Building a Scalable Ensemble Data Assimilation System for Coupled Models
Efficient ensemble data assimilation with coupled models poses particular challenges due to the comp lexity of the model system and due to its high computational cost. On the methodological side, one h as to account for different time scales, but also distinct correlation lengths, of different model...
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ftawi:oai:epic.awi.de:46135 2024-09-15T18:35:36+00:00 Building a Scalable Ensemble Data Assimilation System for Coupled Models Nerger, Lars Sidorenko, Dmitry 2017 application/pdf https://epic.awi.de/id/eprint/46135/ https://epic.awi.de/id/eprint/46135/1/Nerger_CoupledDASystem_exp.pdf https://hdl.handle.net/10013/epic.2885169c-d08c-4158-b0bd-5759100052c3 unknown https://epic.awi.de/id/eprint/46135/1/Nerger_CoupledDASystem_exp.pdf Nerger, L. orcid:0000-0002-1908-1010 and Sidorenko, D. orcid:0000-0001-8579-6068 (2017) Building a Scalable Ensemble Data Assimilation System for Coupled Models , 7. WMO Symposium on Data Assimilation, Florianopolis, Brazil, 11 - 15 September 2017 . hdl:10013/epic.2885169c-d08c-4158-b0bd-5759100052c3 EPIC37. WMO Symposium on Data Assimilation, Florianopolis, Brazil, 11 - 15 September 2017 Conference notRev 2017 ftawi 2024-06-24T04:18:50Z Efficient ensemble data assimilation with coupled models poses particular challenges due to the comp lexity of the model system and due to its high computational cost. On the methodological side, one h as to account for different time scales, but also distinct correlation lengths, of different model c ompartments like the ocean and the atmosphere. Computationally, one often has to deal with multiple program executables, a coupler software, observation handling for different model compartments, and a large number of processors required to compute a complex coupled model. This contribution focuses on the computational aspects. Discussed are the steps required to build a highly scalable and flexible data assimilation system can be built on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AW I-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model coupling is implemented with OASIS-MCT and the model system consists of two separate executable programs for the ocean and atmosphere. Next to the implementation steps, the scalability of the assimilation system is discussed with a realistic configuration of AWI-CM. The high scalability is obtained by an online-connection strategy for the data assimilation system. First, the parallelization of the coupled model system is modified so that the coupled model can perform ensemble forecasts. Second, the analysis (solver) step is directly inserted into the time-stepping loops of each model compartment. Augmenting the coupled model in this online way, the ensemble information is kept in memory and transferred by parallel communication when necessary. Thus, one avoids the need to repeatedly write an ensemble of model fields into files and read them again for the analysis step. Further, the coupled model is only started once and there is no need ... Conference Object Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
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description |
Efficient ensemble data assimilation with coupled models poses particular challenges due to the comp lexity of the model system and due to its high computational cost. On the methodological side, one h as to account for different time scales, but also distinct correlation lengths, of different model c ompartments like the ocean and the atmosphere. Computationally, one often has to deal with multiple program executables, a coupler software, observation handling for different model compartments, and a large number of processors required to compute a complex coupled model. This contribution focuses on the computational aspects. Discussed are the steps required to build a highly scalable and flexible data assimilation system can be built on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AW I-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model coupling is implemented with OASIS-MCT and the model system consists of two separate executable programs for the ocean and atmosphere. Next to the implementation steps, the scalability of the assimilation system is discussed with a realistic configuration of AWI-CM. The high scalability is obtained by an online-connection strategy for the data assimilation system. First, the parallelization of the coupled model system is modified so that the coupled model can perform ensemble forecasts. Second, the analysis (solver) step is directly inserted into the time-stepping loops of each model compartment. Augmenting the coupled model in this online way, the ensemble information is kept in memory and transferred by parallel communication when necessary. Thus, one avoids the need to repeatedly write an ensemble of model fields into files and read them again for the analysis step. Further, the coupled model is only started once and there is no need ... |
format |
Conference Object |
author |
Nerger, Lars Sidorenko, Dmitry |
spellingShingle |
Nerger, Lars Sidorenko, Dmitry Building a Scalable Ensemble Data Assimilation System for Coupled Models |
author_facet |
Nerger, Lars Sidorenko, Dmitry |
author_sort |
Nerger, Lars |
title |
Building a Scalable Ensemble Data Assimilation System for Coupled Models |
title_short |
Building a Scalable Ensemble Data Assimilation System for Coupled Models |
title_full |
Building a Scalable Ensemble Data Assimilation System for Coupled Models |
title_fullStr |
Building a Scalable Ensemble Data Assimilation System for Coupled Models |
title_full_unstemmed |
Building a Scalable Ensemble Data Assimilation System for Coupled Models |
title_sort |
building a scalable ensemble data assimilation system for coupled models |
publishDate |
2017 |
url |
https://epic.awi.de/id/eprint/46135/ https://epic.awi.de/id/eprint/46135/1/Nerger_CoupledDASystem_exp.pdf https://hdl.handle.net/10013/epic.2885169c-d08c-4158-b0bd-5759100052c3 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
EPIC37. WMO Symposium on Data Assimilation, Florianopolis, Brazil, 11 - 15 September 2017 |
op_relation |
https://epic.awi.de/id/eprint/46135/1/Nerger_CoupledDASystem_exp.pdf Nerger, L. orcid:0000-0002-1908-1010 and Sidorenko, D. orcid:0000-0001-8579-6068 (2017) Building a Scalable Ensemble Data Assimilation System for Coupled Models , 7. WMO Symposium on Data Assimilation, Florianopolis, Brazil, 11 - 15 September 2017 . hdl:10013/epic.2885169c-d08c-4158-b0bd-5759100052c3 |
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1810478793893085184 |