radar in almo assimilation of radar information in the alpine model of meteoswiss daniel leuenberger...

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Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

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Page 1: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Radar in aLMo

Assimilation of Radar Information in the Alpine Model of MeteoSwiss

Daniel Leuenberger and Andrea Rossa

MeteoSwiss

Page 2: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

uenberg

er@

Mete

oSw

iss.

chSR

NW

P –

CO

ST-7

17

Lis

bon,

8.O

ctober

20

032

Radar in aLMoIntroduction

Radar information is gaining importance in mesoscale data assimilation

Latent Heat Nudging (LHN): Assimilation method for precipitation information

Trigger model precipitation where radar detects precipitation (heating), supress it elsewhere (cooling)

4DDA, yet computationally very efficient

Conceptionally simple

Assimilation of non-prognostic variables not straight forward

Heuristic approach of weighting observations and model

Page 3: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

uenberg

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Mete

oSw

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chSR

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P –

CO

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17

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bon,

8.O

ctober

20

033

Radar in aLMoRadar Observations

Swiss radar network: 3 C-Band Doppler Radars

Best estimate of surface rain: preprocessed (e.g. clutter reduction, vertical profile corrections)

Resolution: 2 x 2 km2, 5 min

Radar Quality Map

Page 4: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

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chSR

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P –

CO

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Radar in aLMoReal Case Study

CaseSystem of severe convection over SwitzerlandTriggered around 22h30 UTC over the Massif CentralDevelopment ahead of weak cold frontModerately unstable environment as observed by the Swiss radiosonde Payerne at 00UTC (CAPE ~250 J/kg)Strong wind shear (~ 30 m/s at 6000m)

SimulationsOperational Alpine Model (aLMo) of MeteoSwiss (x=7km)Convection ParametrizationStarted 21.8.00 00 UTC from GME of DWDCTRL (no forcing)LHN during 6hLHN+ during 3h, free forecast afterwards

Page 5: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

uenberg

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Mete

oSw

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chSR

NW

P –

CO

ST-7

17

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035

Radar in aLMo

010203040506

CTRL LHN Radar

Case Study of the 21.8.2000 Storm

Hourly Sums of Precipitation: Forcing during 6h

Page 6: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

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chSR

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CO

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ctober

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036

Radar in aLMo

01020304

1h Free Forecast

05

2h Free Forecast

06

3h Free Forecast

Case Study of the 21.8.2000 Storm

Hourly Sums of Precipitation: Forcing during 3h

CTRL LHN+ Radar

Page 7: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

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037

Radar in aLMoFindings I

Model is able to assimilate radar observations

Good impact in analysis, sfc winds in line with observations

Some impact in free forecast up to 03h

Model loses information quickly, i.e. storm dies too early

Why is the model not able to maintain storm?

Environment not representative?

Model resolution ?

Try to find reasons by means of idealized simulations

Page 8: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

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chSR

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20

038

Radar in aLMoIdealized Simulations

Setup

Environment from Payerne sounding of 21.8.00 00UTC

Fine mesh (x = 1km), no CPS, no soil model, no radiation

Trigger convection with warm bubble: No storm development

wind shear

Payerne profile

KW profile

Payerne sounding

KW sounding

Klemp Wilhelmson Environment

Large amount of CAPE (~ 1200 J/kg)

Moderate wind shear

Favorable for splitting supercell storms

Page 9: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

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039

Radar in aLMoOSSE Setup

Reference run

Convection initiated with warm bubble

Model sfc rain serves as „artificial radar observations“

LHN Analysis

Same environment as reference run

No warm bubble initiation

LHN during 3h (artificial rain rates from reference run)

LHN Forecast

LHN during first 30, 60 min

Free run afterwards

Page 10: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

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Mete

oSw

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chSR

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03 10

Radar in aLMoInsertion Frequency of Precipitation Input

LHN linearly interpolates between subsequent observations

Examine relevance of insertion frequency t to LHN Analysis

t = 10minlinear interpolation

t = 1min

Page 11: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

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Mete

oSw

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chSR

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17

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8.O

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20

03 11

Radar in aLMoLHN Analysis (LHN during 3h)

“OBS“ t = 10min

t = 4mint = 1min

Page 12: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

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03 12

Radar in aLMoDomain Sum of LH Nudging Increment

Page 13: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

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03 13

Radar in aLMoLHN Forecast (t = 1min)

“OBS“

Free forecast after 30 min

Analysis (LHN during 3h)

Free forecast after 1h

Page 14: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

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03 14

Radar in aLMoSensitivity to Horizontal Grid Spacing

Analysis (LHN during 3h)Free forecast after 1h

x = 2km

x = 5km

Page 15: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

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Mete

oSw

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chSR

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P –

CO

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Lis

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8.O

ctober

20

03 15

Radar in aLMoFindings II

LHN capable of analysing and initiating supercell storm

Good temporal sampling of the observed phenomena is important

Representative large-/mesoscale environment important

Even a poorly resolved forcing is able to initiate and maintain storm evolution in appropriate environment

Supercell storm very stable dynamics: are findings ‚portable‘ to other situations?

Page 16: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

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er@

Mete

oSw

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chSR

NW

P –

CO

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Lis

bon,

8.O

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20

03 16

Radar in aLMoOutlook

Real-case study

Reduction of grid-size to 2km

Study impact of errors in radar data

More cases

Idealized OSSE

Sensitivity of vertical forcing distribution

Assimilation of model 3D latent heating fields

Assimilation of horizontal winds

Consider case which is less driven by dynamics

Page 17: Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

Danie

l.Le

uenberg

er@

Mete

oSw

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chSR

NW

P –

CO

ST-7

17

Lis

bon,

8.O

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20

03 17

Radar in aLMo

Thank you for your attention !