The federation
Intranet
Software
Probabilistic NEMO
doi:10.5281/zenodo.61611 (with NEMO 3.5)
doi:10.5281/zenodo.6303007 (with NEMO 4.0)
An ensemble/stochastic version of the NEMO ocean model. This is used to explicitly describe uncertainties in model simulations. Developed in Brankart (2013), Brankart et al. (2015), Garnier et al. (2016), Bessières et al. (2017), Leroux et al. (2022). Applied for instance in Candille et al. (2015), Germineaud et al. (2019), Tissier et al. (2019), Zanna et al. (2019), Santana-Falcon et al. (2020).
EnsDAM
Ensemble Data Assimilation Modules. This is a collection of FORTRAN modules that can be used in ensemble data assimilation systems or, more generally in ensemble estimation problems. They include the following tools :
- Ensemble Bayesian update, using an MCMC sampler, with covariance localization (Brankart, 2019),
- Ensemble augmentation, with covariance localization (Brankart, 2019),
- Ensemble anamorphosis (Béal et al., 2010 ; Brankart et al., 2012 ; Brankart, 2019)
- Ensemble scores (Candille et al., 2015 ; Germineaud et al., 2019 ; Brankart, 2019),
- Scale separation (Tissier et al., 2019),
- Generation of stochastic fields, etc.
SeSAM
System of Sequential Assimilation Modules. This is a FORTRAN program to perform various operations needed in data assimilation systems, or more generally in time-dependent inverse problems. SeSAM is a user interface to the tools of the EnsDAM library (using data from NetCDF files rather than data arrays), but also provides older tools that are not included in EnsDAM :
- Ensemble Bayesian update, using the analysis step of the SEEK filter (slightly modified to be equivalent to the analysis step of the Ensemble Tranform Kalman Filter),
- Modified algorithm using domain localization (developed in Testut et al., 2003 ; Brankart et al., 2003, and still recently applied for instance in Tissier et al., 2019 ; Germineaud et al., 2019 ; Metref et al., 2020 ; Santana-Falcon et al., 2020),
- Generalization of the algorithm to truncated Gaussian distributions (Lauvernet et al., 2009),
- Generalization of the algorithm to correlated observation errors (Brankart et al., 2009 ; Ruggiero al., 2016),
- Generalization of the algorithm to adaptive statistics (Brankart et al., 2010),
- Empirical Orthogonal Fonctions (EOF) décomposition,
- Interface to observation databases.