Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7123-2023
https://doi.org/10.5194/gmd-16-7123-2023
Development and technical paper
 | 
08 Dec 2023
Development and technical paper |  | 08 Dec 2023

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations

Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson

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Cited articles

Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteor. Soc., 90, 1283–1296, https://doi.org/10.1175/2009bams2618.1, 2009. a, b
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:aeakff>2.0.co;2, 2001. a, b
Anderson, J. L.: A Local Least Squares Framework for Ensemble Filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:allsff>2.0.co;2, 2003. a
Anderson, J. L.: A Non-Gaussian Ensemble Filter Update for Data Assimilation, Mon. Weather Rev., 138, 4186–4198, https://doi.org/10.1175/2010mwr3253.1, 2010. a
Anderson, J. L.: A Marginal Adjustment Rank Histogram Filter for Non-Gaussian Ensemble Data Assimilation, Mon. Weather Rev., 148, 3361–3378, https://doi.org/10.1175/mwr-d-19-0307.1, 2020. a
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Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
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