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@ARTICLE{Bick:820929,
      author       = {Bick, T. and Simmer, C. and Trömel, S. and Wapler, K. and
                      Hendricks-Franssen, Harrie-Jan and Stephan, K. and Blahak,
                      U. and Schraff, C. and Reich, H. and Zeng, Y. and Potthast,
                      R.},
      title        = {{A}ssimilation of 3{D} radar reflectivities with an
                      ensemble {K}alman filter on the convective scale},
      journal      = {Quarterly journal of the Royal Meteorological Society},
      volume       = {142},
      number       = {696},
      issn         = {0035-9009},
      address      = {Weinheim [u.a.]},
      publisher    = {Wiley},
      reportid     = {FZJ-2016-06193},
      pages        = {1490 - 1504},
      year         = {2016},
      abstract     = {An ensemble data assimilation system for 3D radar
                      reflectivity data is introduced for the
                      convection-permitting numerical weather prediction model of
                      the COnsortium for Small-scale MOdelling (COSMO) based on
                      the Kilometre-scale ENsemble Data Assimilation system
                      (KENDA), developed by Deutscher Wetterdienst and its
                      partners. KENDA provides a state-of-the-art ensemble data
                      assimilation system on the convective scale for operational
                      data assimilation and forecasting based on the Local
                      Ensemble Transform Kalman Filter (LETKF). In this study, the
                      Efficient Modular VOlume RADar Operator is applied for the
                      assimilation of radar reflectivity data to improve
                      short-term predictions of precipitation. Both deterministic
                      and ensemble forecasts have been carried out. A case-study
                      shows that the assimilation of 3D radar reflectivity data
                      clearly improves precipitation location in the analysis and
                      significantly improves forecasts for lead times up to 4 h,
                      as quantified by the Brier Score and the Continuous Ranked
                      Probability Score. The influence of different update rates
                      on the noise in terms of surface pressure tendencies and on
                      the forecast quality in general is investigated. The results
                      suggest that, while high update rates produce better
                      analyses, forecasts with lead times of above 1 h benefit
                      from less frequent updates. For a period of seven
                      consecutive days, assimilation of radar reflectivity based
                      on the LETKF is compared to that of DWD's current
                      operational radar assimilation scheme based on latent heat
                      nudging (LHN). It is found that the LETKF competes with LHN,
                      although it is still in an experimental phase.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000375935600024},
      doi          = {10.1002/qj.2751},
      url          = {https://juser.fz-juelich.de/record/820929},
}