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
<p>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 previou...
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Copernicus Publications
2023-12-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/7123/2023/gmd-16-7123-2023.pdf |
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| author | J. J. Guerrette J. J. Guerrette Z. Liu C. Snyder B.-J. Jung C. S. Schwartz J. Ban S. Vahl S. Vahl Y. Wu Y. Wu I. H. Baños Y. G. Yu Y. G. Yu S. Ha Y. Trémolet T. Auligné C. Gas B. Ménétrier A. Shlyaeva M. Miesch M. Miesch S. Herbener E. Liu E. Liu D. Holdaway D. Holdaway B. T. Johnson |
| author_facet | J. J. Guerrette J. J. Guerrette Z. Liu C. Snyder B.-J. Jung C. S. Schwartz J. Ban S. Vahl S. Vahl Y. Wu Y. Wu I. H. Baños Y. G. Yu Y. G. Yu S. Ha Y. Trémolet T. Auligné C. Gas B. Ménétrier A. Shlyaeva M. Miesch M. Miesch S. Herbener E. Liu E. Liu D. Holdaway D. Holdaway B. T. Johnson |
| author_sort | J. J. Guerrette |
| collection | DOAJ |
| description | <p>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.</p> |
| first_indexed | 2024-03-09T01:58:47Z |
| format | Article |
| id | doaj.art-8bad33cc031043f787e778e416a336af |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-03-09T01:58:47Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
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| series | Geoscientific Model Development |
| spelling | doaj.art-8bad33cc031043f787e778e416a336af2023-12-08T08:40:06ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-12-01167123714210.5194/gmd-16-7123-2023Data 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) assimilationsJ. J. Guerrette0J. J. Guerrette1Z. Liu2C. Snyder3B.-J. Jung4C. S. Schwartz5J. Ban6S. Vahl7S. Vahl8Y. Wu9Y. Wu10I. H. Baños11Y. G. Yu12Y. G. Yu13S. Ha14Y. Trémolet15T. Auligné16C. Gas17B. Ménétrier18A. Shlyaeva19M. Miesch20M. Miesch21S. Herbener22E. Liu23E. Liu24D. Holdaway25D. Holdaway26B. T. Johnson27Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAnow at: Tomorrow.io, Golden, CO 80401, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAnow at: Joint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAnow at: Shenzhen Institute of Meteorological Innovation, Shenzhen, ChinaMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAnow at: Science Applications International Corporation, Reston, VA 20190, USAMesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAnow at: CIRES, University of Colorado, NOAA Space Weather Prediction Center, Boulder, CO 80309, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAnow at: National Centers for Environmental Prediction, NOAA, College Park, MD 20740, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USAnow at: NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAJoint Center for Satellite Data Assimilation, University Center for Atmospheric Research, Boulder, CO 80301, USA<p>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.</p>https://gmd.copernicus.org/articles/16/7123/2023/gmd-16-7123-2023.pdf |
| spellingShingle | J. J. Guerrette J. J. Guerrette Z. Liu C. Snyder B.-J. Jung C. S. Schwartz J. Ban S. Vahl S. Vahl Y. Wu Y. Wu I. H. Baños Y. G. Yu Y. G. Yu S. Ha Y. Trémolet T. Auligné C. Gas B. Ménétrier A. Shlyaeva M. Miesch M. Miesch S. Herbener E. Liu E. Liu D. Holdaway D. Holdaway B. T. Johnson 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 Geoscientific Model Development |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://gmd.copernicus.org/articles/16/7123/2023/gmd-16-7123-2023.pdf |
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