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

On 24 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. Operating directly on the native MPAS unstructured mesh, JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble-variational (EnVar) schemes as well as the ensemble of DA (EDA) technique. 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 30-60 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: Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and Microwave Humidity Sounder (MHS) water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with a 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 d. This indicates JEDI-MPAS's great potential for both research and operations.

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Author Liu, Zhiquan
Snyder, Chris
Guerrette, Jonathan J.
Jung, Byoung-Joo
Ban, Junmei
Vahl, Steven
Wu, Yali
Trémolet, Yannick
Auligné, Thomas
Ménétrier, Benjamin
Shlyaeva, Anna
Herbener, Stephen
Liu, Emily
Holdaway, Daniel
Johnson, Benjamin T.
Publisher UCAR/NCAR - Library
Publication Date 2022-10-26T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2023-08-18T18:41:49.790540
Metadata Record Identifier edu.ucar.opensky::articles:25844
Metadata Language eng; USA
Suggested Citation Liu, Zhiquan, Snyder, Chris, Guerrette, Jonathan J., Jung, Byoung-Joo, Ban, Junmei, Vahl, Steven, Wu, Yali, Trémolet, Yannick, Auligné, Thomas, Ménétrier, Benjamin, Shlyaeva, Anna, Herbener, Stephen, Liu, Emily, Holdaway, Daniel, Johnson, Benjamin T.. (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. UCAR/NCAR - Library. Accessed 04 October 2023.

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