• Aksoy, A., , D. C. Dowell, , and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 12731292, doi:10.1175/2009MWR3086.1.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., , and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 27412758, doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Baldauf, M., , A. Seifert, , J. Förstner, , D. Majewski, , M. Raschendorfer, , and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, doi:10.1175/MWR-D-10-05013.1.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , S.-Y. Ha, , J. Hacker, , A. Fournier, , and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 19721975, doi:10.1175/2010MWR3595.1.

    • Search Google Scholar
    • Export Citation
  • Bierdel, L., , P. Friederichs, , and S. Bentzien, 2012: Spatial kinetic energy spectra in the convection-permitting limited-area NWP model COSMO-DE. Meteor. Z., 21, 245258, doi:10.1127/0941-2948/2012/0319.

    • Search Google Scholar
    • Export Citation
  • Bishop, C. H., , T. R. Holt, , J. Nachamkin, , S. Chen, , J. G. McLay, , J. D. Doyle, , and W. T. Thompson, 2009: Regional ensemble forecasts using the ensemble transform technique. Mon. Wea. Rev., 137, 288298, doi:10.1175/2008MWR2559.1.

    • Search Google Scholar
    • Export Citation
  • Bonavita, M., , L. Torrisi, , and F. Marcucci, 2010: Ensemble data assimilation with the CNMCA regional forecasting system. Quart. J. Roy. Meteor. Soc., 136, 132145, doi:10.1002/qj.553.

    • Search Google Scholar
    • Export Citation
  • Bouttier, F., , B. Vié, , O. Nuissier, , and L. Raynaud, 2012: Impact of stochastic physics in a convection-permitting ensemble. Mon. Wea. Rev., 140, 37063721, doi:10.1175/MWR-D-12-00031.1.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., , and K. R. Mylne, 2009: Ensemble transform Kalman filter perturbations for a regional ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 757766, doi:10.1002/qj.404.

    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., , A. Arribas, , K. R. Mylne, , K. B. Robertson, , and S. E. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722, doi:10.1002/qj.234.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., , G. P. Houtekamer, , Z. Toth, , M. Wei, , and Y. Zhu, 2005: A comparison of the ECMWF, MSC, and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133, 10761097, doi:10.1175/MWR2905.1.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., , M. Leutbecher, , L. Isaksen, , and J. Haseler, 2010: Combined use of EDA- and SV-based perturbations in the EPS. ECMWF Newsletter, No. 123, ECMWF, Reading, United Kingdom, 2228.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and et al. , 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 14101418, doi:10.1175/2010MWR3624.1.

    • Search Google Scholar
    • Export Citation
  • Errico, R. M., 1985: Spectra computed from a limited area grid. Mon. Wea. Rev., 113, 15541562, doi:10.1175/1520-0493(1985)113<1554:SCFALA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343367, doi:10.1007/s10236-003-0036-9.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., , and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation
  • Gebhardt, C., , S. Theis, , P. Krahe, , and V. Renner, 2008: Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett., 9, 6772, doi:10.1002/asl.177.

    • Search Google Scholar
    • Export Citation
  • Gebhardt, C., , S. Theis, , M. Paulat, , and Z. Ben Bouallègue, 2011: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res., 100, 168177, doi:10.1016/j.atmosres.2010.12.008.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., , J. S. Whitaker, , and C. Snyder, 2001: Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Mon. Wea. Rev., 129, 27762790, doi:10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., , and C. Schär, 2007a: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 44674478, doi:10.1175/2007JAS2143.1.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., , and C. Schär, 2007b: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793, doi:10.1175/BAMS-88-11-1783.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., , A. Walser, , W. Langhans, , and C. Schär, 2008: Cloud-resolving ensemble simulations of the August 2005 Alpine flood. Quart. J. Roy. Meteor. Soc., 134, 889904, doi:10.1002/qj.252.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P., , and H. L. Mitchell, 2005: Ensemble Kalman filtering. Quart. J. Roy. Meteor. Soc., 131, 32693289, doi:10.1256/qj.05.135.

    • Search Google Scholar
    • Export Citation
  • Hunt, B. R., , E. J. Kostelich, , and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112126, doi:10.1016/j.physd.2006.11.008.

    • Search Google Scholar
    • Export Citation
  • Kostka, P. M., , M. Weissmann, , R. Buras, , B. Mayer, , and O. Stiller, 2014: Observation operator for visible and near-infrared satellite reflectances. J. Atmos. Oceanic Technol., 31, 12161233, doi:10.1175/JTECH-D-13-00116.1.

    • Search Google Scholar
    • Export Citation
  • Kühnlein, C., , C. Keil, , G. C. Craig, , and C. Gebhardt, 2014: The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation. Quart. J. Roy. Meteor. Soc., 140, 15521562, doi:10.1002/qj.2238.

    • Search Google Scholar
    • Export Citation
  • Kunii, M., 2014: Mesoscale data assimilation for a local severe rainfall event with the NHM-LETKF system. Wea. Forecasting, 29, 10931105, doi:10.1175/WAF-D-13-00032.1.

    • Search Google Scholar
    • Export Citation
  • Leutbecher, M., , and T. Palmer, 2008: Ensemble forecasting. J. Comput. Phys., 227, 35153539, doi:10.1016/j.jcp.2007.02.014.

  • Li, H., , E. Kalnay, , and T. Miyoshi, 2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 523533, doi:10.1002/qj.371.

