hdl:10013/epic.4161e32d-26a2-4d80-9c42-408eaee278aa

High-dimensional nonlinear data assimilation with the nonlinear ensmeble transform filter (NETF) and its smoother extension


Nerger, Lars ORCID: https://orcid.org/0000-0002-1908-1010, Kirchgessner, Paul, Toedter, Julian and Ahrens, Bodo ;
Contact
Lars.Nerger [ at ] awi.de

Abstract


Item Type
Conference (Invited talk)
Authors
Nerger, Lars ORCID: https://orcid.org/0000-0002-1908-1010, Kirchgessner, Paul, Toedter, Julian and Ahrens, Bodo ;
Divisions
AWI Organizations > Infrastructure > Scientific Computing
Primary Division
Organizations > AWI Organizations > Infrastructure > Scientific Computing
Programs
Helmholtz Research Programs > PACES II (2014-2020) > TOPIC 4: Research in science-stakeholder interactions > WP 4.1: Operational analyses and forecasting
Primary Topic
Helmholtz Programs > Helmholtz Research Programs > PACES II (2014-2020) > TOPIC 4: Research in science-stakeholder interactions > WP 4.1: Operational analyses and forecasting
Publication Status
Published
Event Details
Seminar at National Marine Environmental Forecasting Center (NMEFC), Beijing, China, November 9, 2017.
Eprint ID
46138
Cite as
Nerger, L. , Kirchgessner, P. , Toedter, J. and Ahrens, B. (2017): High-dimensional nonlinear data assimilation with the nonlinear ensmeble transform filter (NETF) and its smoother extension , Seminar at National Marine Environmental Forecasting Center (NMEFC), Beijing, China, November 9, 2017 .


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