AROME-MetCoOp: A Nordic Convective-Scale Operational Weather Prediction Model

Malte Müller Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Malte Müller in
Current site
Google Scholar
PubMed
Close
,
Mariken Homleid Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Mariken Homleid in
Current site
Google Scholar
PubMed
Close
,
Karl-Ivar Ivarsson Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Search for other papers by Karl-Ivar Ivarsson in
Current site
Google Scholar
PubMed
Close
,
Morten A. Ø. Køltzow Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Morten A. Ø. Køltzow in
Current site
Google Scholar
PubMed
Close
,
Magnus Lindskog Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Search for other papers by Magnus Lindskog in
Current site
Google Scholar
PubMed
Close
,
Knut Helge Midtbø Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Knut Helge Midtbø in
Current site
Google Scholar
PubMed
Close
,
Ulf Andrae Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Search for other papers by Ulf Andrae in
Current site
Google Scholar
PubMed
Close
,
Trygve Aspelien Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Trygve Aspelien in
Current site
Google Scholar
PubMed
Close
,
Lars Berggren Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Search for other papers by Lars Berggren in
Current site
Google Scholar
PubMed
Close
,
Dag Bjørge Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Dag Bjørge in
Current site
Google Scholar
PubMed
Close
,
Per Dahlgren European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Search for other papers by Per Dahlgren in
Current site
Google Scholar
PubMed
Close
,
Jørn Kristiansen Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Jørn Kristiansen in
Current site
Google Scholar
PubMed
Close
,
Roger Randriamampianina Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Roger Randriamampianina in
Current site
Google Scholar
PubMed
Close
,
Martin Ridal Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

Search for other papers by Martin Ridal in
Current site
Google Scholar
PubMed
Close
, and
Ole Vignes Norwegian Meteorological Institute, Oslo, Norway

Search for other papers by Ole Vignes in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Malte Müller, maltem@met.no

Abstract

Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Malte Müller, maltem@met.no
Save
  • Bauer, P., and Coauthors, 2013: Model cycle 38r2: Components and performance. ECMWF Tech. Memo. 704, 58 pp. [Available online at http://www.ecmwf.int/en/elibrary/7986-model-cycle-38r2-components-and-performance.]

  • Berre, L., 2000: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model. Mon. Wea. Rev., 128, 644667, doi:10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brossier, C. L., V. Ducrocq, and H. Giordani, 2009: Two-way one-dimensional high-resolution air–sea coupled modelling applied to Mediterranean heavy rain events. Quart. J. Roy. Meteor. Soc., 135, 187204, doi:10.1002/qj.338.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brousseau, P., L. Berre, F. Bouttier, and G. Desroziers, 2012: Flow-dependent background-error covariances for a convective-scale data assimilation system. Quart. J. Roy. Meteor. Soc., 138, 310322, doi:10.1002/qj.920.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Caumont, O., V. Ducrocq, E. Wattrelot, G. Jaubert, and S. Pradier-Vabre, 2010: 1D+3DVar assimilation of radar reflectivity data: A proof of concept. Tellus, 62, 173187, doi:10.1111/j.1600-0870.2009.00430.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, W., K.-S. Chung, L. Fillion, and S.-J. Baek, 2014: Radar data assimilation in the Canadian high-resolution ensemble Kalman filter system: Performance and verification with real summer cases. Mon. Wea. Rev., 142, 21182138, doi:10.1175/MWR-D-13-00291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dahlgren, P., 2013: A comparison of two large scale blending methods: Jk and LSMIXBC. METCOOP Memo. 2, Norwegian Meteorological Institute and Swedish Meteorological and Hydrological Institute, 10 pp. [Available online at http://metcoop.org/memo/2013/02-2013-METCOOP-MEMO.PDF.]

  • Dahlgren, P., and N. Gustafsson, 2012: Assimilating host model information into a limited area model. Tellus, 64, 15836, doi:10.3402/tellusa.v64i0.15836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, H. C., 1976: A lateral boundary formulation for multi-level prediction models. Quart. J. Roy. Meteor. Soc., 102, 405418, doi:10.1002/qj.49710243210.

    • Search Google Scholar
    • Export Citation
  • Dee, D., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233334, doi:10.1256/qj.05.137.

  • Donier, S. Y., S. Faroux, and V. Masson, 2012: Evaluation of the impact of the use of the ECOCLIMAP2 database on AROME operational forecasts. Météo-France Tech. Rep., 89 pp. [Available online at http://www.umr-cnrm.fr/surfex/IMG/pdf/test_eco2_arome.pdf.]

  • Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, 2012: The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system. Remote Sens. Environ., 116, 140158, doi:10.1016/j.rse.2010.10.017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2008: Fuzzy verification of high-resolution gridded forecasts: A review and proposed framework. Meteor. Appl., 15, 5164, doi:10.1002/met.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Faroux, S., A. T. Kaptué Tchuenté, J.-L. Roujean, V. Masson, E. Martin, and P. Le Moigne, 2013: ECOCLIMAP-II/Europe: A twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models. Geosci. Model Dev., 6, 563582, doi:10.5194/gmd-6-563-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guidard, V., and C. Fischer, 2008: Introducing the coupling information in a limited-area variational assimilation. Quart. J. Roy. Meteor. Soc., 134, 723735, doi:10.1002/qj.215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Homleid, M., and M. A. Killie, 2013: HARMONIE snow analysis experiments with additional observations. Norwegian Meteorological Institute Tech. Rep. 6, 21 pp. [Available online at https://met.no/filestore/snow_report.pdf.]

  • Kristiansen, J., S. L. Sørland, T. Iversen, D. Bjørge, and M. O. Køltzow, 2011: High-resolution ensemble prediction of a polar low development. Tellus, 63A, 585604, doi:10.1111/j.1600-0870.2010.00498.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, V., J.-L. Champeaux, F. Chauvin, C. Meriguet, and R. Lacaze, 2003: A global database of land surface parameters at 1-km resolution in meteorological and climate models. J. Climate, 16, 12611282, doi:10.1175/1520-0442-16.9.1261.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, V., and Coauthors, 2013: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes. Geosci. Model Dev., 6, 929960, doi:10.5194/gmd-6-929-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montmerle, T., and L. Berre, 2010: Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale. Quart. J. Roy. Meteor. Soc., 136, 14081420, doi:10.1002/qj.655.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinty, J.-P., and P. Jabouille, 1998: A mixed-phased cloud parameterization for use in a mesoscale non-hydrostatic model: Simulations of a squall line and of orographic precipitation. Preprints, Conf. on Cloud Physics, Everett, WA, Amer. Meteor. Soc., 217–220.

  • Randriamampianina, R., 2006: Impact of high resolution observations in the ALADIN/HU model. Idojaras, 110, 329349.

  • Randriamampianina, R., T. Iversen, and A. Storto, 2011: Exploring the assimilation of IASI radiances in forecasting polar lows. Quart. J. Roy. Meteor. Soc., 137, 17001715, doi:10.1002/qj.838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, doi:10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sánchez Arriola, J., M. Lindskog, S. Thorsteinsson, and J. Bojarova, 2016: Variational bias correction of GNSS ZTD in the HARMONIE modeling system. J. Appl. Meteor. Climatol., 55, 12591276, doi:10.1175/JAMC-D-15-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015: A real-time convection-allowing ensemble prediction system initialized by mesoscale ensemble Kalman filter analyses. Wea. Forecasting, 30, 11581181, doi:10.1175/WAF-D-15-0013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976991, doi:10.1175/2010MWR3425.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and D. M. Burridge, 1981: An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon. Wea. Rev., doi:109, 758766, 10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., 2005: Convective-scale assimilation of radar data: progress and challenges. Quart. J. Roy. Meteor. Soc., 131, 34393463, doi:10.1256/qj.05.149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taillefer, F., 2002: CANARI: Technical documentation. CNRM/GMAP Internal Rep., Météo-France, 55 pp. [Available online at http://www.cnrm.meteo.fr/gmapdoc/spip.php?article3.]

  • Valkonen, T., H. Schyberg, and J. Figa-Saldana, 2016: Assimilating Advanced Scatterometer winds in a high-resolution limited area model over northern Europe. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., PP (99), 1–12, doi:10.1109/JSTARS.2016.2602889.

    • Search Google Scholar
    • Export Citation
  • Von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 36643673, doi:10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wattrelot, E., O. Caumont, and J.-F. Mahfouf, 2014: Operational implementation of the 1D+3D-Var assimilation method of radar reflectivity data in the AROME model. Mon. Wea. Rev., 142, 18521873, doi:10.1175/MWR-D-13-00230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3727 1201 158
PDF Downloads 1926 540 61