a new method for the nowcasting of precipitation using radar and nwp data

Tanja WinterrathWSN05 - Toulouse 05.09.2005 -1-

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

Tanja Winterrath

[email protected]

05.09.2005WSN05 - Toulouse -2- Tanja Winterrath

Overview

Overview of Radar Nowcasting at DWD Method Clutter Filter Divergence of the Wind Field Examples Summary and Outlook


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


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


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)


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


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


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


Clutter Filter -

Results

OriginalRadar Data

Radar Data afterClutter Elimination


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)


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%.


Example

18.08.04, 500 hPa Start time = 20:00 Hourly successive...

... radar measurements: ... model results:


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


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...


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!

a new method for the nowcasting of precipitation using radar and nwp data

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