Meteorology and Atmospheric Physics

, Volume 127, Issue 5, pp 519–535 | Cite as

On the performance of the new NWP nowcasting system at the Danish Meteorological Institute during a heavy rain period

  • Bjarke Tobias Olsen
  • Ulrik Smith Korsholm
  • Claus Petersen
  • Niels Woetmann Nielsen
  • Bent Hansen Sass
  • Henrik Vedel
Original Paper

Abstract

At the Danish Meteorological Institute, the NWP nowcasting system has been enhanced to include assimilation of 2D precipitation rates derived from weather radar observations. The assimilation is performed using a nudging-based technique. Here the rain rates are used to estimate the changes in the vertical profile of horizontal divergence needed to induce the observed rain rate. Verification of precipitation forecasts for a 17-day period in August 2010 based on the NWP nowcasting system is presented and compared to a reference without assimilation of precipitation data. In Denmark, this period was particularly rainy, with several heavy precipitation events. Three of these events are studied in detail. The verification is mainly based on scatter plots and fractions skill scores, which give scale-dependant indicators of the spatial skill of the forecasts. The study shows that the inclusion of precipitation observations has a positive impact on the spatial skill of the forecasts. This positive impact is the largest in the first hour, and then gradually decreases. On the average, the forecasts with assimilation of precipitation are skilful after 4 h on scales down to a few tens of kilometers. For the events studied, the assimilation improves the forecasted frequencies of heavy and light precipitation relative to the control, while there is some tendency to overpredict intermediate precipitation levels.

Keywords

Lead Time Numerical Weather Prediction Rain Rate Precipitation Intensity Radar Observation 

Notes

Acknowledgments

This work was supported by the HydroCast project (hydrocast.dhigroup.com) funded partly by the Danish Council for Strategic Research under the Programme Commission on Sustainable Energy and Environment, and the OMOVAST project, funded partly by the Danish Ministry of Environment, under the Programme for Development and Demonstration Projects. The authors would like to express our gratitude for the constructive reviewer feedback. The first author would like to acknowledge DMI for funding the work which was carried out at the institute.

