Articles | Volume 15, issue 20
Geosci. Model Dev., 15, 7859–7878, 2022
https://doi.org/10.5194/gmd-15-7859-2022
Geosci. Model Dev., 15, 7859–7878, 2022
https://doi.org/10.5194/gmd-15-7859-2022
Development and technical paper
26 Oct 2022
Development and technical paper | 26 Oct 2022

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation

Zhiquan Liu et al.

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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. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009bams2618.1, 2009. a
Auligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for satellite data in a numerical weather prediction system, Q. J. Roy. Meteor. Soc., 133, 631–642, https://doi.org/10.1002/qj.56, 2007. a
Barker, D., Huang, X.-Y., Liu, Z., Auligné, T., Zhang, X., Rugg, S., Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, Y.-R., Henderson, T., Huang, W., Lin, H.-C., Michalakes, J., Rizvi, S., and Zhang, X.: The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA, B. Am. Meteorol. Soc., 93, 831–843, https://doi.org/10.1175/bams-d-11-00167.1, 2012. a
Brown, B., Jensen, T., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T., Newman, K., Adriaansen, D., Blank, L., Burek, T., Harrold, M., Hertneky, T., Kalb, C., Kucera, P., Nance, L., Opatz, J., Vigh, J., and Wolff, J.: The Model Evaluation Tools (MET): More than a Decade of Community-Supported Forecast Verification, B. Am. Meteorol. Soc., 102, E782–E807, https://doi.org/10.1175/bams-d-19-0093.1, 2021. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part II: Preliminary Model Validation, Mon. Weather Rev., 129, 587–604, https://doi.org/10.1175/1520-0493(2001)129<0587:caalsh>2.0.co;2, 2001. a
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Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.