edu.ucar.opensky::articles:27006 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T18:59:06.328903 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7n30237 https://doi.org/10.5065/39C5-Z211 https Related Dataset #1 NCEP ADP Global Upper Air Observational Weather Data, October 1999 - continuing https://doi.org/10.5065/DWYZ-Q852 https Related Dataset #2 NCEP GDAS Satellite Data 2004-continuing https://doi.org/10.5065/Z83F-N512 https Related Dataset #3 NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format) https://doi.org/10.5065/D6RX99HX https Related Service #1 Cheyenne: SGI ICE XA Cluster https://doi.org/10.5281/zenodo.7630054 https Related Software #1 JEDI-MPAS Data Assimilation System v2.0.0-beta 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 2023-12-08T00:00:00Z publication https://n2t.org/ark:/85065/d7n30237 https://doi.org/10.5194/gmd-16-7123-2023 Guerrette, Jonathan author Liu, Zhiquan author Snyder, Chris author Jung, Byoung-Joo author Schwartz, Craig S. author Ban, Junmei author Vahl, Steven author Wu, Yali author Baños, Ivette Hernández author Yu, Yonggang author Ha, So-Young author Tremolet, Yannick author Auligne, Thomas D. author Hardy Gas, Clementine author Ménétrier, Benjamin author Shlyaeva, Anna author Miesch, Mark author Herbener, Stephen author Liu, Huichun author Holdaway, Daniel author Johnson, Benjamin T. author UCAR/NCAR - Library publisher An ensemble of 3D 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 a 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation