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A new Method for the Nowcasting of Precipitation using Radar and NWP Data

A new Method for the Nowcasting of Precipitation using Radar and NWP Data. Tanja Winterrath Tanja.Winterrath@dwd.de. Overview. Overview of Radar Nowcasting at DWD Method Clutter Filter Divergence of the Wind Field Examples Summary and Outlook. Information content.

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A new Method for the Nowcasting of Precipitation using Radar and NWP Data

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  1. A new Method for the Nowcasting of Precipitation using Radar and NWP Data Tanja Winterrath Tanja.Winterrath@dwd.de

  2. Overview • Overview of Radar Nowcasting at DWD • Method • Clutter Filter • Divergence of the Wind Field • Examples • Summary and Outlook WSN05 - Toulouse

  3. Information content Forecast lead time after Golding (1998) Radar Nowcasting at DWD -Convective: KONRAD = recognition and linear extrapolation of cells (max. +1h)-Stratiform: Rosenow = pattern recognition and linear extrapolation of image structures (max. +2h) -Blending of the results -Latent Heat Nudging in NWP (LMK) WSN05 - Toulouse

  4. The new Approach... • Aim Extending the forecast beyond 2 hours • Basic Approach Displacement of radar precipitation data with horizontal LM wind fields non-linear and temporally variable displacement of precipitation fieldsexternal wind fields in contrast to directly derived displacement vectors WSN05 - Toulouse

  5. Input data • Radar product - Germany composite - Precipitation amount, derived with improved Z-R relationship (so-called RZ product) • NWP product - Horizontal wind components taken from LM(Lokalmodell, 7 km) or LM-K (2.8 km) WSN05 - Toulouse

  6. Method • Horizontal LM wind field - Choice of a suitable pressure level - Transformation and spatial interpolation onto radar-,grid‘ (1 km x 1 km) - Temporal interpolation in each time step of the advection scheme • Displacement of the precipitation field- 2d Eulerian advection scheme (Bott, 1993)- Time step approx. 30-45 seconds (CFL criterion) WSN05 - Toulouse

  7. Problems and Solutions • Numerical diffusion leads to a smoothing of clutter pixels  Clutter filter • Unrealistic stretching and compression of precipitation patterns (i.e., creation of new extrema)Elimination of divergences of the wind field WSN05 - Toulouse

  8. Clutter Filter • Development of a procedure to completely eliminate clutter pixels from input data • Basic approach: - Marking of all pixels, the 31x31- surrounding square of which contains more than 85% of pixels with a value of 1. less than 45%of the value of the centre pixel 2. < 0.01 mm/h. - Replacement of the centre pixel‘s value by 1. the mean of the data pixels within the surrounding square. 2. zero. WSN05 - Toulouse

  9. Clutter Filter - Results Original Radar Data Radar Data afterClutter Elimination WSN05 - Toulouse

  10. Divergence-free Wind Field • Minimisation of the functional • Iterative Solution:- Gauß-Seidel - Successive Over-Relaxation (SOR) - Chebyshev Acceleration (after Sherman, 1978) WSN05 - Toulouse

  11. Horizontal LM wind field original data Horizontal LM wind field divergence-free Difference Shown are the absolute values of the wind velocities; the mean relative change is approx. 10%. WSN05 - Toulouse

  12. Example • 18.08.04, 500 hPa • Start time = 20:00 • Hourly successive... ... radar measurements: ... model results: WSN05 - Toulouse

  13. 1. Row: Measurements; 2. Row: 500 hPa; 3. Row: 700 hPa +1h +2h +3h +4h +1h, 700 hPa +2h, 700 hPa +3h, 700 hPa +4h, 700 hPa 1 3 4 2 WSN05 - Toulouse

  14. Summary • Aim: Extension of the precipitation nowcast beyond 2 hours to close the gap between linear extrapolation and model nowcast • Status: - Effective clutter filter realised - Area-preserving advection due to divergence-free wind field ... Work in progress... WSN05 - Toulouse

  15. Outlook • Validation and Verification • Determination of optimal pressure level(s) for wind field extraction • Determination of cross-over times to linear extrapolation and NWP • Introduction into operational process chain Poster No. 2.39 on display now! WSN05 - Toulouse

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