Burba, M.

Hollborn, S.

Knippertz, P.

Potthast, R.

Ulbrich, S.

English

KITopen

EGU21-15603, updated on 18 Jan 2022https://doi.org/10.5194/egusphere-egu21-15603EGU General Assembly 2021© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.EnVAR for ICON-LAM: observations and quality controlMareike Burba1, Sven Ulbrich1, Stefanie Hollborn1, Roland Potthast1, and Peter Knippertz21Deutscher Wetterdienst, Forschung und Entwicklung, Offenbach, Germany (mareike.burba@dwd.de)2Karlsruhe Institute of Technology, Karlsruhe, GermanyThe German Weather Service (DWD) introduces the regional NWP model ICON-LAM (ICON LimitedArea Mode) in 2021 to replace the COSMO model. For the ICON-LAM data assimilation, a novelEnVAR (Ensemble VARiational data assimilation) setup is currently evaluated in comparison to theoperational deterministic run of KENDA-LETKF (Local Ensemble Transform Kalman Filter). Thisrequires special care as the observation handling differs for the global assimilation (via EnVAR) andthe regional assimilation (KENDA). Furthermore, the variational quality control for the regionalEnVAR may require a setup differing from the global setup. We will give an introduction to theobservation processing in DWD's data assimilation framework (DACE).For future development, we give an outlook on how a regional EnVAR can be used for a regionaldeterministic analysis by using a global ICON ensemble in combination with a regionaldeterministic ICON-LAM run. This is potentially of interest for DWD's partners with smallercomputational capacities, because a regional EnVAR analysis is computationally less expensivethan running a full KENDA ensemble assimilation cycle.Powered by TCPDF (www.tcpdf.org)

EnVAR for ICON-LAM: observations and quality control

https://publikationen.bibliothek.kit.edu/1000141982/141008711

EnVAR for ICON-LAM: observations and quality control

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