Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-188
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
04 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
BEATBOX: Background Error Analysis Testbed with Box Models
Christoph Knote1, Jérôme Barré2, and Max Eckl1,a 1Meteorological Institute, LMU, Munich, 80333, Germany
2Atmospheric Chemistry Observations and Modeling, NCAR, Boulder (CO), 80302, USA
anow at: Institute of Atmospheric Physics, DLR, Oberpfaffenhofen, 82234, Germany
Abstract. The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows to conduct performance evaluations of data assimilation experiments, sensitivity analyses and detailed chemical scheme diagnostics from an Observation Simulation System Experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint or combined sensitivities. A user-friendly, python-based interface allows tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, e.g. observations error, model covariances, ensemble size, perturbation distribution on initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate: the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

Citation: Knote, C., Barré, J., and Eckl, M.: BEATBOX: Background Error Analysis Testbed with Box Models, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-188, in review, 2017.
Christoph Knote et al.
Christoph Knote et al.
Christoph Knote et al.

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