Building Ensemble-Based Data Assimilation Systems for Coupled Models


Contact
Lars.Nerger [ at ] awi.de

Abstract

A direct connection between a coupled model system and ensemble data assimilation software allows to set up a data assimilation program with high flexibility, efficiency, and parallel scalability. The direct connection can be obtained by extending the source code of the coupled model to create an online-coupled assimilation program. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard, the direct connection avoids the need to stop and restart a whole coupled model system to perform the assimilation of observations in the analysis step of ensemble-based filter methods. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler. This strategy allows us to perform both in-compartment (for weakly coupled assimilation) and cross-compartment (for strongly coupled assimilation) assimilation. The assimilation frequency can be kept flexible, so that assimilation of observations from different compartments can be performed at different intervals. Using the parallel data assimilation framework (PDAF, http://pdaf.awi.de), the online connection strategy will be exemplified for coupled models using a single executable and such that use multiple executables for different compartments and a model coupler as in the case of the OASIS-MCT coupled climate model ECHAM6-FESOM.



Item Type
Conference (Talk)
Authors
Divisions
Primary Division
Programs
Primary Topic
Publication Status
Published
Event Details
International Workshop on Coupled Data Assimilation, Toulouse, France, October 18 - 21, 2016.
Eprint ID
42026
Cite as
Nerger, L. (2016): Building Ensemble-Based Data Assimilation Systems for Coupled Models , International Workshop on Coupled Data Assimilation, Toulouse, France, October 18 - 21, 2016 .


Download
[thumbnail of Nerger_CoupledDASystem_Toulouse2016.pdf]
Preview
PDF
Nerger_CoupledDASystem_Toulouse2016.pdf

Download (2MB) | Preview
Cite this document as:

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Geographical region
N/A

Research Platforms
N/A

Campaigns
N/A


Actions
Edit Item Edit Item