Articles | Volume 17, issue 10
https://doi.org/10.5194/gmd-17-4199-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-4199-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, Colorado, USA
Jonathan J. Guerrette
National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, Colorado, USA
now at: Tomorrow.io, Golden, Colorado, USA
Ivette Hernández Baños
National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, Colorado, USA
William C. Skamarock
National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, Colorado, USA
Michael G. Duda
National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, Colorado, USA
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Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
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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.
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Heavy pollution events often occur in cloudy conditions, which is hard to observe. This study introduces a new 3D-Var analysis that can facilitate aerosol-cloud interactions through weakly coupled data assimilation using WRF-Chem/WRFDA.
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
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Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
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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.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
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Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
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Soyoung Ha
EGUsphere, https://doi.org/10.5194/egusphere-2022-371, https://doi.org/10.5194/egusphere-2022-371, 2022
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Heavy pollution events often occur in cloudy conditions, which is hard to observe. This study introduces a new 3D-Var analysis that can facilitate aerosol-cloud interactions through weakly coupled data assimilation using WRF-Chem/WRFDA.
Soyoung Ha
Geosci. Model Dev., 15, 1769–1788, https://doi.org/10.5194/gmd-15-1769-2022, https://doi.org/10.5194/gmd-15-1769-2022, 2022
Short summary
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In an effort to improve air quality forecasting, the WRFDA 3D-Var system is newly extended for the assimilation of surface PM2.5 and PM10 using the RACM/MADE-VBS chemistry in the WRF-Chem model. Through a case study during the Korea–United States Air Quality (KORUS-AQ) period, it is demonstrated that the analysis can lead to improving the prediction of surface PM concentrations up to 26 % for 24 h, diminishing most bias errors.
Cited articles
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009. a
Bhargava, K., Kalnay, E., Carton, J. A., and Yang, F.: Estimation of Systematic Errors in the GFS Using Analysis Increments, J. Geophys. Res.-Atmos., 123, 1626–1637, https://doi.org/10.1002/2017JD027423, 2018. a
Bloom, S. C., Takacs, L. L., Silva, A. M. D., and Ledvina, D.: Data assimilation using Incremental Analysis Updates, Mon. Weather Rev., 124, 1256–1271, https://doi.org/10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2, 1996. a
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L., Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J., And Zadra, A.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part I: The Global System, Mon. Weather Rev., 143, 2532–2559, https://doi.org/10.1175/MWR-D-14-00354.1, 2015. a
Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteor. Soc., 120, 1367–1387, https://doi.org/10.1002/qj.49712051912, 1994. a
Descombes, G., Auligné, T., Vandenberghe, F., Barker, D. M., and Barré, J.: Generalized background error covariance matrix model (GEN_BE v2.0), Geosci. Model Dev., 8, 669–696, https://doi.org/10.5194/gmd-8-669-2015, 2015. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Guerrette, J. J., Abdi-Oskouei, M., Ban, J., nos, I. H. B., Bresch, J., Ha, S., Jung, B.-J., Liu, Z., Snyder, C., Schwartz, C., Wu, Y., and Yu, Y.: MPAS-Workflow, Zenodo [code], https://doi.org/10.5281/zenodo.10433323, 2023a. a
Guerrette, J. J., Liu, Z., Snyder, C., Jung, B.-J., Schwartz, C. S., Ban, J., Vahl, S., Wu, Y., Baños, I. H., Yu, Y. G., Ha, S., Trémolet, Y., Auligné, T., Gas, C., Ménétrier, B., Shlyaeva, A., Miesch, M., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T.: 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, Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, 2023b. a, b, c
