A physical initialization algorithm for non-hydrostatic NWP models

using radar derived rain rates

Günther Haase

Meteorological Institute, University of BonnGerman Weather Service

Günther Haase Helsinki, 3 October 2002 2

area:

Northern Germany ~ 400x400 km2

x = 7 km

time period:

May, 2000 (t = 1 h)

radar LM

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PIB Algorithmpreprocessing of reflectivity measurements

determine pre-forecast period, conversion efficiency and mean cloud top height

compute present LM-CCL (cloud base)

precipitation analysis saturation adjustment

modify LM variablesw, qv, and qc

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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

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Cloud top height

Derivation of cloud top heights from averaged LMcloud water profiles

Alternative: application of a cloud initialization method using Meteosat measurements (F. Ament)

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Cloud base height

CCL

LCL

• open symbols: LM without convection parameterization• closed symbols: LM with convection parameterization

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Modifications

RRRAD > 0.1 mm/h RRRAD 0.1 mm/h

0qc85%) (rh, MINrh

0w

cbz z

cbct zz z

ctz z

0qc)(zqvqv

zzzz )(z ww

cb

srfcb

srfcbPI

changes no :qcqvqv

)(ww*

PI

z

0qc85%) (rh, MINrh

changes no :w

0qc85%) (rh, MINrh

changes no :w

0qc85%) (rh, MINrh

changes no :w

Günther Haase Helsinki, 3 October 2002 9

Vertical wind (PIB)

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

Computation of vertical wind profiles using a 1-dim cloud model

0,1 wρ

RRc cbzzPI

*v

C = 0.1

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Case study: 13 July 1999

Exp. pre-forecast period forecastCTL – 12 hPIB 12 – 13 UTC 12 hLHN 12 – 13 UTC 12 h

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PIB sensitivity study

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CTL LHNPIB radar

13 UTC

14 UTC

15 UTC

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CTL with convection parameterization

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Hydrology

a) Area averaged hourly accumulated precipita-tion

b) LWP and IWV: PIB generates more clouds than the control run (CTL)

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a) Hit Rate

b) False Alarm Rate

c) Kuipers Score (measures the skill of a forecast relative to a random forecast)

Objective skill scores

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Scale-dependency of the RMSE

• PIB provides better forecasts than the control run (CTL) on all scales• LHN is better on large scales

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a) vertical wind (500 hPa)

b) surface pressure

c) absolute surface pres-sure tendency

Noise

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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

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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!)

Summary (2)

Günther Haase Helsinki, 3 October 2002 21

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

Future research