• Bannister, R., 2008a: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. Quart. J. Roy. Meteor. Soc., 134, 19511970.

    • Search Google Scholar
    • Export Citation
  • Bannister, R., 2008b: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics. Quart. J. Roy. Meteor. Soc., 134, 19711996.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., , W. Huang, , Y.-R. Guo, , A. J. Bourgeois, , and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914.

    • Search Google Scholar
    • Export Citation
  • Berre, L., 2000: Estimation of synoptic and mesoscale forecast error cavariances in a limited area model. Mon. Wea. Rev., 128, 644667.

    • Search Google Scholar
    • Export Citation
  • Berre, L., , and G. Desroziers, 2010: Filtering of background error variances and correlations by local spatial averaging: A review. Mon. Wea. Rev., 138, 36933720.

    • Search Google Scholar
    • Export Citation
  • Berre, L., , S. E. Stefánescu, , and M. B. Pereira, 2006: The representation of the analysis effect in three error simulation techniques. Tellus, 58A, 196209.

    • Search Google Scholar
    • Export Citation
  • Berre, L., , O. Pannekoucke, , G. Desroziers, , S. Stefanescu, , B. Chapnik, , and L. Raynaud, 2007: A variational assimilation ensemble and the spatial filtering of its error covariances: Increase of sample size by local spatial averaging. Proc. ECMWF Workshop on Flow-Dependent Aspects of Data Assimilation, Reading, United Kingdom, ECMWF, 151–168.

  • Bonavita, M., , L. Raynaud, , and L. Isaksen, 2011: Estimating background-error variances with the ECMWF ensemble of data assimilations system: Some effects of ensemble size and day-to-day variability. Quart. J. Roy. Meteor. Soc., 137, 423434.

    • Search Google Scholar
    • Export Citation
  • Brousseau, P., , L. Berre, , F. Bouttier, , and G. Desroziers, 2011: Background error covariances for a convective scale data assimilation system AROME-France 3DVAR. Quart. J. Roy. Meteor. Soc., 137, 409422.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operation NWP settings. Quart. J. Roy. Meteor. Soc., 131, 10131043.

    • Search Google Scholar
    • Export Citation
  • Caron, J.-F., , and L. Fillion, 2010: An examination of background error correlations between mass and rotational wind over precipitation regions. Mon. Wea. Rev., 138, 563578.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., , J.-N. Thépaut, , and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387.

    • Search Google Scholar
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 460 pp.

  • Deckmyn, A., , and L. Berre, 2005: A wavelet approach to representing background error covariances in a limited-area model. Mon. Wea. Rev., 133, 12791294.

    • Search Google Scholar
    • Export Citation
  • Derber, J., , and F. Bouttier, 1999: A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus, 51A, 195221.

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., , L. Berre, , O. Pannekoucke, , S. Stefanescu, , P. Brousseau, , L. Auger, , B. Chapnik, , and L. Raynaud, 2008: Flow-dependent error covariances from variational assimilation ensembles on global and regional domains. HIRLAM Tech. Rep. 68, 17 pp.

  • Fisher, M., 2003: Background error modelling. Proc. ECMWF Seminar on Recent Developments in Data Assimilation for Atmosphere and Ocean, Reading, United Kingdom, ECMWF, 45–63.

  • Houtekamer, P. L., , L. Lefaivre, , J. Derome, , H. Ritchie, , and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev., 124, 12251242.

    • Search Google Scholar
    • Export Citation
  • Martinet, P., , N. Fourrié, , V. Guidard, , Rabier, , T. Montmerle, , and P. Brunel, 2012: Towards the use of microphysical variables for the assimilation of cloud-affected infrared radiances. Quart. J. Roy. Meteor. Soc., in press.

    • Search Google Scholar
    • Export Citation
  • Ménétrier, B., , and T. Montmerle, 2011: Heterogeneous background-error covariances for the analysis and forecast of fog events. Quart. J. Roy. Meteor. Soc., 137, 20042013.

    • Search Google Scholar
    • Export Citation
  • Michel, Y., , T. Auligné, , and T. Montmerle, 2011: Heterogeneous convective scale background error covariances with the inclusion of hydrometeor variables. Mon. Wea. Rev., 139, 29943015.

    • Search Google Scholar
    • Export Citation
  • Montmerle, T., , and C. Faccani, 2009: Mesoscale assimilation of radial velocities from Doppler radar in a preoperational framework. Mon. Wea. Rev., 137, 19371953.

    • Search Google Scholar
    • Export Citation
  • Montmerle, T., , and L. Berre, 2010: Diagnosis and formulation of heterogeneous background error covariances at mesoscale. Quart. J. Roy. Meteor. Soc., 136, 14081420.

