Submitted as: development and technical paper
06 Apr 2023
Submitted as: development and technical paper |  | 06 Apr 2023
Status: this preprint is currently under review for the journal GMD.

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 Hernandez Banos, Yonggang G. Yu, Soyoung Ha, Yannick Tremolet, Thomas Auligne, Clementine Gas, Benjamin Menetrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson

Abstract. An ensemble of three-dimensional ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS). Basic software building blocks are reused from previously presented deterministic 3DEnVar functionality, and combined with a formal experimental workflow manager in MPAS-Workflow. En-3DEnVar is used to produce an 80-member ensemble of analyses, which are cycled with ensemble forecasts in a 1-month experiment. The ensemble forecasts approximate a purely flow-dependent background error covariance (BEC) at each analysis time. The En-3DEnVar BECs and prior ensemble mean forecast errors are compared to those produced by a similar experiment that uses the Data Assimilation Research Testbed (DART) Ensemble Adjustment Kalman Filter (EAKF). The experiment using En-3DEnVar produces similar ensemble spread to and slightly smaller errors than the EAKF. The ensemble forecasts initialized from En-3DEnVar and EAKF analyses are used as BECs in deterministic cycling 3DEnVar experiments, which are compared to a control experiment that uses 20-member MPAS-A forecasts initialized from Global Ensemble Forecast System (GEFS) initial conditions. The experimental ensembles achieve mostly equivalent or better performance than the off-the-shelf ensemble system in this deterministic cycling setting; although, there are many obvious differences in configuration between GEFS and the two MPAS ensemble systems. An additional experiment that uses hybrid 3DEnVar, which combines the En-3DEnVar ensemble BEC with a climatological BEC, increases tropospheric forecast quality compared to the corresponding pure 3DEnVar experiment. The JEDI-MPAS En-3DEnVar is technically working and useful for future research studies. Tuning of observation errors and spread is needed to improve performance and several algorithmic advancements are needed to improve computational efficiency for larger-scale applications.

Jonathan J. Guerrette et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2023-54', Lili Lei, 17 Apr 2023 reply
  • RC1: 'Comment on gmd-2023-54', Anonymous Referee #1, 16 May 2023 reply

Jonathan J. Guerrette et al.

Model code and software

JEDI-MPAS Data Assimilation System v2.0.0-beta Joint Center for Satellite Data Assimilation, and National Center for Atmospheric Research

Jonathan J. Guerrette et al.


Total article views: 354 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
254 89 11 354 7 3
  • HTML: 254
  • PDF: 89
  • XML: 11
  • Total: 354
  • BibTeX: 7
  • EndNote: 3
Views and downloads (calculated since 06 Apr 2023)
Cumulative views and downloads (calculated since 06 Apr 2023)

Viewed (geographical distribution)

Total article views: 336 (including HTML, PDF, and XML) Thereof 336 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 05 Jun 2023
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.