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Ensemble Data Assimilation in NEMO using PDAF
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NEMO itself does not provide full functionality for data assimilation. To enable data assimilation with NEMO, it was coupled with the Parallel Data Assimilation Framework (PDAF, https://pdaf.awi.de). PDAF is open source software providing generic functionality for data assimilation (ensemble filters and smoothers, and variational schemes) as well as ensemble simulations, related diagnostics and tools. For computational efficiency the coupling to NEMO was performed by inserting a few subroutines in higher-level routines of NEMO, which call functions of PDAF. This scheme allows for an in-memory exchange of model fields with the data assimilation software in order to avoid excessive file outputs and model restarts. Alternatively, an offline-coupling using disk files is possible. Next to the NEMO ocean physics, also components like the sea ice or biogeochemical models can be handled, which allows for fully multivariate data assimilation.We discuss the structure and functionality of the implementation with a focus on ensemble filters. The application is exemplified using two setups; NEMO with the biogeochemistry model ERGOM configured at high resolution for the Baltic Sea, and a global eORCA1 configuration coupled with the FABM-MEDUSA biogeochemistry model.
Title: Ensemble Data Assimilation in NEMO using PDAF
Description:
NEMO itself does not provide full functionality for data assimilation.
To enable data assimilation with NEMO, it was coupled with the Parallel Data Assimilation Framework (PDAF, https://pdaf.
awi.
de).
PDAF is open source software providing generic functionality for data assimilation (ensemble filters and smoothers, and variational schemes) as well as ensemble simulations, related diagnostics and tools.
For computational efficiency the coupling to NEMO was performed by inserting a few subroutines in higher-level routines of NEMO, which call functions of PDAF.
This scheme allows for an in-memory exchange of model fields with the data assimilation software in order to avoid excessive file outputs and model restarts.
Alternatively, an offline-coupling using disk files is possible.
Next to the NEMO ocean physics, also components like the sea ice or biogeochemical models can be handled, which allows for fully multivariate data assimilation.
We discuss the structure and functionality of the implementation with a focus on ensemble filters.
The application is exemplified using two setups; NEMO with the biogeochemistry model ERGOM configured at high resolution for the Baltic Sea, and a global eORCA1 configuration coupled with the FABM-MEDUSA biogeochemistry model.
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