    • Search Google Scholar
    • Export Citation
  • Pagé, C., , L. Fillion, , and P. Zwack, 2007: Diagnosing summertime mesoscale vertical motion: Implications for atmospheric data assimilation. Mon. Wea. Rev., 135, 20762094.

    • Search Google Scholar
    • Export Citation
  • Parrish, D., , J. Derber, , R. Purser, , W.-S. Wu, , and Z.-X. Pu, 1997: The NCEP global analysis system: Recent improvements and future plans. J. Meteor. Soc. Japan, 75, 359365.

    • Search Google Scholar
    • Export Citation
  • Pereira, M. B., , and L. Berre, 2006: The use of an ensemble approach to study the background error covariances in a global NWP model. Mon. Wea. Rev., 134, 24662489.

    • Search Google Scholar
    • Export Citation
  • Raynaud, L., , L. Berre, , and G. Desroziers, 2009: Objective filtering of ensemble-based background error variances. Quart. J. Roy. Meteor. Soc., 135, 11771199.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wattrelot, E., , O. Caumont, , S. Pradier-Vabre, , M. Jurasek, , and G. Haase, 2008: 1D+3DVar assimilation of radar reflectivities in the pre-operational AROME model at Météo-France. Proc. Fifth European Conf. on Radar in Meteorology and Hydrology, Helsinki, Finland, 6 pp.

  • Zhang, F., , Y. Weng, , J. Sippel, , Z. Meng, , and C. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter: Humberto (2007). Mon. Wea. Rev., 137, 21052125.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 21 21 3
PDF Downloads 16 16 1

Optimization of the Assimilation of Radar Data at the Convective Scale Using Specific Background Error Covariances in Precipitation

View More View Less
  • 1 Centre National de Recherches Météorologiques-Groupe d’étude de l’Atmosphère Metéorologique, Toulouse, France
© Get Permissions
Restricted access

Abstract

This study focuses on the impact of using specific background error covariances in precipitating areas in the Application of Research to Operations at Mesoscale (AROME-France) numerical weather prediction (NWP) system that considers reflectivities and radial velocities in its assimilation system. Such error covariances are deduced from the application of geographical masks on forecast differences generated from an ensemble assimilation of various precipitating cases. The retrieved forecast error covariances are then applied in an incremental three-dimensional variational data assimilation (3D-Var) specifically in rainy areas, in addition to the operational climatological background error covariances that are used elsewhere. Such heterogeneous formulation gives better balanced and more realistic analysis increments, as retrieved from the assimilation of radar data. For instance, midlevel humidification allows for the reinforcement of the low-level cooling and convergence, the warming in clouds, and high-level divergence. Smaller forecast error horizontal lengths explain the smaller-scale structures of the increments and render possible the increase of data densities in rainy areas. Larger error variances for the dynamical variables give more weight to wind observations such as radial winds. A reduction of the spinup is also shown and is positively correlated to the size of the area where rainy forecast error covariances are applied. Positive forecast scores on cumulated rain and on low-level temperature and humidity are finally displayed.

Corresponding author address: Thibaut Montmerle, Météo-France/CNRM-GAME/GMAP, 42 av. G. Coriolis, 31057, Toulouse, France. E-mail: thibaut.montmerle@meteo.fr

Abstract

This study focuses on the impact of using specific background error covariances in precipitating areas in the Application of Research to Operations at Mesoscale (AROME-France) numerical weather prediction (NWP) system that considers reflectivities and radial velocities in its assimilation system. Such error covariances are deduced from the application of geographical masks on forecast differences generated from an ensemble assimilation of various precipitating cases. The retrieved forecast error covariances are then applied in an incremental three-dimensional variational data assimilation (3D-Var) specifically in rainy areas, in addition to the operational climatological background error covariances that are used elsewhere. Such heterogeneous formulation gives better balanced and more realistic analysis increments, as retrieved from the assimilation of radar data. For instance, midlevel humidification allows for the reinforcement of the low-level cooling and convergence, the warming in clouds, and high-level divergence. Smaller forecast error horizontal lengths explain the smaller-scale structures of the increments and render possible the increase of data densities in rainy areas. Larger error variances for the dynamical variables give more weight to wind observations such as radial winds. A reduction of the spinup is also shown and is positively correlated to the size of the area where rainy forecast error covariances are applied. Positive forecast scores on cumulated rain and on low-level temperature and humidity are finally displayed.

Corresponding author address: Thibaut Montmerle, Météo-France/CNRM-GAME/GMAP, 42 av. G. Coriolis, 31057, Toulouse, France. E-mail: thibaut.montmerle@meteo.fr
Save