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A physical initialization algorithm for non-hydrostatic NWP models using radar derived rain rates

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A physical initialization algorithm for non-hydrostatic NWP models using radar derived rain rates

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  1. A physical initialization algorithm for non-hydrostatic NWP models using radar derived rain rates Günther Haase Meteorological Institute, University of Bonn German Weather Service

  2. radar  LM area: Northern Germany ~ 400x400 km2x = 7 km time period: May, 2000 (t = 1 h) Helsinki, 3 October 2002

  3. PIB Algorithm preprocessing of reflectivity measurements determine pre-forecast period, conversion efficiency and mean cloud top height precipitation analysis saturation adjustment compute present LM-CCL (cloud base) modify LM variables w, qv, and qc Helsinki, 3 October 2002

  4. DWD radar network • 16 C-band radars:  = 5.3 cm • cartesian grid: x = 4 km • temporal resolution: t = 15 minutes • 6 reflectivity classes • fixed Z/R relation Helsinki, 3 October 2002

  5. Cloud top height Derivation of cloud top heights from averaged LM cloud water profiles Alternative: application of a cloud initialization method using Meteosat measurements (F. Ament) Helsinki, 3 October 2002

  6. Cloud base height CCL LCL • open symbols: LM without convection parameterization • closed symbols: LM with convection parameterization Helsinki, 3 October 2002

  7. Modifications Helsinki, 3 October 2002

  8. Vertical wind (PIB) Computation of vertical wind profiles using a 1-dim cloud model assumptions: • only two hyd. components: water vapor and rain • only two cloud processes: condensation and evapo-ration • closure: conversion efficiency of saturated air into rain C = 0.1 Helsinki, 3 October 2002

  9. Case study: 13 July 1999 Helsinki, 3 October 2002

  10. PIB sensitivity study Helsinki, 3 October 2002

  11. CTL PIB LHN radar 13 UTC 14 UTC 15 UTC Helsinki, 3 October 2002

  12. CTL with convection parameterization Helsinki, 3 October 2002

  13. Hydrology • Area averaged hourly accumulated precipita-tion • LWP and IWV: PIB generates more clouds than the control run (CTL) Helsinki, 3 October 2002

  14. Objective skill scores • Hit Rate • False Alarm Rate • Kuipers Score (measures the skill of a forecast relative to a random forecast) Helsinki, 3 October 2002

  15. Scale-dependency of the RMSE • PIB provides better forecasts than the control run (CTL) on all scales • LHN is better on large scales Helsinki, 3 October 2002

  16. Noise • vertical wind (500 hPa) • surface pressure • absolute surface pres-sure tendency Helsinki, 3 October 2002

  17. Summary (1) • PIB is suitable for nowcasting of convective precipitation events on the meso--scale • reduction of spinup and position errors in the precipitation forecast over a couple of hours • closure of the information gap between LM forecasts and nowcasting based on observations • PIB uses only operational radar products as input Helsinki, 3 October 2002

  18. Summary (2) • low computational costs • compatible with future model developments • column approach prevents the method from initializing large-scale precipitation events • PIB reacts very sensitive on variations of the input data (quality control!) Helsinki, 3 October 2002

  19. Future research • combination with a cloud initialization method using Meteosat measurements • using 3-dim reflectivity fields (on model levels) • forcing a dynamic balance between mass and wind fields • application of a modified LM precipitation scheme Helsinki, 3 October 2002