    • Search Google Scholar
    • Export Citation
  • Miyoshi, T., , and M. Kunii, 2012: The local ensemble transform Kalman filter with the Weather Research and Forecasting model: Experiments with real observations. Pure Appl. Geophys., 169, 321333, doi:10.1007/s00024-011-0373-4.

    • Search Google Scholar
    • Export Citation
  • Miyoshi, T., , Y. Sato, , and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var intercomparison with the Japanese Operational Global Analysis and Prediction System. Mon. Wea. Rev., 138, 28462866, doi:10.1175/2010MWR3209.1.

    • Search Google Scholar
    • Export Citation
  • Ott, E., and et al. , 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415428, doi:10.1111/j.1600-0870.2004.00076.x.

    • Search Google Scholar
    • Export Citation
  • Palmer, T., , R. Buizza, , F. Doblas-Reyes, , T. Jung, , M. Leutbecher, , G. Shutts, , M. Steinheimer, , and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. Tech. Rep. ECMWF RD Tech. Memo. 598, 42 pp.

  • Peralta, C., , Z. Ben Bouallègue, , S. Theis, , C. Gebhardt, , and M. Buchhold, 2012: Accounting for initial condition uncertainties in COSMO-DE-EPS. J. Geophys. Res.,117, D07108, doi:10.1029/2011JD016581.

  • Reich, H., , A. Rhodin, , and C. Schraff, 2011: LETKF for the nonhydrostatic regional model COSMO-DE. COSMO Newsletter, Vol. 11, 27–31. [Available online at http://www.cosmo-model.org/content/model/documentation/newsLetters/newsLetter11/1_reich.pdf.]

  • Saito, K., , H. Seko, , M. Kunii, , and T. Miyoshi, 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11594, doi:10.3402/tellusa.v64i0.11594.

    • Search Google Scholar
    • Export Citation
  • Schomburg, A., , C. Schraff, , and R. Potthast, 2015: A concept for the assimilation of satellite cloud information in an Ensemble Kalman Filter: Single-observation experiments. Quart. J. Roy. Meteor. Soc., doi: 10.1002/qj.2407, in press.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., , G. S. Romine, , K. R. Smith, , and M. L. Weisman, 2014: Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter. Wea. Forecasting, 29, 12951318, doi:10.1175/WAF-D-13-00145.1.

    • Search Google Scholar
    • Export Citation
  • Selz, T., , and G. C. Craig, 2015: Upscale error growth in a high-resolution simulation of a summertime weather event over Europe. Mon. Wea. Rev., doi:10.1175/MWR-D-14-00140.1, in press.

    • Search Google Scholar
    • Export Citation
  • Sommer, M., , and M. Weissmann, 2014: Observation impact in a convective-scale localized ensemble transform Kalman filter. Quart. J. Roy. Meteor. Soc., 140, 26722679, doi:10.1002/qj.2343.

    • Search Google Scholar
    • Export Citation
  • Stephan, K., , S. Klink, , and C. Schraff, 2008: Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Quart. J. Roy. Meteor. Soc., 134, 13151326, doi:10.1002/qj.269.

    • Search Google Scholar
    • Export Citation
  • Szunyogh, I., , E. J. Kostelich, , G. Gyarmati, , E. Kalnay, , B. R. Hunt, , E. Ott, , E. Satterfield, , and J. A. Yorke, 2008: A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus, 60A, 113130, doi:10.1111/j.1600-0870.2007.00274.x.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., , and G. J. Hakim, 2008: Performance characteristics of a pseudo-operational ensemble Kalman filter. Mon. Wea. Rev., 136, 39473963, doi:10.1175/2008MWR2443.1.

    • Search Google Scholar
    • Export Citation
  • Vié, B., , O. Nuissier, , and V. Ducrocq, 2011: Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions. Mon. Wea. Rev., 139, 403423, doi:10.1175/2010MWR3487.1.

    • Search Google Scholar
    • Export Citation
  • Weissmann, M., and et al. , 2014: Initial phase of the Hans-Ertel Centre for Weather Research—A virtual centre at the interface of basic and applied weather and climate. Meteor. Z., 23, 193208, doi:10.1127/0941-2948/2014/0558.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., , and T. M. Hamill, 2012: Evaluating methods to account for system errors in ensemble data assimilation. Mon. Wea. Rev., 140, 30783089, doi:10.1175/MWR-D-11-00276.1.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. Academic Press, 676 pp.

  • Zhang, F., , C. Snyder, , and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185, doi:10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , C. Snyder, , and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, doi:10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 128 128 59
PDF Downloads 68 68 40

Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

View More View Less
  • 1 Hans-Ertel Centre for Weather Research, Ludwig-Maximilians-Universität, Munich, Germany
  • | 2 Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany
© Get Permissions
Restricted access

Abstract

A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012.

The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread.

Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.

Corresponding author address: Florian Harnisch, Meteorologisches Institut, Ludwig-Maximilians-Universität, Theresienstrasse 37, 80333 Munich, Germany.E-mail: florian.harnisch@lmu.de

Abstract

A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012.

The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread.

Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.

Corresponding author address: Florian Harnisch, Meteorologisches Institut, Ludwig-Maximilians-Universität, Theresienstrasse 37, 80333 Munich, Germany.E-mail: florian.harnisch@lmu.de
Save