References

  1. Abel SJ, Boutle IA (2012) An improved representation of the raindrop size distribution for single-moment microphysics schemes. Q J R Meteorol Soc 138:2151–2162CrossRefGoogle Scholar
  2. Battan LJ (1973) Radar observation of the atmosphere, vol 2. The University of Chicago Press, Chicago, pp 297–302Google Scholar
  3. Caumont O, Ducrocq V, Wattrelot E, Jaubert G, Pradier-Vabre S (2010) 1D+3Dvar assimilation of radar reflectivity data: a proof of concept. Tellus Ser A Dyn Meteorol Oceanogr 62:173–187CrossRefGoogle Scholar
  4. Caya A, Sun J, Snyder C (2005) A comparison between the 4dvar and the ensemble kalman filter techniques for radar data assimilation. Mon Weather Rev 133(11):3081–3094CrossRefGoogle Scholar
  5. Courtier P, Andersson E, Heckley W, Vasiljevic D, Hamrud M, Hollingsworth A, Rabier F, Fisher M, Pailleux J (1998) The ecmwf implementation of three-dimensional variational assimilation (3d-var). I: formulation. Q J R Meteorol Soc 124(550):1783–1807Google Scholar
  6. Dixon M, Wiener G (1993) Titan: thunderstorm identification, tracking, analysis, and nowcasting—a radar-based methodology. J Atmos Ocean Technol 10(6):785–797CrossRefGoogle Scholar
  7. Ebert EE, Wilson LJ, Brown BG, Nurmi P, Brooks HE, Bally J, Jaeneke M (2004) Verification of nowcasts from the wwrp sydney 2000 forecast demonstration project. Weather Forecast 19(1):73–96CrossRefGoogle Scholar
  8. Gao J, Xue M, Shapiro A, Droegemeier KK (1999) A variational method for the analysis of three-dimensional wind fields from two doppler radars. Mon Weather Rev 127(9):2128–2142CrossRefGoogle Scholar
  9. Gilleland E, Ahijevych DA, Brown BG, Ebert EE (2010) Verifying forecasts spatially. Bull Am Meteorol Soc 91(10):1365–1373CrossRefGoogle Scholar
  10. Golding BW (1998) NIMROD: a system for generating automated very short range forecasts. Meteorol Appl 5:1–16CrossRefGoogle Scholar
  11. Hong SY, Dudhia J (2012) Next-generation numerical weather prediction: bridging parameterization, explicit clouds, and large eddies. Bull Am Meteorol Soc 93(1):ES6–ES9Google Scholar
  12. Jensen DG, Petersen C, Rasmussen MR (2014) Assimilation of radar-based nowcast into a HIRLAM NWP model. Meteorol ApplGoogle Scholar
  13. Jones C, Macpherson B (1997) A latent heat nudging scheme for the assimilation of precipitation data into an operational mesoscale model. Meteorol Appl 4(3):269–277CrossRefGoogle Scholar
  14. Kalman RE et al (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45CrossRefGoogle Scholar
  15. Korsholm US, Petersen C, Sass BH, Nielsen NW, Jensen D, Olsen B, Gill R, Vedel H (2015) A new approach for assimilation of 2D radar precipitation in a high-resolution NWP model. Meteorol Appl 22(1):48–59CrossRefGoogle Scholar
  16. Lilly DK (1990) Numerical prediction of thunderstorms—has its time come? Q J R Meteorol Soc 116(494):779–798Google Scholar
  17. Marshall JS, Palmer WMK (1948) The distribution of raindrops with size. J Meteorol 5(4):165–166CrossRefGoogle Scholar
  18. Marshall J, Hitschfeld W, Gunn K (1955) Advances in radar weather. Adv Geophys 2:1CrossRefGoogle Scholar
  19. Marshall JS, Ballantvne EH (1975) Weather surveillance radar. J Appl Meteorol 14(7):1317–1338CrossRefGoogle Scholar
  20. Molinari J, Dudek M (1992) Parameterization of convective precipitation in mesoscale numerical models: a critical review. Mon Weather Rev 120(2):326–344CrossRefGoogle Scholar
  21. Ninomiya K, Taira R, Ueno M, Kurihara K, Kudo T (1987) Mesoscale very short-range numerical prediction with dynamical initialization including condensation heating. ESA, Mesoscale Analysis and Forecasting, SP-282, pp 611–616Google Scholar
  22. Parrish DF, Derber JC (1992) The national meteorological center’s spectral statistical-interpolation analysis system. Mon Weather Rev 120(8):1747–1763CrossRefGoogle Scholar
  23. Qiu CJ, Xu Q (1992) A simple adjoint method of wind analysis for single-Doppler data. J Atmos Ocean Technol 9(5):588–598CrossRefGoogle Scholar
  24. Rabier F, Järvinen H, Klinker E, Mahfouf JF, Simmons A (2000) The ecmwf operational implementation of four-dimensional variational assimilation. I: experimental results with simplified physics. Q J R Meteorol Soc 126(564):1143–1170CrossRefGoogle Scholar
  25. Roberts NM, Lean HW (2008) Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon Weather Rev 136(1):78–97CrossRefGoogle Scholar
  26. Roebber PJ, Schultz DM, Colle BA, Stensrud DJ (2004) Toward improved prediction: high-resolution and ensemble modeling systems in operations. Weather Forecast 19(5)Google Scholar
  27. Sass BH, Petersen C (2002) Short range atmospheric forecasts using a nudging procedure to combine analyses of cloud and precipitation with a numerical forecast model. DMIGoogle Scholar
  28. Shapiro A, Ellis S, Shaw J (1995) Single-doppler velocity retrievals with phoenix ii data: clear air and microburst wind retrievals in the planetary boundary layer. J Atmos Sci 52(9):1265–1287CrossRefGoogle Scholar
  29. Skamarock WC (2004) Evaluating mesoscale nwp models using kinetic energy spectra. Mon Weather Rev 132(12)Google Scholar
  30. Sun J, Flicker DW, Lilly DK (1991) Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data. J Atmos Sci 48(6):876–890CrossRefGoogle Scholar
  31. Sun J, Crook NA (1997) Dynamical and microphysical retrieval from doppler radar observations using a cloud model and its adjoint. part i: Model development and simulated data experiments. J Atmos Sci 54(12):1642–1661CrossRefGoogle Scholar
  32. Sun J, Crook NA (1998) Dynamical and microphysical retrieval from doppler radar observations using a cloud model and its adjoint. Part II: retrieval experiments of an observed florida convective storm. J Atmos Sci 55(5):835–852CrossRefGoogle Scholar
  33. Sun J (2005) Convective-scale assimilation of radar data: progress and challenges. Q J R Meteorol Soc 131(613):3439–3463CrossRefGoogle Scholar
  34. Sun J, Xue M, Wilson JW, Zawadzki I, Ballard SP, Onvlee-Hooimeyer J, Joe P, Barker DM, Li PW, Golding B et al (2014) Use of nwp for nowcasting convective precipitation: recent progress and challenges. Bull Am Meteorol Soc 95(3):409–426CrossRefGoogle Scholar
  35. Unden P, Rontu L, Järvinen H, Lynch P, Calvo J, Cats G, Cuxart J, Eerola K, Fortelius C, Garcia-Moya JA et al (2002) Hirlam-5 scientific documentation. http://www.hirlam.org
  36. Wang H, Sun J, Zhang X, Huang X, Auligné T (2013) Radar data assimilation with WRF 4D-Var. Part I: system development and preliminary testing. Mon Weather Rev 141(7):2224–2244CrossRefGoogle Scholar
  37. Wattrelot E, Caumont O, Mahfouf JF (2014) Operational implementation of the 1D+3D-Var assimilation method of radar reflectivity data in the AROME model. Mon Weather Rev 142:1852–1873CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  1. 1.Research and DevelopmentDanish Meteorological InstituteCopenhagenDenmark
  2. 2.Wind Energy, Technical University of Denmark, RisøRoskildeDenmark

Personalised recommendations