Preprints
https://doi.org/10.5194/gmd-2022-133
https://doi.org/10.5194/gmd-2022-133
Submitted as: development and technical paper
05 Jul 2022
Submitted as: development and technical paper | 05 Jul 2022
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 1.0.0): EnVar implementation and evaluation

Zhiquan Liu1, Chris Snyder1, Jonathan J. Guerrette1, Byoung-Joo Jung1, Junmei Ban1, Steven Vahl1,a, Yali Wu1,b, Yannick Trémolet2, Thomas Auligné2, Benjamin Ménétrier2, Anna Shlyaeva2, Stephen Herbener2, Emily Liu2,c, Daniel Holdaway2,d, and Benjamin T. Johnson2 Zhiquan Liu et al.
  • 1National Center for Atmospheric Research, Boulder, Colorado 80301, USA
  • 2Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
  • anow at: the Joint Center for Satellite Data Assimilation
  • bnow at: the Shenzhen Institute of Meteorological Innovation, Guangdong, China
  • cnow at: the NOAA National Centers for Environmental Prediction
  • dnow at: the NASA Goddard Space Flight Center

Abstract. On 24th September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI), was publicly released for community use. JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble-variational (EnVar) schemes for deterministic analyses, the ensemble of DAs (EDA) technique to produce ensembles of analyses, and dual resolution, where the analysis increment and the input forecast ensemble are at a lower resolution than that of the background and analysis, thus decreasing computational requirements. Moreover, JEDI-MPAS operates directly on the native MPAS unstructured mesh and can be applied without code modifications to all MPAS meshes, whether uniform, variable resolution, global, or regional. On the observation side, one advanced feature in JEDI-MPAS is the full all-sky approach for satellite radiance DA with the introduction of hydrometeor analysis variables. This paper describes the formulation and implementation of EnVar for JEDI-MPAS. JEDI-MPAS 1.0.0 is evaluated with month-long cycling 3DEnVar experiments with a global 30 km–60 km dual-resolution configuration. The robustness and credible performance of JEDI-MPAS are demonstrated by establishing a benchmark non-radiance DA experiment, then incrementally adding microwave radiances from three sources: AMSU-A temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and MHS water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with substantial and significant positive impact obtained for almost all aspects of forecast verification when progressively adding more microwave radiance data. In particular, the day-5 forecast of the best-performing JEDI-MPAS experiment yields an anomaly correlation coefficient (ACC) of 0.8 for 500 hPa geopotential height, a gap of roughly a half day when compared to cold start forecasts initialized from operational analyses of the National Centers for Environmental Prediction, whose ACC does not drop to 0.8 until a lead time of 5.5 days. This indicates JEDI-MPAS’s great potential for both research and operations. Further development of JEDI-MPAS is ongoing, with emphasis on 3DVar, EDA, and cloud analysis from all-sky radiance observations.

Zhiquan Liu et al.

Status: open (until 30 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Zhiquan Liu et al.

Zhiquan Liu et al.

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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 ensemble-variable 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.