A new Method for the Nowcasting of Precipitation using Radar and NWP Data
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Tanja WinterrathWSN05 - Toulouse 05.09.2005 -1-
A new Method for the Nowcasting of Precipitation using Radar and NWP Data
Tanja Winterrath
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Overview
Overview of Radar Nowcasting at DWD Method Clutter Filter Divergence of the Wind Field Examples Summary and Outlook
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Forecast lead time
Info
rmat
ion
cont
ent
after Golding (1998)
- 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)
Radar Nowcasting at DWD
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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
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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)
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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)
Method
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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
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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.
Clutter Filter
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Clutter Filter -
Results
OriginalRadar Data
Radar Data afterClutter Elimination
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Divergence-free Wind Field Minimisation of the functional
Iterative Solution: - Gauß-Seidel - Successive Over-Relaxation (SOR) - Chebyshev Acceleration
dxdyy
v
x
uvvuuvuE
A
202202),,(
(after Sherman, 1978)
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HorizontalLM wind fieldoriginal data
HorizontalLM wind fielddivergence-free
Difference
Shown are the absolute values of the wind velocities;the mean relative change is approx. 10%.
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Example
18.08.04, 500 hPa Start time = 20:00 Hourly successive...
... radar measurements: ... model results:
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1. Row: Measurements; 2. Row: 500 hPa; 3. Row: 700 hPa+1h +2h +3h +4h
+1h, 700 hPa +4h, 700 hPa+2h, 700 hPa +3h, 700 hPa
1 2 3 4
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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...
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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!