Ha, S., Snyder, C., Skamarock, W. C., Anderson, J. L., and Collins, N.: Ensemble Kalman filter data assimilation for the Model for Prediction Across Scales (MPAS)., Mon. Weather Rev., 145, 4673–4692, https://doi.org/10.1175/MWR-D-17-0145.1, 2017. a, b, c
Hohenegger, C. and Schär, C.: Predictability and error growth dynamics in cloud-resolving models, J. Atmos. Sci., 64, 4467–4478, https://doi.org/10.1175/2007JAS2143.1, 2007. a
Honeyager, R., Herbener, S., Zhang, X., Shlyaeva, A., and Trémolet, Y.: Observations in the Joint Effort for Data Assimilation Integration (JEDI) – Unified Forward Operator (UFO) and Interface for Observation Data Access (IODA), Quarterly Newsletter 66, JCSDA, https://doi.org/10.25923/RB19-0Q26, 2020. a, b
Ingleby, B.: Global assimilation of air temperature, humidity, wind and pressure from surface stations: practice and performance, Forecasting Research Technical Report 582, Met Office, Exeter, UK, https://digital.nmla.metoffice.gov.uk/digitalFile_e12ba2af-84e9-45f2-8f30-146ae421f45c/ (last access: 21 May 2024), 2013. a
Klemp, J. B.: A terrain-following coordinate with smoothed coordinate surfaces, Mon. Weather Rev., 139, 2163–2169, https://doi.org/10.1175/MWR-D-10-05046.1, 2011. a
Klemp, J. B., Skamarock, W. C., and Dudhia, J.: Conservative Split-Explicit Time Integration Methods for the Compressible Nonhydrostatic Equations, Mon. Weather Rev., 135, 2897–2913, https://doi.org/10.1175/MWR3440.1, 2007. a, b
Klemp, J. B., Skamarock, W. C., and Ha, S.: Damping Acoustic Modes in Compressible Horizontally Explicit Vertically Implicit (HEVI) and Split-Explicit Time Integration Schemes, Mon. Weather Rev., 146, 1911–1923, https://doi.org/10.1175/MWR-D-17-0384.1, 2018. a, b
Lei, L. and Whitaker, J. S.: A Four-Dimensional Incremental Analysis Update for the Ensemble Kalman Filter, Mon. Weather Rev., 144, 2605–2621, https://doi.org/10.1175/MWR-D-15-0246.1, 2016. a
Liu, Z., Snyder, C., Guerrette, J. J., Jung, B.-J., Ban, J., Vahl, S., Wu, Y., Trémolet, Y., Auligné, T., Ménétrier, B., Shlyaeva, A., Herbener, S., Liu, E., Holdaway, D., and Johnson, B. T.: 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, Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, 2022. a, b
Lorenc, A. C., Bowler, N. E., Clayton, A. M., and Pring, S. R.: Comparison of Hybrid-4DEnVar and Hybrid-4DVar Data Assimilation Methods for Global NWP, Mon. Weather Rev., 143, 212–229, https://doi.org/10.1175/MWR-D-14-00195.1, 2015. a
Lynch, P. and Huang, X.-Y.: Initialization of the HIRLAM model using a digital filter, Mon. Weather Rev., 120, 1019–1034, https://doi.org/10.1175/1520-0493(1992)120<1019:IOTHMU>2.0.CO;2, 1992. a
MPAS-JEDI-Team: mpas-bundle, Zenodo [code], https://doi.org/10.5281/zenodo.10433668, 2023. a
NCAR: HPE SGI ICE XA – Cheyenne, Computational and Information Systems Laboratory, https://doi.org/10.5065/D6RX99HX, 2023. a
Oliver, H., Shin, M., Matthews, D., Sanders, O., Bartholomew, S., Clark, A., Fitzpatrick, B., van Haren, R., Hut, R., and Drost, N.: Workflow Automation for Cycling Systems, Comput. Sci. Eng., 21, 7–21, https://doi.org/10.1109/mcse.2019.2906593, 2019. a
Polavarapu, S., Ren, S., Clayton, A. M., Sankey, D., and Rochon, Y.: On the Relationship between Incremental Analysis Updating and Incremental Digital Filtering, Mon. Weather Rev., 132, 2495–2502, https://doi.org/10.1175/1520-0493(2004)132<2495:OTRBIA>2.0.CO;2, 2004. a
Skamarock, W. C., Klemp, J. B., Fowler, L. D., Duda, M. G., Park, S.-H., and Ringler, T. D.: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tesselations and C-grid staggering, Mon. Weather Rev., 140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012. a, b, c
Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146, 3445–3460, https://doi.org/10.1175/MWR-D-18-0155.1, 2018. a
University Corporation for Atmospheric Research: JEDI Documentation, University Corporation for Atmospheric Research [data set], https://jointcenterforsatellitedataassimilation-jedi-docs.readthedocs-hosted.com/en/latest/overview/index.html, last access: 21 May 2024. a
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
To mitigate the imbalances in the initial conditions, this study introduces our recent...