is developed to perform probabilistic observing s...
The most radical change to numerical weather prediction (NWP) during the last decade has been the op...
In the framework of the CMEMS Service Evolution SCRUM2 project (Task 2.3) presentation of the underl...
This study is anchored in the H2020 SEAMLESS project (www.seamlessproject.org), which aims to develo...
Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. ...
The NCEP ensemble verification system was developed to evaluate ensemble based probabilistic forecas...
The Parallel Ocean Program (POP), the ocean model component of the Community Earth System Model (CES...
This is the final version. Available on open access from Taylor & Francis via the DOI in this record...